experimental investigations on induction machine...

Experimental investigations on induction machine conditionmonitoring and fault diagnosis using digital signal

processing techniques

Sa’ad Ahmed Saleh Al Kazzaz a, G.K. Singh b,*a Department of Electrical Engineering, University of Mosul, Mosul, Iraq

b Department of Electrical Engineering, Indian Institute of Technology, Roorkee 247667, India

Received 15 February 2002; received in revised form 7 November 2002; accepted 25 November 2002

Abstract

Condition monitoring is used for increasing machinery availability and machinery performance, reducing consequential damage,

increasing machine life, reducing spare parts inventories, and reducing breakdown maintenance. An efficient condition monitoring

scheme is capable of providing warning and predicting the faults at early stages. The monitoring system obtains information about

the machine in the form of primary data and through the use of modern signal processing techniques; it is possible to give vital

information to equipment operator before it catastrophically fails. The suitability of a signal processing technique to be used

depends upon the nature of the signal and the required accuracy of the obtained information. Therefore, in this paper, signals

obtained from the monitoring system are treated with different processing techniques with suitably modified algorithms to extract

detailed information for machine health diagnosis. In this study, on-line analysis of the acquired signals has been performed using

C��, while MATLAB has been used to perform the off-line analysis.

# 2002 Elsevier Science B.V. All rights reserved.

Keywords: Induction machine; Fault; Condition monitoring; Diagnostic; Digital signal processing; Fourier transform

1. Introduction

Predictive maintenance by vibration monitoring of

electrical machine is a scientific approach that becomes

the new route to the maintenance management [1�/4].

Electrical machines, even new ones, generate some level

of vibration [5�/17]. Small levels of ambient vibrations

are acceptable. However, higher levels and increasing

trends are symptoms of abnormal machine perfor-

mance. Machine vibration analysis becomes one of the

important tools for machine faults identification. There

are two types of analysis, time domain and frequency

domain. The frequency domain analysis is more attrac-

tive one because it can give more detailed information

about the status of the machine whereas; the time

domain analysis can give qualitative information about

the machine condition. Generally, machine vibration

signal is composed of three parts, stationary vibration,

random vibration, and noise. Traditionally, Fourier

transform (FT) was used to perform such analysis. If

the level of random vibrations and the noise are high,

inaccurate information about the machine condition is

obtained. Noise and random vibrations may be sup-

pressed from the vibration signal using signal processing

techniques such as filtering, averaging, correlation,

convolution, etc. Sometimes random vibrations are

also important because they are related to some types

of machine faults hence; there is a need to observe these

vibrations also.

Signals obtained from the transducers are in the form

of continuous voltage or current signals. It is necessary

to define their values at certain instants of time to be

suitable for digital signal processing (DSP) applications.

The obtained digital signal is an adequate substitute for

the underlying continuous signal if the interval between

the successive samples is sufficiently small. The sampling

frequency must be twice the highest frequency compo-* Corresponding author. Fax: �/91-1332-73560.

E-mail address: [email protected] (G.K. Singh).

Electric Power Systems Research 65 (2003) 197�/221

www.elsevier.com/locate/epsr

0378-7796/03/$ - see front matter # 2002 Elsevier Science B.V. All rights reserved.

doi:10.1016/S0378-7796(02)00227-4

mailto:[email protected]

nents of the signal (according to Shannon’s theorem) to

avoid aliasing of high frequencies components in the low

frequency region of the spectrum. In the present work,

the sampling frequency has been selected to be fourtimes the highest frequency component of the signal to

prevent any possibility of aliasing and to ensure the

complete reconstruction of the signal.

In this paper, signals obtained from monitoring

system as shown in Fig. 1, are treated with different

processing techniques with suitably modified algorithms

to extract detailed information for induction machine

diagnosis. All the techniques used here for signalanalysis and processing have been implemented in

C�� and MATLAB software. On-line analysis of the

acquired signals is performed using C��, while MATLAB

is used to perform the off-line analysis.

2. Nature of electrical machine faults

The induction motor is considered as a robust and

fault tolerant machine and is a popular choice in

industrial drives. It is important that the measures are

taken to diagnose the state of the machine as and when

it enters into the fault mode. It is further necessary to do

so on-line by continuously monitoring the machine

variables. The reasons behind failures in rotatingelectrical machines have their origin in design, manu-

facturing tolerance, assembly, installation, working

environment, nature of load and schedule of mainte-

nance. Induction motor like other rotating electrical

machine is subjected to both electromagnetic and

mechanical forces. The design of motor is such that

the interaction between these forces under normal

condition leads to a stable operation with minimumnoise and vibrations. When the fault takes place, the

equilibrium between these forces is lost leading to

further enhancement of the fault.

The motor faults can be categorised into two types:

mechanical and electrical. The sources of motor faults

may be internal, external or due to environmental, as

presented in Fig. 2. Internal faults can be classified withreference to their origin i.e. electrical and mechanical or

to their location i.e. stator and rotor. Usually, other

types of fault i.e. bearing and cooling faults refer to the

rotor faults because they belong to the moving parts.

