1. science
2. engineering

ISSN: 2393-994X
KARPAGAM JOURNAL OF ENGINEERING RESEARCH (KJER)
Volume No.: II, Special Issue on IEEE Sponsored International Conference on Intelligent Systems and Control (ISCO’15)
Intelligent Fault Diagnosis of Electrical Machines Based on Energy
Loss Calculation
M. Jose Paul Jacob1, S.J.Saranya2
Safety officer, [email protected], TRF Limited (A TATA Enterprise),Port and Yard Equipments Division, Tuticorin,Tamil Nadu,India.
2
Assistant Professor,[email protected], Department of EIE, Mahendra Engineering College, Tiruchengode,Tamil Nadu,India.
1
Abstract
The objective of this paper is to propose a new method for the detection of fault in electrical machines
(Induction Motor) by developing an intelligent and low-cost vibration monitoring and fault diagnostic
system using Virtual Instrumentation technique. Intelligent system is built with aid of LabVIEW software
and PIC Micro Controller for testing and diagnoses the faults of electrical machines using vibration,
speed and energy loss calculation through comparative diagnoses between standards and faults. The
standards and fault signal obtained from the electrical machine is stored and created a new data base in
the LabVIEW graphical programming environment. The created data base is used to compare the real
time fault with the standards to indicate the failures situations and fault in electrical machines. Result and
recommendations are displayed according to the condition of the electrical machines with an allowable
tolerance.
Keywords: Vibration Sensor, Speed Sensor, Induction Motor, Micro Controller, LabVIEW.
1. Introduction
Electric motors are used in many industrial, home and office applications and their normal operation is an
important condition. They are simple and have high reliability. However, compared to the thermal, electrical and
mechanical stresses, mechanical failures are unavoidable in Electrical Motors. The risk of motor fault can
remarkably induce a serious danger to the normal life and productive activities of the people. It may reduce costly
expensive downtime if signs of failure can be distinguished availably. The Motor faults are typically related to core
components such as stators, rotors, and bearings. Surveys indicate that these components account for 88% of motor
failures. The incipient fault detection of electrical, magnetic and mechanical parts of the motor has recently become
one of the most important problems of motors exploitation. The major three faults in induction motors are winding
faults, faults of the magnetic circuit and faults of the motor mechanical system (mainly bearing failures).
All these faults are connected with some particular phenomena: electrical, magnetic and vibroacoustic ones. The
fault statistics of high- and low-voltage induction motors has been changing within the last few years. There is a
significant increase of mechanical failures in comparison with electrical and magnetic circuits’ failures [1]. It can be
demonstrated in the following way (as the percentage of all motor faults),

Bearing failures : 40% or 50%,

Stator failures
: 16 % or 36%,

Rotor failures
: 2.5% or 10%,

Other failures
: 14% or 20%,
Most of times techniques used for fault diagnoses in electrical motors are vibration analysis and stator current
analysis because they are easy to measure and highly reliable [5][6]. Vibration is one of the key indicators of quality
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ISSN: 2393-994X
KARPAGAM JOURNAL OF ENGINEERING RESEARCH (KJER)
Volume No.: II, Special Issue on IEEE Sponsored International Conference on Intelligent Systems and Control (ISCO’15)
of motor, when they are steady work; the vibration spectrum has certain characteristics, if their internal fails, their
corresponding vibration spectrum and other parameters will also change. In addition, the motor vibration waveform
is not a single sine wave, but composed of many different frequencies waveforms [8][9][10]. Therefore, the
vibration spectrum of motor needs to be analyzed, to prevent malfunction and resolve in timely manner. The levels
of vibration usually increase with deterioration in the condition of the machine. So, vibration is an important
parameter for the condition monitoring of machines and their elements [11][12][13]. Vibration displacement,
velocity and acceleration levels can also be measured. Acceleration is used for high frequencies and displacement
for low frequencies. Vibration velocity is normally measured [14].
Over the past 20 years, the virtual instrument technology has made considerable development and been widely
used in engineering test. Compared with traditional instruments, virtual instrumentation technology has several
advantages, such as, high-performance, excellent expands ability and so on. LabVIEW is commonly virtual
instruments development software with the flexibility of programming languages; it can be combined with built-in
tools, which are designed for testing, measurement, control. And a variety of applications can be created by
LabVIEW, ranging from temperature monitoring to complicated simulation and control systems [15][16][17].
