1.Literature Survey

Advances in Diagnostic Techniques for Induction Machines

This paper investigates diagnostic techniques for electrical machines with

special reference to induction machines and to papers published in the last ten

years. A comprehensive list of references is reported and examined, and

research activities classified into four main topics: 1) electrical faults;

2) mechanical faults; 3) signal processing for analysis and monitoring; and

4) artificial intelligence and decision-making techniques.

A review of induction motors signature analysis as a medium for faults

detection

This paper is intended as a tutorial overview of induction motors

signature analysis as a medium for fault detection. The purpose is to introduce

in a concise manner the fundamental theory, main results, and practical

applications of motor signature analysis for the detection and the localization of

abnormal electrical and mechanical conditions that indicate, or may lead to, a

failure of induction motors. The paper is focused on the so-called motor current

signature analysis which utilizes the results of spectral analysis of the stator

current. The paper is purposefully written without “state-of-the-art” terminology

for the benefit of practicing engineers in facilities today who may not be

familiar with signal processing.

Condition monitoring and fault diagnosis of electrical motors—A review

Recently, research has picked up a fervent pace in the area of fault

diagnosis of electrical machines. The manufacturers and users of these drives

are now keen to include diagnostic features in the software to improve salability

and reliability. Apart from locating specific harmonic components in the line

current (popularly known as motor current signature analysis), other signals,

such as speed, torque, noise, vibration etc., are also explored for their frequency

contents. Sometimes, altogether different techniques, such as thermal

measurements, chemical analysis, etc., are also employed to find out the nature

and the degree of the fault. In addition, human involvement in the actual fault

detection decision making is slowly being replaced by automated tools, such as

expert systems, neural networks, fuzzy-logic-based systems; to name a few. It is

indeed evident that this area is vast in scope. Hence, keeping in mind the need

for future research, a review paper describing different types of faults and the

signatures they generate and their diagnostics’ schemes will not be entirely out

of place. In particular, such a review helps to avoid repetition of past work and

gives a bird’s eye view to a new researcher in this area.

The use of the wavelet approximation signal as a tool for the diagnosis of

rotor bar failures

The aim of this paper is to present a new approach for rotor bar failure

diagnosis in induction machines. The method focuses on the study of an

approximation signal resulting from the wavelet decomposition of the start up

stator current. The presence of the left sideband harmonic is used as evidence of

the rotor failure in most diagnosis methods based on the analysis of the stator

current. Thus, a detailed description of the evolution of the left sideband

harmonic during the start up transient is given in this paper; for this purpose, a

method for calculating the evolution of the left sideband during the start up is

developed, and its results are physically explained. This paper also shows that

the approximation signal of a particular level, which is obtained from the

discrete wavelet transform of the start up stator current, practically reproduces

the time evolution of the left sideband harmonic during the start up. The

diagnosis method proposed here consists of checking if the selected

approximation signal fits well the characteristic shape of the left sideband

harmonic evolution described in this paper. The method is validated through

laboratory tests. The results prove that it can constitute a useful tool for the

diagnosis of rotor bar breakages.

Diagnosis of broken-bar fault in induction machines using discrete wavelet

transform without slip estimation

The aim of this paper is to present a wavelet-based method for broken-

bar detection in squirrel-cage induction machines. The frequency-domain

methods, which are commonly used, need speed information or

accurate slip estimation for frequency-component localization in any spectrum.

Nevertheless, the fault frequency bandwidth can be well defined for any quirrel-

cage induction machine due to numerous previous investigations. The proposed

approach consists in the energy evaluation of a known bandwidth with time-

scale analysis using the discrete wavelet transform. This new technique has

been applied to the stator-current space-vector magnitude and the instantaneous

magnitude of the stator-current signal for different broken-bar fault severities

and load levels

Monitoring of rotor-bar defects in inverter-fed induction machines at zero

load and speed

Rotor-cage fault detection in inverter-fed induction machinesis still

difficult nowadays as the dynamics introduced by the control or load influence

the fault-indicator signals commonly applied. In addition, detection is usually

possible only when the machine is operated above a specific load level to

generate a significant rotor-current magnitude. This paper proposes a new

method of detecting rotorbar defects atzero load and almost at standstill. The

method uses the standard current sensors already present in modern industrial

inverters and, hence, is non invasive. It is thus well suited as a start-up test for

drives. By applying an excitation with voltage pulses using the

switching of the inverter and then measuring the resulting current slope, a new

fault indicator is obtained. As a result, it is possible to clearly identify the fault-

induced asymmetry in the machine's transient reactances. Although the

transient-flux linkage cannot penetrate the rotor because of the cage, the faulty

bar locally influences the zigzag flux, leading to a significant change in the

transient reactances. Measurement results show the

applicability and sensitivity of the proposed method.