Fig. 3 presents the fault tree of induction machine where

the faults are classified according their location: rotor

and stator.

3. Simulation of induction machine under healthy and

fault conditions

Modelling and simulation of electrical machine dy-

namics has attracted many researchers since the early

days of electrical machine invention [18]. The fastadvances in computing facilities and the improvement

in numerical techniques have lead to improvement in

accuracy and simulation efficiency. Mathematical mod-

els have been developed to include the effect of core loss,

saturation effect, winding distribution, and inherent

machine faults [18�/24].

3.1. Dynamic analysis of induction motor

For simulation of dynamic state, the choice of model

is made on the basis of operating conditions as follows:

. Machine operating from balanced sinusoidal supply

under nominal voltages, and under/over voltages

(phase variable model).

. Machine operating from balanced non-sinusoidal

supply obtained from inverter (stationary referenceframe model).

. Machine operating from unbalanced sinusoidal sup-

ply (instantaneous symmetrical component model).

Fig. 1. Schematic diagram of the monitoring system.

S.A.S. Al Kazzaz, G.K. Singh / Electric Power Systems Research 65 (2003) 197�/221198

Fig. 2. Sources of induction machine faults.

Fig. 3. Popular induction machine faults and their causes.

S.A.S. Al Kazzaz, G.K. Singh / Electric Power Systems Research 65 (2003) 197�/221 199

. Machines operating from acquired (recorded voltages

from monitoring system) supply voltages (phase

variable model).

3.1.1. Various test conditions used for simulation purpose

In this work, three phase 5 hp induction motors were

chosen for the simulation purpose because the samemachines are used in the practical investigations. These

machines were simulated (dynamic simulation) for

different test conditions, which are as follows:

. Transient performance of induction motor under

nominal supply.


unbalanced (including single phasing) supply.

. Transient performance of induction motor with

under-voltage and over-voltage supply.

. Transient performance of induction motor undervariable frequency sinusoidal supply.


variable frequency non-sinusoidal supply.


mechanical faults (rotor eccentricity, dry bearing,

and faulty bearing due to ball defects).

3.2. Induction motor health identification based on on-line

machine modelling

The developed monitoring system (Fig. 1) comprisesof three transformers for voltage measurement, three

Hall-Effect probes for current measurements, pulse-

tachometer for speed measurement and four thermo-

couples for temperature measurements. Signal condi-

tioners are used for thermocouples to provide

linearisation, amplifications and cold junction compen-

sation, and for vibration transducer to provide excita-

tion, filtering and amplifications. The data is transferredto the computer using 12-bit A/D converter that

provides less than 0.1% error in amplitude of the

acquired signal. The sampling frequency has been

selected so that complete reconstruction of the signals

can be achieved. The sampling frequency is adjusted to 2

kHz for electrical variables, and 4 kHz for vibration

signals. The speed transducer output is in order of 1000

pulse per revolution. To obtain the motor speed, thesepulses are counted for a specific period of time (200 ms)

using an on-board 16-bit down counter. The system

accuracy and performance is tested with the known

inputs.

The hardware along with the software allows the users

to effectively monitor, store, and analyse machine

variables. The system provides on-line display of

voltages, currents, and temperatures (using C�� gra-

phic facility) with simple data analysis or directly storing

the acquired data for off-line data analysis and proces-

sing. A sampling frequency of the order 15 kHz is

achieved with the on-line display of the acquired data,

and about 50 kHz with the direct storing of the acquired

data. Moreover, the change in sampling frequency;

number of samples, range of display and number ofmachine variables is possible.

The machine health identification can be obtained

with the aid of the on-line monitoring system discussed

above. In this system, three phase currents; three phase

voltages and speed are recorded on-line and stored in

computer memory. The recorded three phase voltages

are fed to the developed machine model in order to

calculate the machine currents and to predict themachine conditions. By comparing the actual recorded

machine currents (recorded simultaneously with ma-

chine voltage) with the simulated currents, the machine

conditions can be obtained qualitatively. One of the

effective methods, which have been adopted recently to

predict machine condition using machine currents, is

Park’s vector approach [25]. Here this method is

employed to obtain the machine behaviour due tovarious supply conditions.

4. Signal analysis

Signal analysis is used to extract some useful features

of the signal i.e. mean value, mean square, root mean

square (RMS) value and the crest factor. The signal

detectors have been implemented by software using

simple algorithms.

4.1. Implementation of root mean square

In this study, the RMS value of the vibration signal is

used for primary investigation of the machine health.

The RMS values of the machine voltages and currents

are used to detect the unbalanced supply conditions, and

to differentiate its effect from the effect of the other

types of faults. Table 1 represents the RMS values of the

machine voltages and currents and the average speed for

five identical machines running under same operatingconditions. These values are used as input to the neural

network based fault classifier. Table 2 represents the

RMS values of radial and axial machine vibrations for

five identical machines running under different condi-

tions.

