Utilization of a tailormade condition monitoring device for third party motors

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IN THE FIELD OF TECHNOLOGY DEGREE PROJECT

ENERGY AND ENVIRONMENT AND THE MAIN FIELD OF STUDY ELECTRICAL ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM SWEDEN 2018,

Utilization of a tailormade condition monitoring device for third party motors

PONTUS GRAHN

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TRITA EECS-EX-2018:482

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Utilization of a tailormade condition monitoring device for third party motors

PONTUS GRAHN

Master of Science Thesis in Electrical Energy Conversion at the School of Electrical Engineering and Computer Science

KTH Royal Institute of Technology Stockholm, Sweden, August 2018.

Supervisor: Thorsten Hinrichs Examiner: Oskar Wallmark

TRITA-EECS-EX-2018:482

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Utilization of a tailormade condition monitoring device for third party motors PONTUS GRAHN

PONTUS GRAHN, 2018.c

School of Electrical Engineering and Computer Science Department of Electric Power and Energy Systems Kungliga Tekniska högskolan

SE–100 44 Stockholm Sweden

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Abstract

Our society moves towards digitalization and the industry is not an exception. Siemens has developed a wireless condition monitoring device called Simotics Connect in order to help them to move forward in the world of digitalization. The Simotics Connect has three inbuilt sensors. One for temperature, one for vibrations and one for magnetic flux density, a product that is new in the market. This master thesis has investigated its usability for third party motors, which has not been done.

Four areas were investigated, the status in the current market, creating a motor geometry estimation based on nameplate data, presenting a temperature model to calculate a motor’s cross section temperature and, finally, proposed a stator current model using the magnetic field measurement.

Market research has shown that a space for the Simotics Connect to thrive in most definitely exists.

The motor geometry estimation, that is based on preliminary electromagnetic sizing, creates a digital twin for the motor that has sufficient accuracy as a tool when calculating e.g. temperature calculations but lacks accuracy for more advanced and sensitive calculations e.g for magnetic flux density measurement usability.

The temperature model that is presented shows great accuracy when calculating the cross section temperature in the stator but the accuracy decreases for the cross section temperature in the rotor.

A stator current model is proposed using a proportional relationship between the magnetic flux density and stator current. The results indicates a linear relationship, though using the digital twin to calculate the proportional constant were concluded to not be accurate enough.

Keywords: Condition monitoring, cross section temperature, digital twin, geometry estimation, induction motors, lumped thermal modeling, preliminary electromagnetic sizing.

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Sammanfattning

Sammhället rör sig idag mot digitalisering och industrin är ej ett undantag. Siemens har utvecklat en trådlös underhållsmätare kallad Simotics Connect för att hjälpa dem sträva mot en värld inom digitalisering. Simotics Connect hat tre inbyggda sensorer. En för tem- peratur, en för vibrationer och en för magnetisk flödestäthet, vilket är nytt på marknaden.

Detta masterprojekt har undersökt användningen av Simotics Connect för tredjepartsmo- torer, vilket ej har gjorts tidigare.

Fyra områden undersöktes, statusen på den nuvarande marknaden, en motorge- ometriuppskattningmodell baserad på namnskylsdata, en temperaturmodell för att beräkna motorns tvärsnittstemperatur och, slutligen, en statorströmmodell som använder sig av magnetiska flödestäthetsmätningen.

Marknadsundersökningen har visat att det finns ett utrymme för Simotics Connect att blomstra inom på den nuvarande marknaden.

Motorns geometriska uppskattning, som är baserad i preliminär elektromagnetisk geometribestämning, skapar en digital tvilling av motorn som är tillräckligt noggrann för att aggera som ett verktyg vid t.ex. temperatursberäkningar men saknar noggrannhet för mer avancerade och känsliga beräkningar, t ex för användbarhet inom magnetisk flödestä- thetsberäkningar.

Temperaturmodellen som presenteras visar stor noggrannhet vid beräkning av sta- torns tvärsnittstemperatur, men noggrannheten minskar för rotorns tvärsnittstemperatur.

En statorströmmodell föreslås med ett proportionellt förhållande mellan magnet- flödesdensiteten och statorströmmen. Resultaten indikerar ett linjärt förhållande, men an- vändandet av den digitala tvillingen för att beräkna proportionell konstant konstateras att inte vara tillräckligt noggrann metod.

Nyckelord: Buntad termisk modelering, digital tvilling, geometri estimering, induktionsmotorer, preliminär elektromagnetisk geometribestämning,

tvärsnittstemperatur, underhållsmätning.

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Acknowledgements

This master thesis has been in collaboration with the Process Industries and Drives - Large Drives (PD LD) at Siemens, Solna, Sweden.

I would like to give thanks to Thorsten Hinrichs that has been acted as my supervisor at Siemens during my thesis. He gave me this opportunity to write my master thesis at Siemens and has always given me support and shown interest in my work. I would also say thanks to Siemens and all the people who works at PD LD in Solna. I was greeted well and was able to work in a nice environment during this period.

I would also give special thanks to Associate Professor Oskar Wallmark and PhD student Konstantina Bitsi. Oskar was not only my examiner during my thesis but also given me advise and counseling during it. He was also one of the people who inspired me to study further in the field of electrical machines and drives. Konstantina helped me a lot regarding the simulation program Comsol that was used during my work. Our short meetings during my master thesis was always helpful.

Finally I would like to thank my family and friends. My parents and sister have always supported me during my five years at KTH, even from far away. Though they have no knowledge in the subject i have been studying in they have shown interest and always been there for me. Moving from Skelleteå to Stockholm could have been difficult if it was not for all my friends I now have here. Spending these five years together have been wonderful and the fact that people are spreading out over the country does not concern me at all, because I know we will keep contact with each other.