Monitoring of defects in induction motors through air-gap torque

observation

This paper suggests a method to monitor defects such as cracked rotor

bars and the shorted stator coils in induction motors. Air-gap torque can be

calculated while the motor is running. No special down time for measurement is

required. Data of the air-gap torque for a motor should be periodically kept for

comparison purposes. Since more data than just a line current are taken, this

method offers other potential possibilities that cannot be handled by examining

only a line current. The theoretical foundation for this proposed method is

presented. Experiments conducted on a 5-hp motor show the validity and

potential of this approach. Further studies are planned to extend the proposed

method in detail and to monitor defects developed in other types of rotating

machines

Estimation of frequency components in stator current for the detection of

broken rotor bars in induction machines

This paper presents a novel high-resolution signal processing

technique for non-intrusive detection of broken bar fault condition in induction

machine rotor. The technique is based on parametric

spectral estimation of stator current waveform recorded while a machine is

running. The frequency components that are related to broken rotor bar

condition are very close to the fundamental frequency, and this combined with

low signal to noise ratio makes the task of detecting a broken rotor bar condition

difficult. The method proposed is based on the least squares fit of the predefined

parametric signal model. The problem is nonlinear with a number of local

minimum values in the feasible region. Classical nonlinear least squares

methods, like Levenberg-Marquardt or Nelder-Mead algorithms, can converge

to a local minimum giving inaccurate spectral estimation parameters. To

overcome this problem we employed the global optimization algorithm based

on grid search. The grid on which the search for optimum is performed is

constructed using the Hyperbolic Cross Points (HCP). The global search

on the HCP grid is complemented with the Nelder-Mead local search algorithm,

to refine the result. We are able to

estimate broken rotor bar frequencies and the associated amplitudes with high

accuracy for wide range of motor operating conditions and

severities of broken rotor bar faults. The results presented in the paper show that

HCP algorithm can be used to diagnose broken bar fault in induction motor

using very short current signal segments and during light motor loadings.

The detection of inter-turn short circuits in the stator windings of

operating motors

This paper develops a winding-function-based method for modeling

polyphase cage induction motors with inter-turn short circuits in the machine sta

-tor winding. Analytical consideration which sheds light on some

components of the stator current spectra of both healthy and faulty machines is

developed. It is shown that, as a result of the nature of the cage rotor, no new

frequency components of the line current spectra can appear as a

consequence of the fault. Only a rise in some of the frequency components

which already exist in the line current spectra of a healthy machine can be

observed. An experimental setup comprising a 3 kW delta-

connected motor loaded by a generator was used to validate this

approach. The experimental results obtained clearly validate the analytical and

simulation results

Induction motor stator faults diagnosis by a current Concordia pattern-

based fuzzy decision system

This paper deals with the problem of detection and

diagnosis of induction motor faults. Using the fuzzy logic strategy, a better

understanding of heuristics underlying the motor faults detection

and diagnosis process can be achieved. The proposed fuzzy approach

is based on the stator current Concordia patterns. Induction motor stator

currents are measured, recorded, and used for Concordia patterns computation

under different operating conditions, particularly for different load levels.

Experimental results are presented in terms of accuracy in the detection

of motor faults and knowledge extraction feasibility. The preliminary results

show that the proposed fuzzy approach can be used for

accurate stator fault diagnosis if the input data are processed in an advantageous

way, which is the case of the Concordia patterns.

A model of asynchronous machines for stator fault detection and isolation

This paper presents a new model of asynchronous machines. This model

allows one to take into account unbalanced stator situations which can be

produced by stator faults like short circuits in windings. A mathematical

transformation is defined and applied to the classical abc model equations. All

parameters which affect this new model can be known online. This makes the

model very useful for control algorithms and fault detection and isolation

algorithms. The model is checked by comparing simulation data with actual

data obtained from laboratory experiments.

An impedance identification approach to sensitive detection and location of

stator turn-to-turn faults in a closed-loop multiple-motor drive

A single closed-loop inverter drive with multiple motors connected to it

is a type of drive topology commonly used in steel processing industry, electric

railway systems, and electric vehicles. However, condition monitoring for this

type of drive configuration remains largely unexplored. This paper

proposes an impedance identification approach to detectand locate

the stator turn-to-turn fault in a multiple-motor drive system. Sensitive and fa

----st fault detection is achieved by utilizing the characteristics of current

regulators in the motor controller. Experimental results show that the proposed

method can reliably detect and locate the stator turn fault on two shaft-coupled

5-hp induction machines under different operating conditions and fault levels

with no need of any machine parameters. Although originally developed for

multiple-motor drives, the detection scheme can also be directly

applied to most of the conventional closed-loop induction motor drives.