4.2. Implementation of Crest factor

The Crest factor is the ratio of the peak value to the

RMS value. It is meaningful where the peak values are

reasonably uniform and repeatable from one signal cycleto another. The Crest factor yields a measure of the

spikiness of a signal and is used to characterise signals

containing repetitive impulses in addition to a lower


level continuous signal. Crest factor is often used to

indicate the rolling element bearing faults.

It may be noticed that both radial and axial vibrations

are affected by the machine condition, but it is difficultto recognise the type of the fault by using these values.

These values can give qualitative information about the

machine condition if it is compared with the vibration

standard. The values of Crest factor of the vibration

signal for healthy and faulty five symmetrical induction

machines running at no load are represented in Table 3.

The bearing faults clearly affect the value of the Crest

factor but the change in Crest factor is small with drybearing fault. Continuous monitoring of this factor

along with historical records can provides useful in-

formation about the bearing condition. From the above

tables, it may be noticed that some of the machine

conditions have a significant effect on the RMS value of

the vibration while the others do not, and the same is

true with the Crest factor. However, using both the

RMS value and the Crest factor for machine healthidentification may increase the system efficiency for

diagnostics.

5. Signal processing techniques

There are two approaches used for processing the

signal; time domain and frequency domain. In time

domain approach, the discrete time signal is directly

analysed by one of the DSP techniques [26�/29] such asfiltering [30,31], averaging [2], convolution, correlation

etc. In frequency domain approach, the signal is first

transformed to the frequency domain using FT then,

different methods of analysis such as averaging, con-

volution, power spectrum, cepestrum etc. can be ap-

plied. Different signal processing approaches have been

used here to extract the salient features of the signalsobtained from the machine.

6. Implementations of signal processing techniques in

time domain

Different aspects are available for time domain

analysis such as; time period of the signal, the peak

value reached by the signal, the average value of the

signal, RMS value of the signal etc. The choice of such

approaches depends on the nature of the signal and the

required information. In this section, some of the DSP

techniques are introduced with their implementationswith the data of vibration and electrical variables.

6.1. Implementation of signal averaging

Time domain averaging is effective in suppressingsignals that are not correlated within the averaging

period. To get good results, it is necessary to know the

repetition frequency precisely and sampling the signal

with an integer number of samples per period. Noise

reduction goes as 1=ffiffiffiffiffi

Np

; greatly increasing the signal to

noise ratio. In machinery diagnostics using the vibration

signal, this approach has some serious drawbacks.

The underlying assumption is that the shaft rotationrate is constant. This is not the case in rotating machines

where there is variability in the shaft speed, which

broaden the spectral peaks and gives poor results. This

Table 1

RMS value of machine voltages, currents and average speed

V12 (V) V23 (V) V31 (V) I1 (A) I2 (A) I3 (A) N (rpm)

Machine 1 417.324 415.566 418.940 4.222 4.176 4.156 1473

Machine 2 413.450 412.053 413.618 4.112 4.141 4.162 1481

Machine 3 419.968 417.243 421.083 4.297 4.247 4.285 1479

Machine 4 416.826 417.940 420.556 4.364 4.314 4.388 1470

Machine 5 418.431 418.563 416.910 4.100 4.117 4.090 1485

Table 2

RMS value of radial and axial vibration for different machine conditions

RMS vibration (radial) (g) RMS vibration (axial) (g)

M/C 1 M/C 2 M/C 3 M/C 4 M/C 5 M/C 1 M/C 2 M/C 3 M/C 4 M/C 5

Healthy condition 0.024 0.025 0.021 0.025 0.031 0.023 0.033 0.032 0.035 0.040

Unbalanced supply 0.054 0.046 0.050 0.043 0.055 0.103 0.115 0.111 0.099 0.112

Single phasing 0.275 0.275 0.278 0.287 0.285 0.239 0.281 0.266 0.248 0.241

Mechanical unbalanced 0.042 0.044 0.038 0.047 0.049 0.043 0.046 0.042 0.051 0.055

Faulty bearing (dry) 0.023 0.025 0.024 0.023 0.026 0.034 0.043 0.045 0.034 0.042

Faulty bearing (ball defect) 0.137 0.124 0.150 0.118 0.168 0.149 0.136 0.138 0.134 0.154


approach assumes that the shaft rate is known with agood deal of precision. Some measurement error in the

shaft rate that serves to mistune the comb filter giving

poor results is always expected. Hence, it is required that

the data acquisition system should have adaptable

sampling rate according to the current shaft rate. This

requirement is difficult to meet in practice. Some of

these problems have been previously addressed and

various solutions have been proposed. One solution isto synchronise the vibration signal with a tachometer

signal, where the tachometer signal marks the beginning

of each averaging period. The data is then, ensemble

averaged over the periods marked by the beginning of

the tachometer pulse. This approach gets around the

problems associated with the ability to adaptively

change the sampling rate. Another alternative is to

take a single vibration measurement at an arbitrarysampling rate and do irregular resampling to interpolate

the signal.