Pontus Grahn Stockholm, Sweden August 2018

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Acronyms

Notation Description Page

List

DAU Data Analysis Unit 11, 12

FEM Finite Element Method 7, 61,

63, 64, 67, 69, 70, 83, 95

FFT Fast Fourier Transform 70

HV High Voltage 11

IM Induction Machine 11, 23,

24, 27

KPI Key Points of Interest 6

LV Low Voltage 23, 24

ODR Online Dispute Resolution 15

RMS Root Mean Square 37

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Nomenclature

List A(r) Cross section area in radial direction dependent

on the radius

42

A_r,slot Rotor slot area 31

A⁰_r,slot Active rotor slot area 31

A_s,active Active stator slot area 52

A_seg Cross section area of end ring segment between two rotor bars

62

A_s,slot Stator slot area 32

Bˆδ Peak fundamental flux density in the air gap 28 B_m Norm of measured magnetic flux density 61 B_m,@ism Norm of magnetic flux density, measured at Is,m 61

B_r,tooth Flux density in the rotor tooth 28

B_r,yoke Flux density in the rotor yoke 28

B_s,tooth Flux density in the stator tooth 28

Bs,yoke Flux density in the stator yoke 28

C_r,lam Lamination factor for rotor 29

c_s Stator winding connection factor 27

C_{s,f ill} Stator winding fill factor 28

C_s,lam Lamination factor for stator 29

C_{T e} Torque reduction constant 28

fs Fundamental stator frequency 27

f_slip,rated Rated slip frequency 69

h_{f in} Fin height 65

h_r,slot Distance from widest part of the rotor slot to most inner part

30 h_r,slot,enc Distance from air gap to widest part of the rotor

slot

27

h_r,yoke Distance from shaft to rotor slot 30

h_s,enc Distance from air gap to wedge 27

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Nomenclature

List

h_s,slot Height of stator slot, straight part only 32

hs,slot,enc Distance from air gap to stator slot 27

h_s,yoke Distance from stator slot to outer stator radius 33

h_th,gap Thermal convection in the air gap 50

Iˆ_r Peak fundamental rotor bar current 31 Iˆ_r⁰ Peak fundamental rotor bar current seen from sta-

tor

32

I_s,m Measured stator current 61

Iˆ_s Peak fundamental stator current 32

Jˆ_r Peak fundamental current density in the rotor bars

28 Jˆ_s Peak fundamental current density in the stator

winding

28

k₁ Winding factor 29

k_BI Magnetic flux density - stator current proportional constant

61

kd,1 Distribution factor 29

k_skew,1 Skew factor 29

k_weight Weight compensation constant 37

l Active length of the induction machine 27 l_seg Length of end ring segment between two rotor

bars

62

nf in Number of fins 66

n_rated Rated rotor speed 27

n_s Number of turns in a stator slot 31

n_strand Number of strands making one conductor 27

p Number of poles 27

P_loss Total power losses 53

P_rated Rated output power 27

Q_r Number of rotor slots 28

q_r Radial power flow 42

q_r(r)⁰ 56

Q_s Number of stator slots 28

q_s Stator windings per pole per phase 29

q_s,skew Slot-skew factor 27

Rend,seg Resistance of end ring segment between two rotor bars

62

r_house Housing radius 38

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Nomenclature

List r_i Inner radius of cylindrical material 43 ro Outer radius of cylindrical material 43

r_r Rotor radius 30

R_r Rotor slot resistance 52

r_r,yoke,out Radius until rotor slots 30

R_s Stator slot resistance 52

r_{shaf t} Shaft radius 29

rs,out Stator radius 33

r_strand Radius of single stator winding strand 62

R_th Thermal resistance 43

R_th(r) Thermal resistance dependent on the radius 45

T_e Electrical torque 31

T_i Temperature at r_i 43

Tm Measured temperature at the housing surface 60

T_o Temperature at ro 43

T_rated Rated torque 29

T_r(r) Temperature in radial direction dependent on the radius

45

v_air Kinematic viscosity of air 50

Vs,rated Rated fundamental phase voltage 27

Vˆ_s,rated Rated peak fundamental phase voltage 29

wf in Width of fin 66

w_house Weight of the housing 38

w_o Outer arc length of a material 43

w_r,s,est Estimated weight of the rotor and stator 37

w_r,slot,in Width of the most inner part of the rotor slot 30

wr,slot,open Opening width of rotor slot 27

wr,slot,out Width of the widest part of the rotor slot 31

w_r,s,real Real weight of the rotor and stator 37

w_r,tooth Distance between rotor slots, straight part 30 w_s,slot,in Width of the inner part of the stator slot 32

ws,slot,open Opening width of stator slot 27

w_s,slot,out Width of the outer part of the stator slot 32 ws,tooth Distance between stator slots, straight part 32

w_tot,est Estimated total weight of the motor 37

w_tot,real Real weight of the motor 37

δ Air gap height 28

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Nomenclature

List

δ_lam Lamination thickness 28

∆r Step size in radial direction 55

∆ ˆV_s,rated Peak fundamental voltage drop at rated load 28

η Efficiency of a motor 53

κ_th Specific heat constant 42

κ_th,gap Thermal conductivity in the air gap 50

κ⁰_th,house Thermal conductivity in the stator slot 51

κ_th,s,slot Thermal conductivity in the stator slot 50

φ Angular phase shift between voltage and current 27

ρhouse Density of housing material 38

σ Electrical conductivity 52

ω_m Angular rotor frequency 50

γ_r Rotor slot pitch angle 29

γ_s Stator slot pitch angle 29

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Chapter 1 Introduction

1.1 Background and objectives

Computers, smart phones, tablets, today’s society is becoming more digital by the second.

This digital revolution is happening everywhere, where the term digitalization has become popular. Digitalization is a word that is used everywhere, in many different contexts, but the definition of the word is the process of making something digital that previously was analog [1]. Digitalization in a more common use is not only to make something digital but also to utilize and combine digitized tools with each other, in an overall solution. A simple example of this phenomenon is how email, the digitized version of analog mail, and the digital calendar on your computer, the digitized version of the analog calendar, can work together. These two evolutions are child’s of the digitalization process but in this process they have also been combined in an overall solution, where you can send an invitation for a meeting by email that will directly show up in your digital calendar. Digitalization is making its way into every corner of society, everything from social interactions, such as social web pages, to visualizations tools, in order to help building processes. One area where digitalization is in progress is in industries and factories. The next step for many industries is to make everything digitized for the purpose of acquiring a better understand- ing and be able to optimize the processes. An important part of digitalization in industries is to digitize condition monitoring for electrical motors, in order to optimize maintenance.

Different kinds of condition monitoring tools exists today. The most conventional one is to have a technician on site measure different Key Points of Interests (KPIs) and then let the technician write a report regarding the condition of the motor. Another technique is to have permanent mounted sensors measuring the KPIs which could either be hardwired to a computer or hardwired to a gateway on or close to the motor which then sends the measurement to a computer from the gateway. Both of these techniques are very accurate, because the KPIs are directly measured, but measuring on site takes a lot of time and mounting hardwired sensors is very expensive. A third option that has been appear- ing resent years is wireless condition meters with one or several sensors only mounted

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1.2. Thesis outline on the housing of the motor. The data from these meters can then be sent to a gateway which sends it to a computer, or directly to a computer depending on the transmission method. Analytical tools on different levels of complexity can then be used to analyze the measured data.