This paper deals with the vibration analysis method which now dominates the other methods. Accelerometers are
used to acquire the signals from the electrical machines. The acquired signals are analyzed further using LabVIEW
software. Signals from the accelerometer are connected to the PIC Micro Controller through an interface called RS
232. The signals acquired using the micro controller is given as an input to LabVIEW. The acquired signals will be
then shifted to frequency spectrum and then analyzed. The analysis is done by comparing standards and faults of
electrical machine. The standards and fault signal obtained from the electrical machine is stored and created a new
data base. The detecting the various faults based on the access database and the decision support system includes the
information of about the failures situations of the machines[18], its causes, the diagnostic methods and their
different combinations is done with LabVIEW software. The acquired signal is compared with the preset values and
the condition of the electrical machine is then displayed.
2. Vibration Signals for Fault Detection in Electrical Machines
When an electrical machine is running periodically high pressures and unavoidable frictions cause high failure and
often it fails before its expected lifetime. The Motor faults are typically related to core components such as stators,
rotors, and bearings. Mostly bearing failures are highly occurs in the motion machines than other failures.
Bearing faults such as outer race, inner race, ball defect and train defect causes machine vibration. These defects
have vibration frequency components that are characteristic of each type of defect. The mechanical vibration caused
by the bearing defect results in air-gap eccentricity. Oscillations in air-gap length induce variations in flux density.
These variations produce quickly accelerate the wear of a bearing and intense vibrations are generated as a result of
the repetitive impacts of the moving components on the defect.
Rotor fault such as broken rotor bars, causes asymmetrical working condition within rotor, this in turn results in
various additional phenomena, such as change in the stator current spectrum, additional internal forces and
mechanical vibrations, oscillatory components of rotor speed, electromagnetic torque, output power, etc. These effects
are very weak in the initial stage of the rotor fault and only sensitive measurement methods can detect the damage. In
practice, the vibration analysis is the most useful one in such cases. Characteristic components with frequency
dependent on motor slip s occur in the stator current spectrum in the case of a rotor bar fault. The values of slip
harmonics in the spectrum of stator current for various damages of rotor cages (the number of broken bars, fault kind)
and various vibration levels can be treated as a set of input data for LabVIEW.
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ISSN: 2393-994X
KARPAGAM JOURNAL OF ENGINEERING RESEARCH (KJER)
Volume No.: II, Special Issue on IEEE Sponsored International Conference on Intelligent Systems and Control (ISCO’15)
3. Hardware component of vibration, speed testing and Analysis system
Vibration, speed measurement and analysis system consists of two main parts as follows: the first one is sensor
measurement system that pickup a variety of signals or parameters of machine, and makes it into a standard voltage
or current signals. The other one includes signal acquisition, analysis and processing, and signal display and record.
Virtual instrumentation generally refers to acquisition and display of vibration signals, while a variety of further
processing, analysis. The structure of vibration and speed testing system is shown in Fig. 1.
Vibration Signal
Acceleration
Transducer
Amplifier
Speed Signal
Micro Controller
Computer
Proximity
Sensors
Fig. 1. Structure of vibration and speed testing system
The basic hardware includes acceleration transducer, speed sensor (Proximity), amplifier, Micro Controller,
Personal computer and so on. The experimental set up is shown in Fig. 2.
Fig. 2. Test Bench of vibration and speed testing system
4. Proposed Methodology
The schematic representation of the work is shown in Fig. 3. It consists of three major parts, namely (i) data
collection and preprocessing, (ii) model design and training, and (iii) model execution.
4.1. Data Collection and Preprocessing
The objective of the data pre-processing is to determine the suitable location for data required for the modeling
activity. Although a number of parameter may impact the electrical machines performances (e.g. Vibration, Speed,
and Temperature), the Bearing failure, stator failure & winding failure are the most considered problem created in
electrical machines due to ageing. So these parameters (vibration & speed), are taken as input for model design.
These two parameters are considered good indicator of the electrical machines performance and fault diagnosis.
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ISSN: 2393-994X
KARPAGAM JOURNAL OF ENGINEERING RESEARCH (KJER)
Volume No.: II, Special Issue on IEEE Sponsored International Conference on Intelligent Systems and Control (ISCO’15)
Data Collection &
Preprocessing
Identification of Input &
Output Parameter
Model Design
Lab View
Coding is done by Energy
Loss Calculation
Model Training
Data Base for Various Faults
Predicting the Real Time Fault
Model Execution
Fig. 3. Steps of the Model Development Process
4.2. Model Design and Training
In this work, a virtual instrumentation technology is used to design a data flow diagram. The data flow diagram
consists of two sections, namely the front panel and the circuit block diagram. The front panel with various
parameters such as sensor section, operating type, graph section, accelerometer section, fault indication section &
energy loss section used to indicate the performance of machines and speed control. These are shown schematically
in Fig.4.