For voltage and current signals, this approach is

successfully implemented to clean up the signal from

noise. The reference point is precisely specified and an

average of 18 runs gave good results. Fig. 4 shows the

voltage waveforms of different runs and their average

where the improvement in the signal can be observedclearly. Then the obtained signal is transformed to the

frequency domain and treated with different signal

processing and analysis techniques.

6.2. Implementation of correlation

Correlation between two signals is used to obtain the

similarity between them. Correlation function of the

baseline and real time signals is used to investigate theirrelationship. Vibration, current and voltage signals of a

healthy machine are considered as baseline signals. For

primary investigation, two segments of a long vibration

signal are correlated to obtain their similarity, as shown

in Fig. 5. The correlation function of the vibration

signals obtained from different runs with the same

conditions is shown in Fig. 6. The figure shows that

the correlation among them is high. The correlationbetween a baseline vibration signal and a faulty bearing

vibration signal is shown in Fig. 7. It can be concluded

that even the vibration signals obtained from the same

machine running at the same condition, the correlationamong them is not very high. This is due to the presence

of noise and random vibrations. The information

obtained from the correlation function play an impor-

tant role in selecting the input data of the neural

network. For electrical quantities such as voltages and

currents, better results are obtained by implementing the

correlation function. Fig. 8 shows a couple of current

signal and a couple of voltage signal obtained from thesame machine under healthy and faulty conditions and

the corresponding correlation functions.

6.3. Implementation of signal filtering

Filters are used for two purposes, to attenuate the

noise and undesired frequency components and to

separate some individual frequencies or band of fre-

quencies for their relation with the machine faults. TheRMS value of selected frequency components or band

of frequencies is used to obtain machine condition by

comparing the obtained values with the corresponding

reference values. As it is mentioned above, the filter

characteristic plays the key role of obtaining the

required resolution and accuracy of the analysis. For

example, it is desired to pick up a frequency component

related to a certain type of machine fault which is closeto a dominant frequency component, i.e. supply fre-

quency of 50 Hz and double rotational speed 48.5 Hz

(for rotor speed�/1455 rpm). Fig. 9 shows the vibration

signal before and after using smoothing filter for

removing the high frequency noise from the signal.

Due to the fluctuations in the vibration signal, bands of

frequencies rather than individual frequency compo-

nents are used to identify the machine conditions. Theband pass filter that met the above-mentioned require-

ments is achieved using FIR filter.

Fig. 10 presents the results of band pass filtering of

three frequency regions of the vibration signal. For the

purpose of diagnosis, variable tuned band pass filter is

used to separate four frequency regions related to some

common types of machine fault. The filter is first tuned

to pass the frequency band of 1�/200 Hz, this band isrelated to the first and second harmonics of the bearing

characteristic frequencies and shaft frequency, which is

related to the mechanical unbalance. A narrow band

Table 3

Crest factor of radial vibration for different machine conditions

Machine 1 Machine 2 Machine 3 Machine 4 Machine 5

Healthy condition 0.7487 0.6442 0.5993 0.6755 0.8143

Unbalanced supply 0.6792 0.7451 0.6598 0.7027 0.8569

Single phasing 0.9914 0.9813 0.9380 0.9017 0.9540

Mechanical unbalanced 0.4678 0.6519 0.5406 0.5581 0.7276

Faulty bearing (dry) 0.8802 0.8536 0.7677 0.7844 0.9028

Faulty bearing (ball defect) 1.1275 1.0579 1.0231 1.1282 1.3382


Fig. 4. Sample of machine terminal voltage with average of 18 runs.

Fig. 5. (a and b) Two segments of radial vibration signal; (c) correlation among them.


filter is tuned to pass a frequency band of 2f9/4 Hz (f�/

supply frequency) i.e. 96�/104 Hz for 50 Hz supply

frequency. This band indicates the supply conditions i.e.

unbalanced supply, turn-to-turn short, and single phas-

ing. The third frequency band is selected by the filter to

pass a frequency band of 220�/400 Hz, which covers the

high order harmonics of bearing characteristic fre-

quency. The filter is then tuned to pass a frequency

Fig. 6. (a and b) Two vibration signal obtained from healthy machine; (c) correlation among them.

Fig. 7. (a and b) Two vibration signal obtained from healthy and faulty machine; (c) correlation among them.


Fig. 8. (a and b) Terminal voltages of healthy and faulty machine; (c) correlation among them. (d and e) Line currents of healthy and faulty machine;

(f) correlation among them.