The most common sensors these wireless condition meters have are either temperature or vibration sensors and some of them include both. Siemens have during 2017 and 2018 developed a wireless condition meter that is mounted on the housings cooling fins and measures temperature, vibrations and also magnetic flux density, which has not been done before. This utilize the opportunity to determine more information about the condition of the motor with analytical tools. The condition meter developed by Siemens is called Simotics Connect and will be included when buying the new industry motor Simotics SD Next Generation 1LE5. The Simotics Connect will be using wifi, sending the measured data to a cloud called MindSphere. In MindSphere a web application is developed to analyze the data and give conclusions about the motors condition. [2, 3]

The application that is developed in MindSphere is tailor made for Simotics SD Next Generation 1LE5, due to the fact that the Simotics Connect is only included when buying this motor and can not, yet, be bought separately. For Siemens to expand its market they have decided to utilize the Simotics Connect for third party motors as well in the future. This master’s thesis is an initial project in that direction. The task of this project is to determine how useful the measured data is when only knowing the nameplate information of the motor.

The thesis is divided into several sections in order to get an overall conclusion about how to utilize the Simotics Connect for third party motors. First a market research will be held to gain insight about similar products that exists today in the market and the rele- vance of the Simotics Connect. To be able to use more sophisticated calculation models a estimated geometry is needed. One chapter is presenting digital twin creation loop in order to estimate the motors geometry from the nameplate data. Then a temperature model is presented based on lumped thermal theory. Several papers have used lumped thermal theory in a similar fashion, e.g in [4] and [5]. The difference with the method presented in this paper is the simplicity and taking advantage of knowing the housing temperature.

Next a model to determine the stator current with help of the magnetic flux density measurement will be proposed. The models will be verified through both simulations using the Finite Element Method (FEM) simulation tool Comsol and through an experiment in collaboration with Siemens maintenance partner Mekano.

1.2 Thesis outline

The thesis consist of 7 chapters and will hold the following structure

• Chapter 1: Introduction and background of the master thesis

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1.2. Thesis outline

• Chapter 2: A marker research is conducted to understand the Simotics Connects importance

• Chapter 3: A method to create a digital twin is presented

• Chapter 4: A method of how to calculate the cross section temperature of a motor and a method of calculating the stator current based on magnetic flux density measurement are presented

• Chapter 5: Verification methods for the condition monitoring methods are presented

• Chapter 6: The results from the previous chapters are summarized and discussed

• Chapter 7: Final summary of the conclusion of the projects and suggestions for future work

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Chapter 2 Market research

There is of course different sort of solutions regarding condition monitoring, as mentioned in the introduction. In the simplest sense a service team can be hired in order to determine the health and possible lifetime of the machine. ABB has for example a service called ABB MACHsence-P, where their engineers visit the site and take different readings from the machines, analyses the data and summarize everything in a report [6]. This report would include recommendations of corrective actions and preventive maintenance [6].

This solution would not give you real time condition monitoring, only when the service is applied the condition of the machine will be available. There are several solutions to get real time condition monitoring. Another option would be to have sensors connected to different measuring points on the machine which in turn is connected to a relay, mounted on or close to the motor. The relay sends the data via hardwire or wireless connections to a server or display unit. If a site has many machines there would be many hardware connections with this type of solution. The third option is to use a complete wireless method to decrease the hardware connections. The complete wireless solution would be to only have one smart meter device connected to a single point on a machine that can collect several measurements through its inbuilt sensors. This solution would minimize the hardware connections, but the accuracy for the results regarding the condition o a machine could decrease due to less specific measuring points.

In the upcoming sections different products on the market will be described. Their current and upcoming technology will be discussed, but also their service and analytical tools will be investigated. Siemens Simotics Connect will be presented in the last section.

2.1 Other Companies and Products

There are several products on the market that tries to find solution for condition monitoring. Several questions will be answered, such as: Is the product the producer’s main product or a tool that they offer for other products that they focus on? What kind of technology are they using? Which measurements are available? What analytical tools are

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2.1. Other Companies and Products they offering to compliment the product? What are the producers current view on service regarding their product? The products below are chosen from a consumer perspective, where their availability is accessed by a customer with no current contacts in the area.

2.1.1 ABB

ABB is not a company that only focus on products regarding condition monitoring for machines. Condition monitoring is instead products that they offer as a complimentary service. The four products that they have available are the ABB MACHsense-P, the ABB LEAP, the ABB MACHsense-R and the WiMon 100 [6, 7]. The first four subsections below will describe the four products in detail. Then a section is dedicated to the analytical tools that ABB can provide for their products and, lastly, ABBs services and support for the products will be investigated.

ABB MACHsense-P

As mentioned before, the ABB MACHsense-P is a service that evaluate the condition of the machine. ABBs engineers must visit the site to perform the service, which exclude this as a real time condition monitoring product. The method uses vibration and electrical measurement to detect possible problems with the machine. ABB MACHsense-P deals with problems concerning four different areas, the rotor winding, bearings, overall mechanical condition and power supply quality. The data is analysed with a single software platform to avoid problems regarding accuracy and other drawbacks. The service also includes the knowledge about the machine parameters, so that the method becomes more reliable. ABB recommend this service on a six months interval. Some of the key bene- fits that ABB mentions are that the actual slip is calculated from the measurements, that the load is normalized and used in the analysis, that a wide range of vibration frequencies are used to cover more potential problems, that the service is done during operation condition. [6]

ABB LEAP

Just as the ABB MACHsense-P, the ABB LEAP is a service where ABBs engineers must visit the site, which exclude this as a real time condition monitoring product. The ABB LEAP goal is to estimate the lifetime of a machines stator winding. For a greater under- standing of the windings condition the measured data is converted into quantities such as stator winding contamination and aging of the insulation system. The machine has to be offline when the service is performed. When the test is done the customer will get a report containing the estimated lifetime up to a 90 % accuracy and recommendations for maintenance. [6]

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2.1. Other Companies and Products ABB MACHsense-R

The ABB MACHsense-R is a real time wireless condition meter [6] for High Voltage (HV) Induction Machine (IM) [8]. For this device to work an ABB engineer must install the sensors at the measuring points on the machine [6]. The sensors are measuring vibration (via four channels) and temperature (via five channels) and the data is sent to a Data Analysis Unit (DAU) which is mounted on the machine or close to the machine [6,9]. The DAU converts the measurements into usable data regarding the cage rotor, bearings and overall mechanical condition [6]. This is done to avoid false alarms that can occur when only analyses the measurements [6]. The DAU can use GPRS, 3G, which requires a SIM card which will affect costs, to send the data [9]. WiFi or ethernet can also be used if the customer wants to avoid the additional cost of a SIM card [9]. Fig. 2.1 shows the ABB MACHsense-R mounted on a machine.