Fig. 4. Front panel of Vibrobench Interface
The sensor section will prompt the user to select the type of sensor that is used for vibration analysis. The
Operating type section will ask the user, the type of operation to be performed viz. Standardization, Measurement
and Analysis, Recording. The graph section consists of five graphs; they are acceleration, velocity, vibration, power
spectrum & speed. These graphs are used to indicate the various performance activities in the machines.
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ISSN: 2393-994X
KARPAGAM JOURNAL OF ENGINEERING RESEARCH (KJER)
Volume No.: II, Special Issue on IEEE Sponsored International Conference on Intelligent Systems and Control (ISCO’15)
The Accelerometer Section will show the vibration frequency and maximum amplitude of the vibrating machine
and also the frequency and displacement of the process signal. The Energy Loss Section is based on formulas the
energy loss of the machine in terms of Joule at the maximum vibration will be calculated and shown in the display.
The energy loss is also shown in terms of Calories. After this, the number of samples and the rate of samples are
displayed. The number of samples taken is 100 and the rate at which the samples are taken is 1000. The Fault
indication section shows the fault occurs in machines comparing with the data base. The data base indicates the
various failures of electrical machines.
The Accelerometer Section will show the vibration frequency and maximum amplitude of the vibrating machine
and also the frequency and displacement of the process signal. The Energy Loss Section is based on formulas the
energy loss of the machine in terms of Joule at the maximum vibration will be calculated and shown in the display.
The energy loss is also shown in terms of Calories. After this, the number of samples and the rate of samples are
displayed. The number of samples taken is 100 and the rate at which the samples are taken is 1000. The Fault
indication section shows the fault occurs in machines comparing with the data base. The data base indicates the
various failures of electrical machines.
Fig. 5. Block Diagram of Vibrobench Interface
Depending on the front panel, the block diagram is also divided into many sections. The block diagram consist
of the graphical source code of the front panel (Fig.5), which describes the various mathematical operations, graph
section, Express VI’s (Virtual Instruments) etc,. Express VI tools are used to create interactive and simplify the
development of test, measurement, and control applications. The data base is created using multiple array system
for storing and comparison analysis in LabVIEW for Fault diagnosis. The mathematical calculation of energy loss
has been programmed using the energy loss calculation theory.
4.3. Energy Loss Calculation
Energy loss in a machine means a decrease in efficiency of that machine. Energy loss can occur in various ways.
The energy loss may be due to sound, vibration, heat, friction etc. Here, the energy loss is calculated by taking
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ISSN: 2393-994X
KARPAGAM JOURNAL OF ENGINEERING RESEARCH (KJER)
Volume No.: II, Special Issue on IEEE Sponsored International Conference on Intelligent Systems and Control (ISCO’15)
vibration into consideration. The rate of change of energy with time (dW/dt) is given by
dW/dt = force x velocity = Fv = - c v2 = - c (dx/dt)2
(1)
ΔE= ∫ FD (dx/dt) dt = ∫ FD X ω cosωt dt
(2)
Or
The negative indicates that energy dissipates with time. Consider a motion as x(t) = X sinω dt, where X is the
amplitude of the motion, ωd is natural frequency and the energy dissipated in a complete cycle is given by
substituted motion x(t) into Eq. (2) is.
ΔW = ∫ c (dx/dt)2 dt = ∫ cX2 ωd cos2ωdt . d(ωdt) = Π c ωd X2
(3)
This shows that the energy dissipated is proportional to the square of the amplitude of motion. We know that
energy loss occurs due to the damping given to the machine. The frequency of damped vibration is given by
ωd= (√1 - ζ2) ωn
(4)
The logarithmic decrement is given by
Δ = 2 Π ζ = 2.32 X
5. Simulation Results
In order to evaluate the proposed approach, experimental analysis on machine fault diagnosis were carried out.
An acquisition of vibration signals was carried out on a test bench that was made of an induction motor. The data
acquisition set on the machine consists of seven examples of vibration signal recorded on different levels of failures.