Fig. 9. (a) Vibration signal of faulty machine; (b) filtered version.


band of 550�/950 Hz, this band is related to the

vibration of electromagnetic origin i.e. rotor and stator

slot harmonics. Later to filtering the mentioned bands,

RMS values of these bands are calculated and then used

in the diagnostic algorithm. Table 4 presents the RMS

values of the selected bands for different machine

conditions. It can be observed from the Table 4 that

the RMS value of some frequency bands is affected with

the supply condition while the others have relations with

machine conditions. The effect of changing the operat-

ing frequency from 25 to 50 Hz in steps of 5 Hz on the

RMS values of these frequency bands for healthy and

faulty machines is also included and presented in Table

5. The change in the supply frequency leads to change in

the machine speed, so far all speed dependent frequency

harmonics and supply frequency dependent harmonics

change their location in the spectrum. The RMS value

of different frequency bands change with changing the

supply frequency. For small frequency band such as thesecond band presented in Table 5, the change in supply

frequency must be considered for correct use of the

RMS value for diagnosis purpose.

7. Implementation of signal processing technique in

frequency domain

Frequency analysis of a signal highlights many

important hidden features and extracts some useful

information. The accuracy of information extraction

depends upon the nature of the signal and the method of

analysis. In the present work, FTs and short term FT

Fig. 10. (a) Vibration signal and filtered version; (b) (10�/200 Hz) band pass; (c) (98�/102 Hz) band pass; (d) (680�/850 Hz) band pass.

Table 4

RMS value (g) of selected frequency bands of radial vibration

Healthy condition Unbalanced supply Single phasing Mechanical unbalanced Faulty bearing (dry) Faulty bearing (ball defect)

1�/200 Hz 0.02966 0.06506 0.25115 0.01343 0.01582 0.18849

96�/104 Hz 0.00770 0.08155 0.32252 0.01607 0.03263 0.08412

220�/400 Hz 0.01617 0.01486 0.03942 0.01293 0.02255 0.13093

550�/950 Hz 0.00204 0.00250 0.00993 0.00176 0.00300 0.02211


(STFT) are used to analyse the vibration, current, and

voltage signal in frequency domain.

7.1. Implementation of spectrum averaging

The implementation of signal averaging for vibration

signal in time domain has serious drawbacks due to the

instability of the signal and the difficulties in getting a

reference point. However, spectrum averaging (or scan

spectrum averaging) is used to obtain the averaging of a

long signal in frequency domain with adequate results.

The long signal is divided into number of equal length

segments and then the spectrum of each segment is

obtained. The average spectrum is then obtained by

adding all the spectrums and dividing by the number of

segments. Fig. 11 shows the vibration spectrum of three

segments and the average spectrum of 35 segments. It is

noticed that the effect of random vibrations and noise

are eliminated in the average spectrum. The average

spectrum is considered for the implementation of

harmonics analysis method. Scan spectrum averaging

method is also applied for vibration signals obtained by

repeating the same experiment for number of times at

same conditions. Fig. 12 shows the vibration spectrum

of three runs and the average spectrum of nine runs of

an induction machine running under same conditions.

Table 5

Effect of varying the supply frequency on the RMS value (g) of selected frequency bands of the vibration signals

Supply frequency (Hz) Healthy machine Faulty machine

1�/200 Hz 96�/104 Hz 220�/400 Hz 550�/950 Hz 1�/200 Hz 96�/104 Hz 220�/400 Hz 550�/950 Hz

25 0.01003 0.05000 0.05345 0.00667 0.19683 0.06191 0.05210 0.00816

30 0.01228 0.08660 0.09856 0.00816 0.24361 0.11475 0.04855 0.00943

35 0.03751 0.09292 0.07221 0.00816 0.36574 0.14478 0.09103 0.01700

40 0.01228 0.06892 0.10690 0.00943 0.25539 0.14634 0.06655 0.01333

45 0.01418 0.10488 0.12101 0.01247 0.10959 0.14732 0.09449 0.01563

50 0.01585 0.10042 0.13283 0.01700 0.18849 0.10412 0.13093 0.02211

Fig. 11. (a, b and c) Spectrums of a windowed long vibration signal; (d) average of 35 spectrums.


This approach has one drawback that is the effect of

window transitions. However, due to the finite length of

the window function, the ends of the convoluted

vibration signal may be distorted. Overlapping between

the windows may reduce this effect.

7.2. Implementation of correlation

Correlation in frequency domain can be achieved by

direct multiplication of the two spectrums after taking

the conjugate of one of them. The spectrum of the same

signals used in the implementation of the correlation in

time domain is used here as in the Figs. 13�/16. It can be

noticed that the results of correlating two vibration

signals in frequency domain give more informationabout the similarity of the two spectrums, in comparison

with same signals in time domain. Although, implement-

ing correlation with vibration signal does not give

adequate results due to change in the signal spectrum.

For current and voltage signals correlation coefficient

are more beneficial.

7.3. Implementation of signal filtering

Signal filtering in frequency domain is much easier to

implement than in time domain. It is simply achieved by

multiplying the spectrum of the signal with that of the

rectangular window. The width of the window is equal

to the bandwidth of the filter, while the centre frequency

of the window specifies the location of the filter in the

band. One of the main advantages of this technique is

the possibility of filtering only one frequency compo-

nent. The accuracy of the filtering depends upon the

resolution of the spectrum. Fig. 17 shows the vibration

spectrum of a healthy induction machine before and

after filtering some frequency components using vari-

able length rectangular window. The spectrum of the

machine current before and after filtering the

fundamental frequency is shown in Fig. 18. The best

results are achieved when the filtered spectrum compo-

nents are equal to integer multiple of the frequency

resolution.