Effects of time-varying loads on rotor fault detection in induction machines

This paper addresses the problem of motor current spectral analysis for

the detection of non idealities in the air gap flux density when in the

presence of an oscillating or position-varying load torque. Several schemes have

been proposed for the detection of air gap eccentricities and broken rotor bars.

The analysis of these effects, however, generally assumes that the load torque is

constant. If the load torque varies with the rotational speed, then the motor

current spectral harmonics produced by the load will overlap the harmonics

caused by the fault conditions. The motor current spectral components in the

presence of various fault and load conditions are reviewed. The

interaction of the effects on the actual stator current spectrum caused by

the fault condition and the torque oscillations are shown to be separable only if

the angular position of the fault with respect to the load torque characteristic is

known. This is an important result in the formulation of an online fault

detection scheme that measures only a single phase of the stator current. Since

the spatial location of the fault is not known, its influence on a specific current

harmonic component cannot be separated from the load effects. Therefore,

online detection schemes must rely on monitoring a multiple frequency

signature and identifying those components not obscured by the load effect.

Experimental results which show the current spectra of an induction

machine under eccentric air gap and broken rotor bar conditions are given for

both fixed and oscillating loads

Dynamic simulation of dynamic eccentricity in induction machines-winding

function approach

The paper describes a method for the dynamic simulation of

dynamic rotor eccentricity in squirrel cage rotor induction machines. The

method is based on a winding function approach, which allows for all

harmonics of magnetomotive force to be taken into account. It is demonstrated

how this complex dynamic regime can be modeled using the mutual inductance

curves of symmetrical machine using proper scaling techniques. Experimental

results demonstrate the effectiveness of the proposed technique and validate the

theoretical analysis

Performance analysis of a three-phase induction motor under mixed

eccentricity condition

A substantial proportion of induction motor faults are eccentricity related.

In practice, static and dynamic eccentricities happen to exist together. With this

point in mind, an analytical approach to evaluate the performance ofa three-

phase induction motor under mixed eccentricconditions is presented in this

paper. A clear and step-by-step theoretical analysis, explaining completely the

presence of certain harmonics in the line-current spectrum in the

presence of eccentricity, is discussed. More importantly, it is shown for the first

time that a link exists between the low- and high-frequency elements of these

harmonics. It is also shown that these high-frequency components are not very

strong in all types of machine. These results will be useful in generating rules

and laws to formulate online tools for machine condition monitoring. Finite-

element results to substantiate the inductance values used in the simulation are

also included. The analysis is validated by the line-current spectrum of the

eccentric machine obtained through simulation using the modified winding-

function approach (MWFA) and experimentation.

Dynamic simulation of cage induction machine with air gap eccentricity

This paper describes a method for the dynamic simulation of a cage

induction machine with static and dynamic air gap eccentricity. The method is

based on the winding function theory and extension of this theory to the

nonuniform airgap case. A method of calculating all inductances in a

machine with both static and dynamic air gap eccentricity is presented and a

numerical analysis of a machine with specified parameters is presented. Stator

line current spectra collected experimentally for both the static and

dynamic eccentricity conditions confirm the results obtained by the proposed

numerical model.

Detection of eccentricity faults in induction machines based on nameplate

parameters

Eccentricity-related faults in induction motors have been studied

extensively over the last few decades. They can exist in the form of static or

dynamic eccentricity or both, in which case it is called a

mixed eccentricity fault. These faults cause bearing damage, excessive vibration

and noise, unbalanced magnetic pull, and under extreme conditions, stator-rotor

rub which may seriously damage the motors. Since eccentricity faults are often

associated with large induction machines, the repair or replacement costs arising

out of such a scenario may easily run into tens and thousands of dollars.

Previous research works have shown that it is extremely difficult to detect

such faults if they appear individually, rather than in mixed form, unless the

number of rotor bars and the pole-pair number conform to certain

relationships. In this paper, it is shown that the terminal

voltages of induction machines at switch-off reveal certain features that can lead

to the detection of these faults in individual form, even in machines that do not

show these signatures in line-current spectrum in steady state, or to

the detection of the main contributory factor in case of mixed eccentricity.

Bearing damage detection via wavelet packet decomposition of the stator

current

Bearing faults are one of the major causes of motor failure.

The bearing defects induce vibration, resulting in the modulation

of the stator current. In this paper, the stator current is analyzed via

wavelet packet decomposition to detect bearing defects. The proposed method

enables the analysis of frequency bands that can accommodate the rotational

speed dependence of the bearing defect

frequencies. The wavelet packet decomposition also provides a better

treatment of nonstationary stator current than currently used Fourier techniques.

Detecting motor bearing faults

Three-phase induction motors are the workhorses of industry because of

their widespread use. They are used extensively for heating, cooling,

refrigeration, pumping, conveyors, and similar applications. They offer users

simple, rugged construction, easy maintenance, and cost-effective pricing.