Fig. 2.1: The MACHsense-R mounted on a machine. [8]

WiMon 100

The WiMon 100 is a product that has vibration sensor, temperature sensor and radio all in one. No external sensors must be connected to specific points, which tells us it is a complete wireless condition monitoring device. It uses WirelessHART as communication protocol which sends the data to a gateway. The gateway can handle up to 100 WiMon 100 connected to it. The WiMon is dust and watter jetting resistant and has a battery life up to five years. [7]

Tools for analyzing

The analytical service that ABB offers for the ABB MACHsense-R is that the DAU send the data to a secure ABB server, where it is monitorized [6]. The service uses data from

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2.1. Other Companies and Products SKF and FAG to detect problems with the bearing [9]. If a fault occurs in the regarded areas an alarm message will be sent to the customer with information about the problem and recommendations about preventive maintenance [6]. The ABB MACHsense-R can be applied to any kind of electrical machine [9].

ABB is working on integrate the ABB MACHsense-R into the ABB Ability platform, a unified web portal. Customers will have easier access and to the real time data, where the end goal is to get access via their computers, tablets and phones. The DAU will be upgraded with more channels to measure e.g. current and voltage. Through the portal the customer will have access to overall vibrations, vibration trend, temperature trend, spectrum gaps, time waveform, speed, load trends and number of start and stops. This service is projected to be available late 2018. [8]

The WiMon 100 uses a tool called WiMon Data Manager. This is a system browser that has automated data acquisition from the sensors. It can store waveforms and dynamic data. It has a interface that can show measurement waveform and trends. The WiMon Data Manager also support a package called ABB Analyst. The ABB Analyst is a program containing an interface for condition monitoring analysis. It is designed for ABB electronic vibration monitoring modules and can display historical vibration data in a more detailed way. It can also help with recognize patterns and trends. [7]

Services and support

ABB offers a lot of services and support to ensure no problems during operation and long lifetimes for the product. Everything from installation and commission, maintenance and field service, spare parts, repair to consulting, training and special support. [6]

2.1.2 SKF

Unlike ABB, SKF focus more on products that complements and improves already ex- isting systems, not focusing on making the systems them self. SKF has many products when it comes to condition monitoring. They have four hardware wired products that can monitor an electrical machine but also a product that includes the wireless method. First, these four solutions will be described below. Then we have investigated how SKF analyze the data that is collected and how SKF provide service and support.

Hardwired monitoring

The hardwire monitoring systems that SKF provides can collect data at real time. They offer several products in this area. A key component that they offer is the SKF Multilog On-line System IMx, which works as a relay. There are several versions of this sort of device, but its main function is to collect data from several measuring sensors. There are several sensor inputs on each unit which are collected by the SKF Multilog On-line

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2.1. Other Companies and Products Systems. These devices can also be complimented by two products, the CMON 2504 and the Metal particle sensor CMSS-ONL-100-2. The CMON 2504 is an acoustic emission interface card that provides the ability to access lubricant conditions within bearing while it is in service. Which gives the opportunity to detect more defects than just using the SKF Multilog On-line IMx. The Metal particle sensor CMSS-ONL-100-2 provides data regarding particle type, size and count rate per minute. Together with the SKF Multilog On-line System IMx it can help to give a more precise location where a problem has occurred. [10]

SKF offer two devices that can be used without the SKF Multilog On-line IMx, the CMPT CTU system and the SKF Machine Tool Observer MTx. The CMPT CTU has more functions than the IMx. It can collect measurements about temperature and vibrations and communicate alarms when needed. The outputs can be used directly for displaying or communicated to the customer via mail or text. There exist general functions that can be included depending on the device it is connected to, to acknowledge problems. The SKF Machine Tool Observer MTx receive measurements from sensors to document data regarding rotating parts of the machine. It can also give control signal output, such as emergency stop if the problem it detects is severe. SKF provides with a Server/Client software where the data that the SKF Machine Tool Observer MTx collects. Just as the CMPT CTU, the SKF Machine Tool Observer MTx can send out alerts via mail or text to the customer if problems occur. [10]

SKF Multilog On-line System WMx

This product is similar to the IMx, but it is wireless. It has eight sensor inputs that can measure temperature and vibration. The sensors must be connected via cables to the measuring points. It can be both battery driven and connected to a power supply and has a battery life up to three years. It uses WiFi as the communication medium and has a range of 300 m. The data that is collected is acceleration, velocity, displacement, temperature, speed and bearing condition. The SKF Multilog On-line System WMx can be viewed in Fig. 2.2. [11]

SKF Multilog On-line System WVT

The SKF Multilog On-line System WVT is very similar to the earlier product. It has 17 measurement input but can also use measurements from a tachometer sensor. [12]

SKF Wireless Machine Condition Sensor - CMWA 8800

This device is a combined sensor, data collector and radio, all in one. This device falls under the complete wireless condition monitoring category. That means that no additional sensors must be used and connected anywhere. The communication protocol the prod-

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2.1. Other Companies and Products

Fig. 2.2: The SKF Multilog On-line System WMx. [11]

uct uses is called WirelessHART, which is optimized for plant areas. It collects vibration and temperature data and can detect faults such as imbalance, misalignment, looseness and bearing problems. To detect problems, it uses a feature called SKF Acceleration En- veloping. It runs on battery and has up to five years of battery life. Because it uses Wire- lessHART to be able to use this product a WirelessHART Gateway is needed to transfer the data onto a local area network. The WirelessHART protocol uses a meshed network- ing method which has greater reliability. The SKF Wireless Machine Condition Sensor - CMWA 8800 can be viewed in Fig. 2.3. [13]

Tools for analyzing

SKF provides an analytic software that is called SKF @ptitude Monitoring Suite [14].

This software can be used for all the Multilog On-line System IMx and the three wireless products. The tool includes three components, the SKF @ptitude Analyst and SKF @ptitude Inspector, the SKF @ptitude Observer and SKF Customized Interfacing [14]. The SKF Customized Interfacing purpose is to optimize process monitoring and performance and give the customer a tailored experience when using the software [15].