7.4. Spectrum analysis

In the present work, FFT algorithm is used to

perform discrete Fourier transform (DFT) for the

vibration, voltage, and current signals. The time domain

signals and their spectrums are shown in Fig. 19. For

each type of signal, there are different techniques for

extracting the important features for diagnosis.

Fig. 12. Vibration spectrums of different runs and average of nine runs.


Fig. 13. (a and b) Spectrums of two vibration segments of healthy induction machine; (c) cross correlation among them.

Fig. 14. (a and b) Vibration spectrums of healthy induction machine obtained from different runs; (c) cross correlation among them.


Fig. 15. Vibration spectrums of (a) healthy machine, (b) faulty machine, (c) cross correlation among them.

Fig. 16. (a and b) Voltage spectrum of two machine; (d and e) the corresponding line currents; (c and f) the voltages and currents correlation,

respectively.


Fig. 17. (a) Healthy machine vibration spectrum and the filtered version; (b) (10�/200 Hz) band pass; (c) (98�/102 Hz) band pass; (d) (680�/850 Hz)

band pass.

Fig. 18. (a) Machine current spectrum; (b) spectrum after filtering the fundamental harmonic.


7.4.1. Vibration analysis

In order to achieve earliest possible recognition of the

defect in a machine, a comparison of the spectrum of the

machine under study with the spectrum of a typical

(healthy) one must be performed. However, four

approaches of comparison are used.

7.4.1.1. Narrow band analysis. This type of analysis is

performed using high-resolution in frequency. By in-

creasing the resolution of the spectrum, more details

about the frequency contents of the signal can be

achieved, but the stability of the spectrum becomes

poorer. Frequency resolution of 0.1, 1.0, 2.0 Hz is used

here for the purpose of comparison. Fig. 20 shows the

vibration spectrums with different resolutions. It can be

noticed that, although high frequency resolution gives

more detail information, any small change in the signal

(i.e. small random vibration and noise) makes a

difference in the individual lines and thereby in the

spectrum as a whole. In addition, if there is a change in

the rotation speed, all the rotation dependent frequency

components will change in the spectrum (i.e. 1% change

in the speed cause the 1�/ components to shift its

location by 1 and so on), so that direct comparison is

difficult to achieve. Frequency resolution of 1 Hz for

vibration signal gave acceptable detail and stable

spectrum for most of the studied cases.

However, from the literature some frequency compo-

nents related to some types of faults, and from the

calculation of the machine vibration another set offrequency components are obtained. The amplitudes of

these components are used to specify the degree of fault

for the certain operating condition. The vibration

spectrums for mechanical unbalance, supply unbalance

and single phasing are presented in Fig. 21. It can be

observed that the first rotational harmonic has a

dominant value in the three spectrums, which is higher

than the baseline value. In this case, it is difficult torecognise the type of the fault, hence another frequency

components must be considered for comparison. In

addition, by including another machine quantities such

as currents and voltages and the expert’s knowledge,

enhancement of the system ability for diagnosis may be

achieved.

7.4.1.2. Variable band harmonic analysis. In this ap-

proach, selected frequency components from the vibra-

tion spectrum are used for comparison. It has been

noticed that the locations of these selected components

rapidly change. These changes are due to the random

vibrations and change in machine speed. Hence, narrow

band analysis may not be suitable for the comparisonbetween the two spectrums. In the present approach, a

small band of frequency is considered instead of

individual frequency components. The bands used are

Fig. 19. (a, b and c) Voltage, current and vibration signals, respectively; (a?, b? and c?) the corresponding spectrums.


Fig. 20. (a, b and c) Vibration spectrums for different value of frequency resolution 0.1, 1 and 2 Hz, respectively; (a?, b? and c?) zoomed version.

Fig. 21. Vibration spectrum correspond in various machine conditions; (a) healthy machine; (b) mechanical unbalanced; (c) unbalanced supply; (d)

single phasing.


a percentage of the order of the frequency component.

Different percentages of the frequency components are

tried to select the optimum value that gives acceptable

results for the comparison between different types ofmachine fault. For example, if the selected frequency is

equal to 100 Hz and the percentage is 2%, the band will

include the frequency components from 98 to 102 Hz

and so on for other frequencies. After selecting the

width of the band, three procedures are used for

obtaining the compared value of the band. The first

one is achieved by calculating the average value of the

band components and then using this value for compar-ison with the corresponding value of other spectrums.

