Electric Machines

Modeling, Condition Monitoring, and Fault Diagnosis

Hamid A. Toliyat, Subhasis Nandi, Seungdeog Choi, Homayoun Meshgin-Kelk

• Publisher: CRC Press
• Year: 2017
• Print ISBN: 9780849370274
• eBook ISBN: 9781351837873
• Topic: Technology & Engineering
• Subtopic: Electrical Engineering & Telecommunications
• Index: Technology & Engineering

With countless electric motors being used in daily life, in everything from transportation and medical treatment to military operation and communication, unexpected failures can lead to the loss of valuable human life or a costly standstill in industry. To prevent this, it is important to precisely detect or continuously monitor the working condition of a motor. Electric Machines: Modeling, Condition Monitoring, and Fault Diagnosis reviews diagnosis technologies and provides an application guide for readers who want to research, develop, and implement a more effective fault diagnosis and condition monitoring scheme—thus improving safety and reliability in electric motor operation. It also supplies a solid foundation in the fundamentals of fault cause and effect.

Combines Theoretical Analysis and Practical Application

Written by experts in electrical engineering, the book approaches the fault diagnosis of electrical motors through the process of theoretical analysis and practical application. It begins by explaining how to analyze the fundamentals of machine failure using the winding functions method, the magnetic equivalent circuit method, and finite element analysis. It then examines how to implement fault diagnosis using techniques such as the motor current signature analysis (MCSA) method, frequency domain method, model-based techniques, and a pattern recognition scheme. Emphasizing the MCSA implementation method, the authors discuss robust signal processing techniques and the implementation of reference-frame-theory-based fault diagnosis for hybrid vehicles.

Fault Modeling, Diagnosis, and Implementation in One Volume

Based on years of research and development at the Electrical Machines & Power Electronics (EMPE) Laboratory at Texas A&M University, this book describes practical analysis and implementation strategies that readers can use in their work. It brings together, in one volume, the fundamentals of motor fault conditions, advanced fault modeling theory, fault diagnosis techniques, and low-cost DSP-based fault diagnosis implementation strategies.

1

Introduction

Seungdeog ChoiPh.D.

Toshiba International

The population of electric motors has greatly increased in recent years, not only in the United States but also in the world market as shown in Table 1.1 and Table 1.2. The world market is expected to be around $16.1 billion in 2011, which is assumed more than 50% growth just within 5 years [1]. Electric motors have been applied to almost every place in our daily life, such as manufacturing systems, air transportations, ground transportations, building air-conditioner systems, home energy conversion systems, various cooling systems in electrical devices, and even cell phone vibration systems.

It is also a well-known fact that the electric motors consume more than 50% of whole electrical energy demand in the United States. The annual electrical energy demand in the United States was 3,873 billion kilowatt-hours in 2008, which is expected to be further increased in every year depending on population and economic growth [11]. This data indicates that more than 1,900 billion kilowatt-hours is consumed by electric motors annually in the United States, which is the biggest energy consumption by any single electric device in modern society.

With the rapidly increased population and huge electric energy consumption, sophisticated control and reliability of motor operations from a harsh industrial environment has now been a major requirement in many industrial applications. It is especially important where an unexpected shutdown might result in the interruption of critical services such as medical, transportation, or military operations. In those applications where continuous process is needed and where down time is not tolerable, an unexpected failure of a motor might result in costly maintenance or loss of life.

As shown in Figure 1.1, the electrical motor consists of many mechanical and electrical parts, such as a rotor bar, rotor magnet, stator winding, endring, bearing, and gear box. Due to the commonly harsh industrial environments, each part of electric motors is potentially exposed to the high risk of unexpected mechanical, chemical, and electrical system failures. The reasons why electric motors fail in industry have been commonly reported as follows:

1. Post the standard lifetime
2. Wrong-rated power, voltage, and current
   TABLE 1.1
   Number of Motors by Application

   Application	Population
   Fans and pumps	3,847,161
   Air compressor	632,731
   Others	7,954,438
   TOTAL	12,434,330

   Source: US Department of Energy (2002). http://www1.eere.energy.gov/manufacturing/tech_deployment/pdfs/mtrmkt.pdf
3. Unstable supply voltage or current source
4. Overload or unbalanced load
5. Electrical stress from fast switching inverters or unstable ground
6. Residual stress from manufacturing
7. Mistakes during repairs
8. Harsh application environment (dust, water leaks, environmental vibration, chemical contamination, high temperature)

Figure 1.2 shows an example of a well known electrical motor fault such as bearing ball damage. The bearing ball is taken from the bearing module that had been diagnosed as faculty for 6 months. The main types of motor faults are commonly categorized as electrical faults, mechanical faults, and outer drive system defects, which are as follows [2–5]:

1. Electrical faults
   1. Open or short circuit in motor windings (mainly due to winding insulation failure)
   2. Wrong connection of windings
   3. High resistance contact to conductor
   4. Wrong or unstable ground
   TABLE 1.2
   Motor System Energy Usage by Application

   Application	GWh / Yr
   Fans and pumps	221,417
   Air compressor	91,050
   Others	262,961
   TOTAL	575,428

   Source: US Department of Energy (2002). http://www1.eere.energy.gov/manufacturing/tech_deployment/pdfs/mtrmkt.pdf
   

   FIGURE 1.1
   2009 Honda FCX Clarity Fuel Cell Vehicle test drive photo gallery. From Christine and Scott Gable, http://alternativefuels.about.com/od/fuelcellvehiclereviews/ig/09-Honda-FCX-Clarity-Fuel-Cell/
2. Mechanical faults
   1. Broken rotor bars
   2. Broken magnet (or partial demagnetization)
   3. Cracked end-rings
   4. Bent shaft
   5. Bolt loosening
   6. Bearing failure
   7. Gearbox failure
   8. Air-gap irregularity
3. Outer motor drive system failures
   1. Inverter system failure
   2. Unstable voltage/current source
   3. Shorted or opened supply line

FIGURE 1.2
Bearing ball fault and subsequent fatigue damage. Vibration consultant. http://www.vibrationconsultants.co.nz/Fault%20Diagnosis.html

The bearing fault is known to make up almost 40%, stator related about 38%, rotor related about 10%, and others make up 12% of whole electrical motor fault [2–6].

The electric motor design is commonly intended to have electrical and mechanical symmetry in the stator and the rotor for better coupling and higher efficiency...

Table of contents

• Cover Page
• Title Page
• Copyright Page
• Contents
• Preface
• 1 Introduction
• 2 Faults in Induction and Synchronous Motors
• 3 Modeling of Electric Machines Using Winding and Modified Winding Function Approaches
• 4 Modeling of Electric Machines Using Magnetic Equivalent Circuit Method
• 5 Analysis of Faulty Induction Motors Using Finite Element Method
• 6 Fault Diagnosis of Electric Machines Using Techniques Based on Frequency Domain
• 7 Fault Diagnosis of Electric Machines Using Model-Based Techniques
• 8 Application of Pattern Recognition to Fault Diagnosis
• 9 Implementation of Motor Current Signature Analysis Fault Diagnosis Based on Digital Signal Processors
• 10 Electric Implementation of Fault Diagnosis in Hybrid Vehicles Based on Reference Frame Theory
• 11 Robust Signal Processing Techniques for the Implementation of Motor Current Signature Analysis Diagnosis Based on Digital Signal Processors
• Index