The SKF @ptitude Observer is a protection and condition monitoring software for rotating machine. This software can collect and display data from several hundred machines in one place for easy access. It can both display live data and history data in mul- tiple trends. The machines parameters are used to display machine fault frequencies. [16]

There are two types of warnings that can be implemented in the SKF @ptitude Observer, primary and secondary alarms. The primary alarms sample time depends on the

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Fig. 2.3: The SKF Wireless Machine Condition Sensor - CMWA 8800. [13]

measurements and the secondary alarms sample time can be set by the user. The software can also generate several kinds of useful reports that the customer might want to have. It stores the data in a Microsoft SQL Server with a maximum storage of 10 GB. The alarm setting capabilities that exists are many. An unlimited number of trend alarms can be set and a four alarm levels per measurement. When an alarm is triggered an automatic mail or text can be sent to the user. [16]

The last components for the SKF @ptitude Monitoring Suite is the SKF @ptitude Analyst and SKF @ptitude Inspector software. The SKF @ptitude Inspector is mainly tar- geted for Online Dispute Resolution (ODR) [17]. The SKF @ptitude Analyst is a software for diagnostics and analyses [17]. When the SKF @ptitude Observer only do analysis for on-line system, the SKF @ptitude Analyst is a step higher in the hierarchy [18]. This software can be used in a more broader sense, e.g. for operator inspection rounds, periodic condition monitoring data collection or in-depth vibration analysis and expert advice [17].

It can make a more detailed level at the alarm level where it can display all points that needs immediate attention [18]. It can also do multi-parameter analysis which can help to figure out in more detail where a problem might be located [18]. Another feature of the software is its scalability, where add-ons can be added to tailor to specific needs [18].

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2.1. Other Companies and Products Services and support

SKF guaranties full support that the products you buy from them are maintained to the SKF quality standards. The SKF Product Support Plans works for both hardware and software. Everything from telephone and email support to help with calibration and different courses are available in this plan. [19]

2.1.3 Schaeffler Technologies

Schaeffler Technologies is a company that mainly focus on bearings. To support their products, they have also developed a condition monitoring device called FAG SmartCheck and several tools for analysing the data that the FAG SmartCheck collects. [20]

FAG SmartCheck

The FAG SmartCheck has an integrated vibration sensor and external sensors such as temperature and speed can be connected to it. It uses ethernet to communicate, which makes this product a mix of all the described categories in the introduction. It is a ready to use product immediately at delivery, with general settings that can be used for condition monitoring. The machines parameters can be implemented directly into the device for more precise and customized monitoring. The FAG SmartCheck has also an integrated database for FAG and INA standard bearings to detect the specific problem it detects.

Several FAG SmartChecks can be mounted on the same machine to monitor specific parts.

A led light is visible on the device to indicate if an alarm has been triggered. It has no battery but uses a power source to function. The FAG SmartCheck can be viewed in Fig.

2.4. [20]

Fig. 2.4: The FAG SmartCheck. [20]

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2.1. Other Companies and Products Tools for analyzing

The FAG SmartCheck has many features on its own, but Schaeffler Technologies also provides some software’s to make more detailed displaying and analysing available. They offer three different software’s, FAG SmartWeb, FAG SmartUtility light and FAG SmartU- tility. The FAG SmartWeb software is a software that is integrated with the FAG SmartCheck and FAG SmartUtility light is a free PC software that is included in delivery of the device. The FAG SmartUtility is a paid-for software that allows the user to use all the available functions. FAG SmartWeb includes displaying and configuration of the FAG SmartCheck. FAG SmartUtility light expands the FAG SmartWeb by including handling of all SmartCheck devices. FAG SmartUtility expands this even further by including analytical software. If using the FAG SmartUtility the user can analyse the data with help of trends, time signals and frequency spectrum. [20]

Schaeffler Technologies offers a wide range of services. They can help with commission- ing, courses, operation and even remote monitoring if there are no trained users available on the site [15]. Regarding support the user will be provided with a direct link to the Schaeffler monitoring center [16].

2.1.4 AMC Vibro

AMC Vibro, just as SKF, is a company that does not build systems them self but focus on compliment the systems with devices for monitoring conditions. They do offer lone standing sensors, but they also have two wireless condition monitoring sensors, the AV Sensor 2000R and the AV Sensor 4000R [21]. These two products will be described in more detail below.

AV Sensor 2000R

This product is a sensor measuring temperature and vibrations with inbuilt radio. It has an internal memory for storage and a battery life time up to six years. It needs a relay called AV Monitor Gateway to receive the data. The AV Sensor 2000R can be viewed in Fig.

2.5. [21]

AV Sensor 4000R

The AV Sensor 4000R is similar to the 2000R. It is powered by a power source or induc- tive charging. You have to add a temperature sensor to measure temperature. It also has a humidity sensor, which the 2000R does not have. It needs the AV Monitor Gateway to

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Fig. 2.5: The AV Sensor 2000R. [22]

receive data. Beside this it can also trigger alarms and warnings that can be sent to relays if that is desired. The AV Sensor 4000R can be viewed in Fig. 2.6. [21]

Fig. 2.6: The AV Sensor 4000R. [21]

Tools for analyzing

The AV Sensor 2000R and 4000R sends the data to the AV Monitor Gateway. This device has a web server where the customer can configure the system and view the received data.

It can respond to warnings and alarms. The user can connect a SD card to save data on and monitor the condition of the sensor batteries. The user interface that AMC Vibro can provide is called the VIBnavigator. The features of this interface includes displaying data and configuration of thresholds. There exist two versions of the VIBnavigator, the Stan- dard Edition and the Remote Diagnostics Edition. The later has features such as dedicated databases for analysis, managing data sets and events from a phone. [22]

AMC Vibro offers several services. The AVE Care for constant monitoring 24 hours a day of key machines. The AVE Training provides with information on how to use the

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2.1. Other Companies and Products equipment. The AVE Rate helps to assess the dynamic state of the on-site measurement.

[23]

2.1.5 Elovis

Elovis is a company that focus on contact-less monitoring, long time monitoring and wireless monitoring. The advantages with their wireless monitoring is that it is live. They have two product that can be used for wireless condition monitoring, the Tediasens SN-X and Tediasens SN-I. These two products will be described below in more detail. [24]

Tediasens SN-X

This product has three analog measurement channels, where almost any sensor can be connected to. It has an inbuilt radio which uses WiFi as protocol. It is powered by a power source and has not the feature of a battery. The Tediasens SN-X needs a Tediasens AP as an relay that can receive the data that is being measured. The Tediasens SN-X can be viewed in Fig. 2.7. [24]

Fig. 2.7: The Tediasens SN-X. [23]

Tediasens SN-I

The Tediasens SN-I is similar to the previous product, but instead of three input channels it has an internal acceleration sensor that can measure vibrations. Just as de SN-X it uses WiFi for communication and does not have an integrated battery. It also needs a Tediasens AP that can receive the data it measures. It can be screwed on to the machine or fastened by magnets. The Tediasens SN-I can be viewed in Fig. 2.8. [24]

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Fig. 2.8: The Tediasens SN-I. [24]

Tools for analyzing

There exists an interface that is called the Tediasens GUI. Though to get information about this interface one has to contact Elovis. [24]

2.1.6 Ascribo

Ascribo is another company that focus on condition monitoring and control of electrical machines. They use external sensors that can be connected to a gateway, which in turn can send the data via ethernet or wireless with 3G. They use two kinds of sensors, an acceleration sensor that can measure vibrations and a hall sensor that can measure rotation speed. The two gateways that they offer are called Able Gateway E and Able Gateway 3G.