These factors have promoted standardization and development of a

manufacturing infrastructure that has led to a vast installed base of motors; more

than 90% of all motors used in industry worldwide are ac induction motors.

Causes of motor failures are bearing faults, insulation faults, and rotor faults.

Early detection of bearing faults allows replacement of the bearings, rather than

replacement of the motor. The same types of bearing defects that plague such

larger machines as 100 hp are mirrored in lower hp machines which have the

same type of bearings. Even though the replacement of defective bearings is the

cheapest fix among the three causes of failure, it is the most difficult one

to detect. Motors that are in continuous use cannot be stopped for analysis. We

have developed a circuit monitor for these motors. Incipient bearing failures are

detectable by the presence of characteristic machine vibration frequencies

associated with the various modes of bearing failure. We will show that circuit

monitors that we developed can detect these frequencies using wavelet packet

decomposition and a radial basis neural network. This device monitors an

induction motor's current and defines a bearing failure.

Fault detection of linear bearings in brushless ac linear motors by vibration

Analysis

Electric linear motors are spreading in industrial automation because they

allow for direct drive applications with very high dynamic performances, high

reliability, and high flexibility in trajectory generation. The moving part of the

motor is linked to the fixed part by means of linear bearings. As in many other

electric machines, bearings represent one of the most vulnerable parts because

they are prone to wear and contamination. In the case of linear roller bearings,

this issue is even more critical as the rail cannot be easily fully enclosed and

protected from environmental contamination, unlike the radial rotating bearing

counterpart. This paper presents a diagnostic method based

on vibration analysis to identify which signature is related to a specific fault.

2.Introduction

Condition monitoring and fault diagnosis of induction motors are of great

importance in production lines. It can significantly reduce the cost of

maintenance and the risk of unexpected failures by allowing the early detection

of potentially catastrophic faults. In condition based maintenance, one does not

schedule maintenance or machine replacement based on previous records or

statistical estimates of machine failure. Rather, one relies on the information

provided by condition monitoring systems assessing the machine's condition.

Thus the key for the success of condition based maintenance is having an

accurate means of condition assessment and fault diagnosis. On-line condition

monitoring uses measurements taken while a machine is in operating condition.

There are around 1.2 billion of electric motors used in the United States, which

consume about 57% of the generated electric power. Over 70% of the electrical

energy used by manufacturing and 90% in process industries are consumed by

motor driven systems.

Among these motor systems, squirrel-cage induction motors (SCIM) have

a dominant percentage because they are robust, easily installed, controlled, and

adaptable for many industrial applications. SCIM find applications in pumps,

fans, air compressors, and machine Tools, mixers, and conveyor belts, as well

as many other industrial applications. Moreover, induction motors may be

supplied directly from a constant frequency sinusoidal power supply or by an

a.c. variable frequency drive. Thus condition based maintenance is essential for

an induction motor.

It is estimated that about 38% of the induction motor failures are caused

by stator winding faults, 40% by bearing failures, 10% by rotor faults, and 12%

by miscellaneous faults. Bearing faults and stator winding faults contribute a

major portion to the induction motor failures. Though rotor faults appear less

significant than bearing faults, most of the bearing failures are caused by shaft

misalignment, rotor eccentricity, and other rotor related faults.

Besides, rotor faults can also result in excess heat, decreased efficiency,

reduced insulation life, and iron core damage. So detection of mechanical and

electrical faults are equally important in any electrical motor.

In the proposed method, vibration signals are obtained using piezo-

electric sensor and motor current signature analysis is performed using Hall

Effect sensor. The features of the signal are analyzed using wavelet packet

transform. Besides other signal processing techniques, wavelet packet transform

is preferred because it has certain advantages. Traditional signal processing

techniques like Fourier transform can perform only on stationary signals. Since

it is not well suited for non-stationary signals Short time Fourier transform

(STFT) is used. STFT uses a constant window function as a base to obtain the

frequency spectrum coefficients. The size of the window function cannot be

changed which led to the need for wavelet transform. Wavelet transform uses a

varying size window function as its base.

In wavelet transform low frequency signals are decomposed repeatedly to

obtain low frequency information. In wavelet transform the information about

high frequency signals are limited. In the proposed method, wavelet packet

transform decomposes both low frequency and high frequency information. It

can analyze both stationary and non-stationary signals.

There are many classifier models to effectively classify the faulty data from the

healthy one. They are:

Analytical model-based methods, Artificial Intelligence-based methods.