The second one is achieved using the highest peak in the

band. The third procedure uses the energy of the band

for comparison with the energy in the corresponding

band in the reference spectrum. It is observed that all the

procedures give good results in the case of medium and

high degree of faults. For small degree of fault,

uncertain information may be obtained, especially ifthe level of random noise is high and there is fluctuation

in the speed, load and supply voltage

7.4.1.3. Band frequency measurement. This approach

can be used to give a primary investigation about the

status of the machine. Bands of frequency in the

vibration spectrum are selected according to the origin

of the fault. The RMS value of these bands is used to

specify the degree and the origin of the faults bycomparing them with the corresponding bands in the

reference spectrum. From the literature and experimen-

tal observations, four frequency bands are selected to

cover the vibration harmonics of mechanical and

electromagnetic origin. The problem in using this

approach arises with some type of faults, where the

vibration harmonics and its multiples may cover wide

range of the spectrum especially at high degree of faultsand that may increase the uncertainty of the obtained

information.

7.4.1.4. Spectrum masking. As mentioned earlier, if

there is a level of random change in the signal or change

in the speed, narrow band analysis approach may fail to

provide accurate information. This problem may be

recovered with the help of Spectrum Masking technique.The technique involves two steps. In the first, a new

spectrum is formed by adding the energy present in a

number of narrow frequency bands of the original

spectrum. The width of narrow frequency band is equal

to a fixed width. Fig. 22 shows the original spectrum and

the new-formed spectrum. The bands of the new

spectrum must be wide enough to encompass the

variation in the signal. From the new generated spec-trum, another spectrum is generated to compensate the

change in the speed. Taking each segment of the

spectrum and pushing one bandwidth to either side as

shown in Fig. 23 obtain this spectrum. The minimum

level of the spectrum components is fixed to a threshold

level. The new spectrum is used successfully to compen-

sate up to 3.5% change in the speed. If the speed changesare more than the amount corresponding to the selected

bandwidth, the vibration harmonics will be lied outside

the limits of the mask. Fig. 24 presents the spectrum

masking for healthy machine and for the machine with

defected bearing. The comparison using these two

spectrums is much easier than using the original

spectrums.

7.4.2. Voltage and current signals

Frequency analysis of electrical variables of the

machine has been used to predict the machine condition.

Current harmonics can be related to most types of the

machine faults [32�/34]. In the present investigation,

current harmonics are examined to demonstrate the

relation between current harmonics and the machine

health. Fig. 25 shows the current waveform and its

spectrum for faulty and healthy induction machine. Itcan be noticed that the spectrum becomes smoother in

the case of a fault. This is due to the interaction (adding

and subtracting) between the mmf space harmonics and

the harmonics developed by some types of machine fault

[35]. The effect of feeding the machine from PWM

inverter with filtered output on the machine current

waveform and the spectrum for healthy and faulty

condition are given in Fig. 26. It can be noticed thatin the case of non-sinusoidal supply such as PWM

inverter it is difficult to distinguish between the current

harmonics due to the supply harmonics and the

harmonics originated by the machine conditions. It

may be noticed that a precise monitoring system is

needed to distinguish between the low-level frequency

components and the high-level supply frequency com-

ponent. This method needs to adjust the samplingfrequency so that the interest frequency component is

equal to integer multiple of the frequency resolution

[36,37].

Terminal voltage harmonics are examined to find the

exact value of the supply frequency, which is needed by

the diagnostic algorithm. In addition, the harmonic

analysis of the terminal voltage is used to predict the

harmonics of the machine. This is achieved by recordingthe machine terminal voltage directly after switching off

the machine i.e. induced machine voltage. The induced

voltage harmonics are same as air-gap flux harmonics

that can be related to the machine condition. Fig. 27

shows the induced voltage waveform of healthy and

faulty machine and their spectrum. For the same reason

mentioned above the faulty spectrum of the induced

voltage became smoother.The harmonics analysis of the machine vibration can

be used to detect wide range of the machine faults. The

change in the amplitude of current and voltage harmo-


Fig. 22. Spectrum masking of the vibration signal using (a) band energy; (b) peak value.

Fig. 23. Vibration spectrum with spectrum masking.


Fig. 24. (a) Vibration spectrum and the modified mask of healthy induction machine; (b) vibration spectrum and the modified mask of faulty

induction machine.

Fig. 25. Effect of machine condition on current harmonics (a) healthy machine; (b) machine with faulty bearing.


Fig. 26. Effect of machine condition on the current harmonics under non-sinusoidal supply. (a) Healthy machine; (b) machine with faulty bearing.

Fig. 27. Effect of the machine condition on the induced voltage harmonics. (a and b) Induced voltage of healthy and faulty machine, respectively; (a?and b?) the corresponding spectrums.


nics due to the machine faults have a small level and the

harmonic are very close to other spectrum harmonics,

hence can not be easily detected. The harmonic distor-

tion in current and voltage is used to assist the

diagnostic task in obtaining the machine condition.

7.5. Implementation of short term Fourier transform

STFT is used to estimate the frequency contents of the

non-stationary signals. STFT is dividing the signal into

a small segment using a window function (W) and

perform FFT for each segment. However, the vibration

signal picked up from the machine has two components;

stationary and non-stationary. Non-stationary part has

no relation with most of the studied faults. STFT is

applied to eliminate the non-stationary part by adding

the spectrum of all windowed segments and dividing it

by the number of segments as in case of spectrum

averaging.