A more thorough description of these two gateways will presented below. [25, 26]

Able Gateway

The two different products, the E and 3G, has the same capabilities and the only difference is that the 3G uses radio to communicate the data. These two gateways can handle 254

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2.1. Other Companies and Products sensors and the 3G version is delivered with a machine cabin for storage. [25, 26]

Tools for analyzing

Ascribo can provide the user with a cloud application called Able Monitor. This application can display data on computers and mobile phones. Its purpose is to give the user an overview of the machines that are connected to the gateway. The user can set alarms that can also be sent by email or text message when triggered. The Able Monitor provides information about what kind of fault that has occurred, such as inner race, outer race or rolling element fault. [27]

Ascribo has a portfolio of services. They can assist with decision regarding monitoring, installation, propose solutions, training, support. They have also specialist that can help with analysis if interpretation is needed. [28]

2.1.7 TWave

The company TWave has a similar solution as Ascribo, though they have no version that is wireless. It is a company that focus on solutions for condition monitoring. The two modules that they offer are called the T8 M and T8 L. The modules are used as the Ascribos gateways, they receive measurement data from the connected sensors. A more detailed description of the T8 M and T8 L is resented below. [29]

T8 M and T8 L

These to modules are used to receive measurement data from the connected sensors. The sensor that TWave offers is an acceleration sensor that can measure vibrations. As mentioned earlier, the T8 M and T8 L both need an ethernet cable connected to them, no wireless alternative exists. Both of these modules have eight dynamical inputs. The difference between the T8 M and T8 L is that the L has four static inputs, which the M does not have. They are not battery driven and needs a power source. TWave has also an ex- tension module that provides an additional four analog inputs and four relay outputs. The T8 M and T8 L modules can be viewed in Fig. 2.9. [29]

Tools for analyzing

TWave offers three optional software’s, the Supervisor, Diagnostic and Turbomachinery.

These can also be customized, so that the customer only pays for the functions it needs.

The software’s are web based, so no additional application has to be installed. The Su-

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Fig. 2.9: To the left: the T8 M, to the right: the T8 L. [28]

pervisor can only display the data. The Diagnostic includes some analytical tools and Turbomachinery extend the analytical features even further. [30]

2.1.8 Monnit

Monnit is a company that does not focus on condition monitoring for only machines, but for any purpose that needs a wireless sensor. For the purpose of condition monitoring Monnit can offer acceleration sensors for vibration measurements and temperature sensors. They offer one acceleration sensor and three different kinds of temperature sensors, these will be explained below. [30], [31]

Sensor

Moonit offers an acceleration sensor that can measure vibrations. It is powered by coin cells battery and has a lifetime up to two years. [30]

The three temperature sensors are hardwired thermocouple sensors. They are called Coin Cell, AA battery and Industrial. The Coin Cell is of course powered by coin cells batteries. It has a battery life up to three years. The AA battery version has a longer life time, up to eight years. The industrial is more optimal for industries. It has the same features as the AA battery but is more protected against dirt, dust, water and ice. Because all of these are thermocouple the actual sensor can be placed at some distance from the radio. [31]

Tools for analyzing

Moonit offers several tools for analyzing the measurements, but nothing that is customized for electrical machines. To analyze the measurements a gateway is needed that can receive the data from the sensors. They offer software’s for analyzing, configurations, web storage, alarms, notifications via mail or text. [32]

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2.2. Simotics IQ Services and support

When it comes to services Moonit can offer product customization, product development, training and installation. [33]

2.2 Simotics IQ

The Simotics IQ is a condition monitoring concept developed by Siemens. This concept consists of an electrical Low Voltage (LV) IM, Simotics Connect, MindSphere and Min- dApp [2]. This concept is so far tailor made for the 1LE5 industry motor that Siemens has developed. The three services will be described below.

2.2.1 Simotics Connect

This device is a combined sensor, data acquisition and radio, that is placed on a LV IM [2].

This product does not need connection to specific measuring points on the machine. The Simotics Connect falls under the complete wireless condition monitoring group. It can measure vibrations, housing surface temperature and magnetic flux density [3]. Several values can be calculated from these measurements, the values are listed in Table 2.1 [3].

The device uses WiFi in order to send the data and has a battery life up to two years [2].

Table 2.1: Calculated values from the Simotics Connect Calculated values

Electrical stator frequency Slip frequency

Torque Electrical Power Operating status Operating hours Number of starts Rotational speed

2.2.2 MindSphere

MindSphere is an open, cloud-based operating system developed by Siemens. Its purpose is to gather data from the industry and use this data for tasks that helps the consumer. In MindSphere this data can be analyzed and displayed to the customer in different applications. MindSphere also offers the option for the customer or other companies to program their own applications [34].

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2.2. Simotics IQ

2.2.3 MindApp

As previously mentioned the MindSphere has many usable applications. One that is suitable for customer that has a LV IM with a Simotics Connect attached is the MindApp. The MindApp can provide an overview of the machines condition and trigger alarms when something is wrong. It also provides with the possibility of more detailed information about the machines. This application is tailor made for the 1LE5 motor. [3]

2.2.4 Services and support

Siemens offers both service and support to their customers. Regarding services, they offer life cycle analysis, optimization and modernization, spare parts and repair, but also service on site. The support they offer includes manuals and guides for their products, technical consulting and technical support for free. They have also paid-technical support that provides with prioritized answering and availability 24 hours a day. [35]

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Chapter 3 Digital twin

A technique, that can be useful when digitalizing the industry, is to have digital twins of all the components. A digital twin is a copy of the real components geometry and features that is stored digitally. One digital twin together with specific measurements can be used to monitor the condition of the component with high accuracy. Together with all the other digital twins in a industry a digital factory can be utilized for even higher accuracy concerning fault detection and optimization problem.

A producer of a certain component can of course create a digital twin with the knowledge of all the geometrical parameters and features. When dealing with third party motors, the geometry and its features are unknown and has to be estimated. In this thesis a technique called Preliminary Electromagnetic Sizing is used to estimate the geometry of an third party motor using only the nameplate data, which is known. This technique is first and foremost used when doing preliminary design of induction motors, but has here been adapted in order to estimate the geometry when knowing the nameplate data. The data that nameplate includes are the rated speed, number of poles, rated voltage, rated power, rated current, stator current frequency, power factor, efficiency and total weight. In the upcoming sections the method of creating a digital twin with the preliminary electromagnetic sizing method is explained.