Analytical model based methods are efficient monitoring systems for

providing warning and predicting certain faults in their early stages. Artificial

Intelligence based methods are of two categories: Knowledge based models

and Data based models. When considering fault diagnostics of induction motor

it is difficult to develop an analytical model that describes the performance of a

motor under all its operation points. It is difficult for a human expert to

distinguish faults from the healthy operation. Though analytical based methods

and knowledge based methods are effective classification methods, their

performance in induction motors is not good. Moreover conventional methods

cannot be applied effectively for vibration signal diagnosis due to their lack of

adaptability and the random nature of vibration signal. In such a situation, data

based models are used to classify faults in induction

motors.

Data based models are applied when:

— the process model is not known in the analytical form

— expert knowledge of the process performance under faults is not available

Some of the popular data based models are neural networks, fuzzy systems and

Support vector machine. Neural networks and fuzzy logic are widely used in the

field of fault diagnostics. Fuzzy logic provides a systematic framework to

process vague and qualitative knowledge. Using fuzzy logic it is possible to

classify a fault in terms of its degree of severity.Artificial neural network are

modeled with artificial neurons. Each artificial neuron accepts several inputs,

applies preset weights to each input and generates a non-linear output based on

the result. The neurons are connected in layers between the inputs and outputs.

Support Vector Machine, a novel machine learning technique is used in this

paper. It is based on statistical learning theory, and is introduced during the

early 90’s. SVM is opted in this paper since it is shown to have better

generalization properties than traditional classifiers.

Efficiency of SVM does not depend on the number of features of

classified entities. property is very useful in fault diagnostics, because the

number of features to be chosen to be the base of fault classification is thus not

limited.

Industrial Motor’s condition monitoring systems collect data from the

main components such as the generator, the gearbox, the main bearing, and the

shaft. The purpose of this data-gathering is to minimize downtime and

maintenance costs while increasing energy availability and the lifetime service

of wind turbine components. An ideal condition monitoring system would

monitor all the components using a minimum number of sensors.

There have been a few literature reviews on Industrial Motor’s condition

monitoring. This chapter aims to review the most recent advances in condition

monitoring and fault diagnostic techniques with a focus on wind turbines and

their subsystems related to mechanical fault. This section summarizes the

monitoring and diagnostic methods for the major subsystems in Industrial

Motor’s such as gearbox, bearing, and generator which are the primary focus of

this study.

2.1.1 Gearbox and Bearing

Gearbox fault is widely acknowledged as the leading issue for wind

turbine drive train condition monitoring among all subsystems [11-19]. Gear

tooth damage and bearing faults are both common in the Industrial Motor’s.

According to McNiff [27], bearing failure is the leading factor in turbine

gearbox problems. In particular, it was pointed out that the gearbox bearings

tend to fail in different rates. Among all bearings in a planetary gearbox, the

planet bearings, the intermediate shaft-locating bearings, and the high-speed

locating bearings tend to fail at the fastest rate, while the planet carrier bearings,

hollow shaft bearings, and non-locating bearings are least likely to fail. This

study indicates that more detailed stress analysis of the gearbox is needed in

order to achieve a better understanding of the failure mechanism and load

distribution which would lead to improvement of drive train design and sensor

allocation. Vibration measurement and spectrum analysis are typical choices for

gearbox monitoring and diagnostics. For instance, Yang et al. developed a

neural network based diagnostic framework for gearboxes in [28]. The

relatively slow speed of the wind turbine sets a limitation in early fault

diagnosis using the vibration monitoring method. Therefore, acoustic emission

(AE) sensing, which detects the surface stress waves generated by the rubbing

action of failed components, has recently been considered a suitable

enhancement to the classic vibration based methods for multisensory

monitoring scheme for gearbox diagnosis, especially for early detection of

pitting, cracking, or other potential faults.

Lekou et al. presented their study using AE in parallel with vibration,

temperature, and rotating speed data for health monitoring [29]. It was shown

that monitored periodic statistics of AE data can be used as an indicator of

damage presence and damage severity in Industrial Motor’s.

Chen et al. set up a finite-element (FE) simulation study of stress wave

based diagnosis for the rolling element bearing of the wind turbine gearbox. It is

noteworthy that FE analysis is a good complementary tool to the experimental

based study, with which the physical insight of various levels of faults can be

investigated. Notice that AE measurement features very high frequencies

compared to other methods, so the cost of data acquisition systems with high

sampling rates needs to be considered. Besides, it is noise-rich information from

AE measurement. Advanced algorithms are needed to extract useful

information. For mechanical faults of the drive train, the electrical analysis was

investigated. Diagnosis of gear eccentricity was studied using current and power

signals. It is noteworthy that the data were obtained from a wind turbine

emulator, incorporating the properties of both natural wind and the turbine rotor

aerodynamic behavior. Although the level of turbulence simulated was not

described, the demonstrated performance was still promising for practical

applications. Torque measurement has also been utilized for drive train fault

detection. The rotor faults may cause either a torsional oscillation or a shift in

the torque-speed ratio. Also, shaft torque has a potential to be used as an

indicator for decoupling the fault-like perturbations due to higher load.