The window function used in this investigation is the

rectangular window, which is simple to implement and

has some useful characteristics such as narrow main lobe

4p /(2N�/ 1). The spectrum of the rectangular is shown

in Fig. 28. It can be noticed that there are several side

lobes at both ends of the window. These side lobes may

give uncertain information in the vibration spectrum

and cause Gibbs phenomena [29]. Making overlap

between the successive windows can reduce this effect.

In this investigation, the number of samples used is

50 000 and the width of window is equal to 4000 samples

with overlap equal to 1200 samples. Then an average

spectrum is obtained using the spectrums of the wind-owed data. Fig. 29 shows the vibration signal, the

spectrums of the signal of different window locations

and the average spectrum. Fig. 29(b) shows that the

effect of random vibration and the noise has reduced in

the average spectrum. This procedure is repeated for all

experimental data and the obtained spectrums are used

to extract the required information and to state the

health of the machine.

8. Isolation of random vibrations

Any vibration signal obtained from electromechanical

systems contains a level of random changes. These

random changes in the measured signal may be due tothe random vibrations. These random vibrations can be

related to the health of the machine for some faults such

as dry bearing fault or bearing ageing. If these random

vibrations could be isolated from the measured signal,

useful information about bearing health may be ob-

tained.

From experimental observations, it is noticed that

there are some changes in the level of RMS values ofvibration signals obtained from the successive segments

of a long record signal or from repeatable tests

Fig. 28. Frequency response of the rectangular window.


performed on induction machine subjected to dry

bearing fault running under similar conditions. The

presence of dry bearing fault at an early stage introduces

a small level of randomness in the vibration signal.

Therefore, it is difficult to isolate these random vibra-

tions by traditional signal processing techniques. The

difficulty in discovering such fault is that neither thevariation in vibration level nor additional spectrum

components is detectable In order to isolate these

random vibrations, the algorithm presented in Fig. 30

is used with some modifications. In this algorithm, the

RMS value of each segment of the vibration signal is

calculated but not averaged as shown in Fig. 30. These

values are obtained from the same machine running

with the same condition. The fluctuation in the RMSvalues is nothing but the random vibrations.

9. Conclusion

In this paper, the treatment of raw data obtained from

physical machine parameters are presented. The imple-

mentations of various DSP and analysis techniques in

time and frequency domain with different machinevariables are given. Signal detectors such as mean

square value and crest factor are used with the voltages,

currents and vibration signals in time domain. In

addition, signal filtering, signal averaging and correla-

tion are used in time and frequency domain simulta-

neously. FTs and STFTs are used to present the time

domain signal in frequency domain. The obtained

vibration spectrum is analysed using narrow band,

variable band, selected band and spectrum masking

approaches. The data implemented with mentioned

techniques covers different operating conditions of the

machine under test. The obtained information is used as

input to the neural network.

Time domain analysis is used to obtain the machine

condition qualitatively using correlation coefficient,

RMS value, and crest factor. For primary investigation,

time domain analysis provides a rough figure, and is not

appropriate for fault classification and ranking. Fre-

quency domain analysis of the vibration signal provides

detailed information. The vibration harmonics are

related to types of machine faults. The machine condi-

tion can be obtained by comparing the amplitude of

these harmonics with those obtained from correspond-

ing ones in the healthy machine.

The traditional treatment of vibration spectrum

fluctuations is the averaging, which may lead to hide

some features of short duration. The alternative ap-

proach to such non-stationary vibration signal is the

Wavelet transform that can provide useful information

about any signal in time domain with different bands of

Fig. 29. (a) Implementation of STFT using moving rectangular window; (b) average spectrum.


frequencies. WT gives variable time resolution for

different frequency bands rather than STFT, which

gives constant resolution.

The area of condition monitoring and diagnostic is

very wide and includes many topics. It is suggested to

make some improvements in the monitoring system

through the use of the following:

. Inclusion of mmf harmonics in induction machine

model so that the relation between the machines

space harmonics and vibration harmonics can be

established. This can be made using machine per-

meance approach. An alternative to this is the

modelling of electromagnetic behaviour of the ma-

chine using finite element approach.

. Estimation of machine parameters (resistance and

inductance) through on-line modelling of induction

machine. The estimated values of machine para-

meters can give indication about the machine health

through a comparison with healthy one.

. Building a database for vibration harmonics using

experimental and theoretical investigations for var-

ious size and design of standard of three phase

induction motors. Through this data base, a new

standard for vibration can be established instead of

the traditional one, which depends upon RMS

velocity of vibration rather than harmonic amplitude.

. Employing expert system for fault diagnosis of

induction motor using rules obtained from the

connection weight of a supervised neural network

and rules extracted from the heuristic knowledge.

This combination of ANN knowledge and expert’s

knowledge may enhance the accuracy and efficiency

of the monitoring system for diagnosis.

Fig. 30. Isolation of random vibrations from different wavelet decompositions.


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