3.1 Preliminary electromagnetic sizing

First several geometrical parameters has to be defined. These parameters can be viewed in Fig. 3.1. The preliminary electromagnetic sizing method that has been adapted from [36]. Before the algorithm is described four sections will go through the parameters the algorithm needs. These sections are divided into known parameters, assumed parameters without range, assumed parameters with range and calculated parameters.

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3.1. Preliminary electromagnetic sizing

A_s,slot

A_r,slot r_r

r_{shaf t} r_s,in

r_r,yoke,out

r_s,out

h_r,yoke h_s,yoke

h_s,slot

h_r,slot

h_r,slot,enc hs,slot,enc

h_s,enc

δ ws,slot,open

wr,slot,open

wr,slot,out

w_s,slot,out

w_r,tooth ws,tooth

wr,slot,in

r_house

Fig. 3.1: Geometrical parameters of the stator and rotor.

3.1.1 Known parameters

The known parameters that needed for the algorithm are listed in Table 3.1. The first six parameters in this table can be found on the nameplate of the machine. The active length is assumed to be available through estimation by observing the machine that is in question.

3.1.2 Assumed parameters without range

There are several parameters needed in the algorithm that has to be assumed. Some guidelines for these assumptions exists in [37]. The parameters, their assumed value and argu- ments for the assumptions can be viewed in Table 3.2.

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3.1. Preliminary electromagnetic sizing Table 3.1: Known Parameters

Symbol Description

P_rated Rated output power

p Number of poles

n_rated Rated rotor speed

Vs,rated Rated fundamental phase voltage f_s Fundamental stator frequency

cos(φ) Power factor

l Active length of the IM

Table 3.2: Assumed Parameters Without Range

Symbol Description Assumed value Argumentation

h_s,slot,enc See Fig. 3.1 1· 10⁻³m

In the range of a few millimeters [37]

hs,enc See Fig. 3.1 0.5· 10⁻³m

Smaller than h_s,slot,enc according to design

h_r,slot,enc See Fig. 3.1 2· 10⁻³m

Bigger than h_s,slot,enc[37]

ws,slot,open See Fig. 3.1 2.5· 10⁻³m

Estimation through experience

wr,slot,open See Fig. 3.1 1· 10⁻³m

Estimation through experience

q_s,skew Slot-skew factor 1 Commonly 1 [37]

c_s

Stator winding

connection factor 1

Assuming Y-connected stator winding nstrand

Number of strands

making one conductor 1

Assuming one strand per conductor

As mentioned only guidelines exists for these parameters, they could vary and some knowledge regarding induction motors can be useful. One can study the equations in the upcoming algorithm to see how these parameters affects the resulting geometry if the result seems unreasonable. For example, wr,slot,open will affect the rotor slot area to some degree but ws,slot,openwill does not affect the stator slot area. ws,slot,openis not included in the algorithm, but is needed to get a complete geometry.

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3.1.3 Assumed parameters with range

Just as there were some guidelines to the parameters described in 3.1.2, [36, 37] also provides a recommended data range for other parameters that will be used in the algorithm.

In Table 3.3 the description of these parameters and their recommended range are listed.

In Table 3.4 a range of different recommendations for stator and rotor slots are listed dependent on number of poles.

Table 3.3: Assumed Parameters With Range

Symbol Description Range

∆ ˆV_s,rated

Peak fundamental voltage

drop at rated load 0.5 ˆV_sto 0.1 ˆV_sV δ_lam Lamination thickness 0.2· 10⁻³to 0.5· 10⁻³ m

Bˆ_δ

Peak fundamental flux

density in the air gap 0.8 to 1.0 T

B_s,tooth Flux density in the stator tooth 1.6 to 2.0 T

Bs,yoke Flux density in the stator yoke 1.4 to 1.7 T

B_r,tooth Flux density in the rotor tooth 1.6 to 2.0 T

B_r,yoke Flux density in the rotor yoke 1.4 to 1.7 T

C_{T e} Torque reduction constant 0.9 to 0.95 Jˆ_s

Peak fundamental current density

in the stator winding 3.5· 10⁶ to 5.5· 10⁶ A/m² Jˆ_r

Peak fundamental current density

in the rotor bars 2.0· 10⁶ to 4.0· 10⁶ A/m² δ Air gap height, see Fig. 3.1 0.3· 10⁻³to 0.6· 10⁻³ m

C_{s,f ill} Stator winding fill factor 0.3 to 0.5

Table 3.4: Suitable stator and rotor slots combinations

Number of poles Q_s/Q_r

2 36/28, 48/38, 54/46, 60/52 4 48/40, 48/56, 60/44, 60/76 72/58 6 54/42, 54/66, 72/54, 72/84, 72/88

8 45/70, 72/58, 72/88

Just as in 3.1.2, these ranges are just recommended guidelines and can be adjusted outside their range if the resulting geometry is unreasonable. Later in this chapter a reasoning will be described how to choose a value from these ranges.

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3.1.4 Calculated parameters

Several parameters, that are needed in the upcoming algorithm, can now directly calculated with the parameters from 3.1.1 - 3.1.3. Equation (3.1) to (3.10) shows how to calculate these parameters and description for each parameter can be found in Table 3.5.

T_rated = P_rated 2· π ·n_rated

60

(3.1)

Vˆ_s,rated =√

2· Vs,rated (3.2)

r_{shaf t} = (0.075· T^rated+ 17.5)· 10⁻³ (3.3)

Cs,lam = 42.5· δ^lam+ 0.6032· arctan 108 · 10³· δ^lam

(3.4)

q_s= Q_s p 3

(3.5)

γ_s = 2· π Qs

(3.6)

γr = 2· π

Q_r (3.7)

k_skew,1 =

sin π· p · qs,skew

2· Q^s

!

π· p · q^s,skew 2· Q^s

(3.8)

kd,1 =

sin π 6

!

q_s· sin π 6· q^s

! (3.9)

k₁ = k_skew,1· k^d,1 (3.10)

Note that no pitch factor is included in the winding factor, because we can not know if the stator winding is short pitched. Later in the calculations we will also assume that the lamination thickness is the same in the rotor as in the stator, i.e. that C_r,lam= C_s,lam

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3.1. Preliminary electromagnetic sizing Table 3.5: Calculated Parameters according to [37]

Symbol Description

T_rated Rated torque

Vˆ_s,rated Rated peak fundamental phase voltage

r_{shaf t} See Fig. 3.1

C_s,lam Lamination factor for stator q_s Stator windings per pole per phase γ_s Stator slot pitch angle γ_r Rotor slot pitch angle

kskew,1 Skew factor

k_d,1 Distribution factor

k₁ Winding factor

3.1.5 Geometry estimation algorithm

The estimation algorithm consists of 15 steps and is based on the preliminary electromagnetic sizing method from [36]. The algorithm includes an iteration where the rotor radius is increased until certain qualifications are met. Note that the goal of this estimation is to calculate the values for the unknown parameters in Fig. 3.1.