However, inline torque sensors are usually expensive and difficult to install.

Therefore, using torque measurement for drive train fault diagnosis and

condition monitoring is still not practically feasible.

2.1.2 Generators

The Industrial generators are also subject to failures in bearing, stator, and

rotor among others components. For induction machines, about 40% of

failures are related to bearings, 38% to the stator, and 10% to the rotor. The

major faults in induction machine stators and rotors include inter-turn faults in

the opening or shorting of one or more circuits of a stator or rotor winding,

abnormal connection of the stator winding, dynamic eccentricity, broken rotor

bars of cracked end-rings for cage rotor, static and/or dynamic air-gap

eccentricities, among others. Faults in induction machines may produce some of

the following phenomena: unbalances and harmonics in the air-gap flux and

phase currents, increased torque oscillation, decreased average torque, increased

losses and reduction in efficiency, and excessive heating in the winding.

2.1.3 Machine Vibration Analysis

Vibration analysis is a proven and effective technology being used in

condition monitoring. For the measurement of vibration, different vibration

transducers are applied, according to the frequency range. Vibration

measurement is commonly done in the gearbox, turbines, bearings, and shaft.

For wind turbine application, the measurement is usually done at critical

locations where the load condition is at maximum, for example, wheels and

bearings of the gearbox, the main shaft of turbine, and bearings of the generator.

Different types of sensors are employed for the measurement of vibration:

acceleration sensors, velocity sensors, and displacement sensors. Different

vibration frequencies in a rotation machine are directly correlated to the

structure, geometry, and speed of the machine. By determining the relation

between types of defects and their characteristic frequencies, the causality of

problems can be determined, and the remaining useful life of components can

be estimated. The history of the equipment, its failure statistic, vibration trend,

and degradation pattern are of vital importance in determining the health of the

system and its future operating condition. Using vibration analysis, the presence

of a failure, or even an upcoming failure, can be detected because of the

increase or modification in vibrations of industrial equipment. Since an analysis

of vibrations is a powerful tool for the diagnosis of equipment, a number of

different techniques have been developed. There are methods that only

distinguish failures at a final state of evolution and there are others, more

complex, that identify defects at an early phase of development.

2.2 Review Conclusions

To achieve an accurate and reliable condition monitoring system for wind

turbines, it is necessary to select measurable parameters as well as to choose

suitable signal processing methods. In some examples, electrical sensors

installed around the generator are highly recommended as they are non-invasive

and easy to implement compared to the mechanical ones. In wind turbines,

because of the noisy environment due to the presence of power electronics

converters, signal to noise ratio of measured signals is low and the usage of

electrical parameters are often more problematic than in a lab environment.

Inaccurate signal analysis leads to various false alarms which makes fault

detection unreliable. To overcome this drawback, several approaches have been

proposed by introducing the vibration measurement and using vibrations as an

index for detecting mechanical fault in the system. However, those methods

have been applied mostly for drive train failure, bearing faults, and gear tooth

damage by using acoustic emission (AE) techniques for detection. Therefore, to

enhance the effectiveness and thorough of condition-based predictive

maintenance, dissertation proposes a vibration based monitoring system for

rotor imbalance conditions.

3.1 HARDWARE IMPLEMETATION

3.1.1 ADXL330 MEMS Accelerometer

Figure 3.1 MEMS Accelerometer

The ADXL330 is a complete three-axis acceleration measurement

system on a single monolithic IC. The ADXL330 has a measurement

range of ±6g.The block diagram is illustrated in Figure 3.2. It contains a

micro-machined sensor and signal conditioning circuit to implement the

open loop acceleration measurement architecture. The output signals are

analog voltages that are proportional to acceleration. The accelerometer

can measure the static acceleration of gravity in tilt sensing applications

as well as dynamic acceleration resulting from motion, shock, or

vibration. Deflection of the structure is measured using a differential

capacitor that consists of independent fixed plates and plates attached to

the moving mass. The fixed plates are driven by 180° out-of-phase square

waves. Acceleration deflects the moving mass and unbalances the

differential capacitor resulting in a sensor output whose amplitude is

proportional to acceleration. Phase-sensitive demodulation techniques are

then used to determine the magnitude and direction of the acceleration.

Figure 3.2 Block Diagram of ADXL330 MEMS Accelerometer

The demodulator output is amplified and brought off-chip through

a 32kΩ resistor. The user then sets the signal bandwidth of the device by

adding a capacitor. This filtering improves measurement resolution and

helps prevent aliasing. The user selects the bandwidth of the

accelerometer using the CX, CY, and CZ capacitors at the XOUT,

YOUT, and ZOUT pins. Bandwidths can be selected to suit the

application, with a range of 0.5 Hz to 1600 Hz for X and Y axes, and a

range of 0.5 Hz to 550 Hz for the Z axis.

3.1.1.1 Features

3-axis sensing

Small, low-profile package : 4 mm × 4 mm × 1.45 mm

LFCSP

Low power: 180μA at VS = 1.8 V (typical)

Single-supply operation: 1.8 V to 3.6 V

10,000 g shock survival

Excellent temperature stability

BW adjustment with a single capacitor per axis

RoHS/WEEE lead-free complian

3.1.1.2 Pin configuration

Figure 3.3 Pin Configuration

Table 3.1 Pin function Description of ADXL330 Tri-axial Accelerometer

Pin no. Mnemonic Description Pin no. Mnemonic Description

1. NC No connect 9. NC No Connect

2. ST Self test 10. YOUT Y Channel Output

3. COM Common 11. NC No Connect

4. NC No connect 12. XOUT X Channel Output

5. COM Common 13. NC No Connect

6. COM Common 14. VS Supply Voltage (1.8V to 3.6V)

7. COM Common 15. VS Supply Voltage (1.8V to 3.6V)

8. ZOUT Z Channel Out 16. NC No Connect

Figure 3.4 ADXL330 Tri-axial Accelerometer Mounted on the Motor Housing

The wireless sensor network is implemented and an accelerometer

is also integrated in this monitoring system for detecting the vibration

signals. Vibration signals were collected using ADXL330 tri-axial

accelerometer mounted on the motor housing in Figure 3.3

All three-axis vibration signals are acquired at a sampling rate of

2kHz by a 12-bit ADC conversion.

3.1.1.3 Applications

Cost-sensitive, low power, motion- and tilt-sensing

applications

Mobile devices

Gaming systems

Disk drive protection

Image stabilization

Sports and health devices

3.1.1.4 Advantages

Apart from the significant cost saving over traditional force-

balance accelerometers, due to the nature of their design micro-

electromechanical systems sensors have a much better high frequency

response. Where most earthquake accelerometers are specified as having

a frequency response of DC to 50Hz, 100Hz or in some cases 200Hz, the

seismic-oriented MEMS sensors have a much higher frequency range.

For example, the Silicon Designs units used in the ESS-1221 sensor have

a frequency response of DC to 400Hz, and the Colibrys SF3000L MEMS

sensors extend to 1000Hz.

Frequency response is important when recording strong motion,

particularly for events at close range where high frequencies have not

been attenuated with distance. In blast monitoring, where the source can

be only dozens of metres away from the sensor, frequencies of 5000Hz

or more can be recorded, so it is possible that large, nearby earthquakes

could achieve their peak accelerations in frequencies above 200Hz.

Earthquake recorders typically record data at 100sps and 200sps, meaning

that frequencies above 50Hz or 100Hz are not recorded. More can be

learnt about earthquakes by using MEMS accelerometers and recorders

capable of sampling at up to 2000sps.

Condition monitoring and fault diagnosis of induction motors are

of great importance in production lines. It can significantly reduce the

cost of maintenance and the risk of unexpected failures by allowing the

early detection of potentially catastrophic faults. In condition based

maintenance, one does not schedule maintenance or machine replacement

based on previous records or statistical estimates of machine failure.

Rather, one relies on the information provided by condition monitoring

systems assessing the machine's condition.

Thus the key for the success of condition based maintenance is

having an accurate means of condition assessment and fault diagnosis.

On-line condition monitoring uses measurements taken while a machine

is in operating condition.

There are around 1.2 billion of electric motors used in the United

States, which consume about 57% of the generated electric power. Over

70% of the electrical energy used by manufacturing and 90% in process

industries are consumed by motor driven systems.

Among these motor systems, squirrel-cage induction motors

(SCIM) have a dominant percentage because they are robust, easily

installed, controlled, and adaptable for many industrial applications.

SCIM find applications in pumps, fans, air compressors, machine tools,

mixers, and conveyor belts, as well as many other industrial applications.

Moreover, induction motors may be supplied directly from a constant

frequency sinusoidal power supply or by an a.c. variable frequency drive.

Thus condition based maintenance is essential for an induction motor.

It is estimated that about 38% of the induction motor failures are

caused by stator winding faults, 40% by bearing failures, 10% by rotor

faults, and 12% by miscellaneous faults. Bearing faults and stator

winding faults contribute a major portion to the induction motor failures.

Though rotor faults appear less significant than bearing faults, most of the

bearing failures are caused by shaft misalignment, rotor eccentricity, and

other rotor related faults. Besides, rotor faults can also result in excess

heat, decreased efficiency, reduced insulation life, and iron core damage.

So detection of mechanical and electrical faults are equally important in

any electrical motor.