1. First an initial value for the rotor radius, rr, has t be selected. This value should be somewhat bigger than the calculated shaft radius, r_{shaf t}.

2. We can now calculate the parameters h_r,yoke, w_r,tooth and r_r,yoke,out with Equation (3.11) to (3.13).

h_r,yoke= 2r_rBˆ_δ

C_r,lamB_r,yokep (3.11)

w_r,tooth= 2πr_rBˆ_δ

C_r,lamQ_rB_r,tooth (3.12)

r_r,yoke,out = r_{shaf t}+ h_r,yoke (3.13)

3. Then h_r,slot can be calculated with Equation (3.14).

h_r,slot = r_r− rr,yoke,out− hr,slot,enc (3.14) If h_r,slot > 0 one can proceed to step 4. Otherwise algorithm restarts with increased rotor radius, rr.

4. Now wr,slot,in can be calculated with Equation (3.15).

w_r,slot,in = γ_r− 2 arcsin

w_r,tooth 2rr,yoke,out

!

r_r,yoke,out (3.15)

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3.1. Preliminary electromagnetic sizing If w_r,slot,in ≤ 0 an triangular rotor slot has to be implemented and h^r,yokehas to be recomputed with Equation (3.16).

h_r,yoke= w_r,tooth 2 sin γ_r 2

! − r^{shaf t} (3.16)

If h_r,yoke is recomputed r_r,yoke,out and h_r,slot has to be computed once again with Equation (3.13) and (3.14). Once again, if h_r,slot > 0 one can proceed to step 5.

Otherwise algorithm restarts with increased rotor radius, rr. 5. w_r,slot,outcan now be calculated with Equation (3.17).

w_r,slot,out= (r_r,yoke,out+ h_r,slot) γ_r− 2 arcsin

w_r,tooth

2(r_r,yoke,out+ h_r,slot)

!

(3.17)

6. The rotor slot area Ar,slotis then calculated with Equation (3.18) A_r,slot = (w_r,slot,in+ w_r,slot,out)h_r,slot

2 +(w_r,slot,out+ wr,slot,open)h_r,slot,enc

2 (3.18)

As we assumed that the rotor bars are skewed, with q_s,skew = 1, we need to calculate the effective rotor slot area, A⁰_r,slot with Equation (3.19).

A⁰_r,slot = l

v u u

tl²+ 2πq_s,skewr_r Q_r

!2Ar,slot (3.19)

7. Then we can compute the peak fundamental rotor bar current, ˆI_r, with Equation (3.20).

Iˆ_r= ˆJ_rA⁰_r,slot (3.20)

8. The torque, T_e can then be calculated with Equation (3.21).

T_e = C_{T e}Q_rk_skew,1Iˆ_rlr_rBˆ_δ

2 (3.21)

If the calculated torque, T_e, is not within a decided boundary requirement of the rated torque, Trated, the algorithm restarts with increased rotor radius, rr. Otherwise one can proceed to step 9.

9. Now the number of turns in each stator slot, ns can be calculated with Equation (3.22).

n_s= ( ˆV_s,rated− ∆ ˆV_s,rated)c_s

4πq_sk₁lr_rBˆ_δf_s (3.22)

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3.1. Preliminary electromagnetic sizing 10. After that the peak fundamental rotor current can be transformed to the stator side

with Equation (3.23).

Iˆ_r⁰ = Qscs

3n_sq_sk₁p

Iˆ_r (3.23)

11. Then the peak fundamental stator current, ˆI_s, can be calculated with Equation (3.24).

Iˆ_s = Iˆ_r⁰

cos(φ) (3.24)

12. When knowing the peak fundamental stator current, ˆI_s, the stator slot area, As,slot

can be calculated with Equation (3.25).

As,slot = n_sIˆ_s Cs,f illJˆs

(3.25)

13. Now ws,tooth and ws,slot,in can be calculated with Equation (3.26) and (3.27), respectively.

w_s,tooth = 2πr_s,inBˆ_δ

C_s,lamQ_sB_s,tooth (3.26)

w_s,slot,in = 2π(r_s,in + h_s,slot,enc)

Q_s − w^s,tooth (3.27)

14. After that can we calculate h_s,slot and w_s,slot,out with Equation (3.28) and (3.29), respectively.

h_s,slot =1 4

(

csc π Q_s

"

2(h_s,slot,enc+ r_s,in) sin π Q_s

+ w_s,slot,in− w^s,tooth

!2

+ 16A_s,slotsin π Q_s

#1/2

− 2(h^s,slot,enc+ r_s,in)

+ csc π Qs

(w_s,tooth− w^s,slot,in) )

(3.28) w_s,slot,out=1

2 ("

2(h_s,slot,enc+ r_s,in) sin π Q_s

+ w_s,slot,in− w^s,tooth

!2

+ 16A_s,slotsin π Q_s

#1/2

+ 2(h_s,slot,enc+ r_s,in) sin π Q_s

− w^s,tooth− w^s,slot,in )

(3.29)

Utilization of a tailormade condition monitoring device for third party motors

Utilization of a tailormade condition monitoring device for third party motors

PONTUS GRAHN

Utilization of a tailormade condition monitoring device for third party motors

Abstract

Sammanfattning

Acknowledgements

Contents

Acronyms

Nomenclature

Chapter 1 Introduction

1.1 Background and objectives

1.2 Thesis outline

Chapter 2

Market research

2.1 Other Companies and Products

2.1.1 ABB

2.1.2 SKF

2.1.3 Schaeffler Technologies

2.1.4 AMC Vibro

2.1.5 Elovis

2.1.6 Ascribo

2.1.7 TWave

2.1.8 Monnit

2.2 Simotics IQ

2.2.1 Simotics Connect

2.2.2 MindSphere

2.2.3 MindApp

2.2.4 Services and support

Chapter 3 Digital twin

3.1 Preliminary electromagnetic sizing

3.1.1 Known parameters

3.1.2 Assumed parameters without range

3.1.3 Assumed parameters with range

3.1.4 Calculated parameters

3.1.5 Geometry estimation algorithm