In the proposed method, vibration signals are obtained using piezo-

electric sensor and motor current signature analysis is performed using

Hall Effect sensor. The features of the signal are analyzed using wavelet

packet transform. Besides other signal processing techniques, wavelet

packet transform is preferred because it has certain advantages.

Traditional signal processing techniques like Fourier transform can

perform only on stationary signals. Since it is not well suited for non-

stationary signals short time Fourier transform (STFT) is used. STFT uses

a constant window function as a base to obtain the frequency spectrum

coefficients. The size of the window function cannot be changed which

led to the need for wavelet transform. Wavelet transform uses a varying

size window function as its base. In wavelet transform low frequency

signals are decomposed repeatedly to obtain low frequency information.

In wavelet transform the information about high frequency signals are

limited. In the proposed method, wavelet packet transform decomposes

both low frequency and high frequency information. It can analyze both

stationary and non-stationary signals. There are many classifier models to

effectively classify the faulty data from the healthy one. They are:

Analytical model-based methods, Artificial Intelligence-based methods.

analytical model based methods are efficient monitoring systems for

providing warning and predicting certain faults in their early stages.

Artificial Intelligence based methods are of two categories: Knowledge

based models and Data based models. When considering fault diagnostics

of induction motor it is difficult to develop an analytical model that

describes the performance of a motor under all its operation points. It is

difficult for a human expert to distinguish faults from the healthy

operation. Though analytical based methods and knowledge based

methods are effective classification methods, their performance in

induction motors is not good. Moreover conventional methods cannot be

applied effectively for vibration signal diagnosis due to their lack of

adaptability and the random nature of vibration signal. In such a situation,

data based models are used to classify faults in induction motors.

Data based models are applied when:

— the process model is not known in the analytical form

— expert knowledge of the process performance under faults is not

available

Some of the popular data based models are neural networks, fuzzy

systems and Support vector machine. Neural networks and fuzzy logic

are widely used in the field of fault diagnostics. Fuzzy logic provides a

systematic framework to process vague and qualitative knowledge.

Using fuzzy logic it is possible to classify a fault in terms of its degree

of severity. Artificial neural network are modeled with artificial neurons.

Each artificial neuron accepts several inputs, applies preset weights to

each input and generates a non-linear output based on the result. The

neurons are connected in layers between the inputs and outputs.

Support Vector Machine, a novel machine learning technique is

used in this paper. It is based on statistical learning theory, and is

introduced during the early 90’s. SVM is opted in this paper since it is

shown to have better generalization properties than traditional

classifiers. Efficiency of SVM does not depend on the number of

features of classified entities. property is very useful in fault diagnostics,

because the number of features to be chosen to be the base of fault

classification is thus not limited.

CONCLUSION

One of the most serious problems in Industrial Motor’s is the

possibility of mechanical failure, specially for rotating parts of gears and

generators. Therefore, a machine health monitoring system is a very

important tool in Industrial Motor’s. Moreover, wireless sensor

technologies make it possible to measure and control the vibrations of the

machine during operation. The methods of mechanical fault detection

through vibration analysis have been analyzed and assessed based on

their ability to detect machine abnormalities. By using an MEMS

accelerometer which is low cost, light in weight, compact in size and low

in power consumption, a vibration detection method is proposed in this

dissertation. Machine vibration analysis in time and frequency domain

has been analyzed and a severity detection technique is also established.

These are the essential components for an advance health monitoring

system. The implementation of mechanical fault monitoring system can

be used to estimate the range of severity levels, which makes it possible

to detect the abnormalities before failure. It is very useful part of the

condition based predictive maintenance.

This control technique works well both under the normal and

disturbance operation. This enhancement of the vibration suppression

capabilities opens up the possibility of improving the performance of the

windmill. This will greatly improve the power quality and reduce the

downtime when there is wear and tear on the mechanical components,

such as shaft, gear box, and rotating parts.

Dissertation Contributions

This study presents a excellent health monitoring for Industrial

Motor’s systems to detect the severity level of mechanical fault

conditions. Moreover, a new 3 axis sensor is proposed to monitor the

wind turbine output during imbalance conditions. The major

contributions of this dissertation are:

Develop and implement a MEMS based wireless sensor network for a

health monitoring which will be able to detect mechanical fault

conditions based on the vibration signature.

Propose and validate vibration based detection techniques to predict the

level of fault severity and be able to estimate the usable life of the

equipment.

To reduce the downtime and manual procedures by continues monitoring

of wind turbine operations.

Developing a new predictive monitoring system instead of protective

system to ensure the Good condition of wind turbines.

To develop a wireless monitoring system to overcome the drawbacks of

wired networks.

The overall cost of the monitoring system gets reduced due to no usage of

wired conductors for signal transmission.

The low cost microcontrollers and low power consumption equipments

are used.