Different operating conditions of the machine were considered: healthy, bearing fault and rotor fault. The fault types
and legends describing them all through this work are:
a)
b)
c)
d)
e)
f)
g)
Class1 : Healthy motor
Class 2: Bearing fault in outer ring (load side)
Class 3: Bearing fault in inner ring (load side)
Class 4: Bearing fault in outer ring (Fan side)
Class 5: Bearing fault in inner ring (Fan side)
Class 6: Bearing fault in roller ball (load side)
Class 7: Bearing fault in roller ball (Fan side)
The method of electrical machine diagnostics by the spectrum of high frequency vibration envelope is based on
the analysis of characteristics in the form of friction forces in good and defective bearings as well as in the features
of shock pulses that appears in the interaction of rolling surfaces with cavities, spills or cracks in the bearing
elements. In rolling element, bearings with defects of installation including misalignment of races and loads on the
rolling elements increase and, these loads become dependent on the rotation angle of the rotating race and the cage.
Thus, the friction forces, together with the random vibration excited by them, which then become amplitude
modulated.
In bearings with non-uniform wear of inner and outer races and rolling elements, the friction coefficient in turn
depends on the rotation angle of rotating race and cage which results in similar amplitude modulation of the friction
forces and the resultant high frequency vibration. As a result, all defects of bearing installation, wear, and cavities
can be detected by the high frequency vibration envelope. The acceleration, velocity, displacement graphs and
joules, calories are obtained from vibration signal by using LabVIEW software. The amplitudes for the induction
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ISSN: 2393-994X
KARPAGAM JOURNAL OF ENGINEERING RESEARCH (KJER)
Volume No.: II, Special Issue on IEEE Sponsored International Conference on Intelligent Systems and Control (ISCO’15)
motor being tested and analysis have been done according to various faults condition of electrical machine.
Frequency spectra are generated from the data collected by the accelerometer using LabVIEW. The Frequency
spectrum from Fig (7-13) is generated based on the data obtained by the MEMS Accelerometer.
Fig. 7. Healthy Motor
Fig. 10. Bearing Fault in Outer Ring (Fan Side),
Fig. 8. Bearing Fault in Outer Ring(Load Side)
Fig. 11. Bearing Fault in Inner Ring (Fan Side),
Fig. 9. Bearing Fault in Inner Ring (Load Side)
Fig. 12. Bearing Fault in roller ball (Fan Side),
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ISSN: 2393-994X
KARPAGAM JOURNAL OF ENGINEERING RESEARCH (KJER)
Volume No.: II, Special Issue on IEEE Sponsored International Conference on Intelligent Systems and Control (ISCO’15)
Fig. 13. Bearing Fault in roller ball (Load Side),
Table 1. Percent correct detection of motor condition under fault
Condition of motor
Healthy Motor
Bearing Fault Outer Ring (Load)
Bearing Fault Inner Ring (Load)
Bearing Fault Outer Ring (Fan)
Bearing Fault Inner Ring (Fan)
Ball Bearing Fault (Load)
Ball Bearing Fault (Fan)
Acceleration
39
42
47
41
44
56
52
Velocity
29
35
39
32
36
45
43
Displacement
14
17
21
15
18
27
24
Joules
1044
3491
4374
3468
4048
5671
5328
Calories
249
833
1044
828
966
1325
1272
We observe that the acceleration corresponding to the motor with fault bearing it is bigger than that for motor
with healthy motor. This is because the motor is unbalanced due to the internal disturbance accorded by fault drives.
6. Experimental Results
A measuring setup was arranged to get data from motor. The motor used in the measurement was same as used in
the LabVIEW motor simulation program. The data was recorded through the help of waveform. The tests were
carried out with the motor in healthy condition and with real fault parts of motor. Different rotor faults were
prepared, from one to six fault bearings. The data obtained from the waveform is recorded and created a data base.
Data is collected from the full speed condition of motor. The data base contains the maximum peak values and
energy loss of various fault analysis for testing. This data is compared with the maximum peak of vibration signal
obtained from real time fault with analyzed data to indicate the fault condition of motor. A delay cycle is used to
increase the accuracy of the fault condition of the motor and to avoid the overlapping other data signal. This method
is very simple and has high accuracy, to analysis the various fault conditions of motor. Some error occures due to
the external source of vibration added with motor vibration, due to that sensing signal is affected. This type of error
is unavoidable in fault dedection.
7. Conclusion
An intelligent and low cost vibration monitoring and diagnostic system for electrical machines has been presented
in this paper. After review the art of machinery fault detection, different conventional and recent techniques were
discussed for machine fault signature analysis with particular regard to rolling contact bearing fault diagnosis
through vibration analysis. Here new method has been adapted to diagnose the machines fault through vibration
analysis. The vibration signal acquired in real time from accelerometer mounted on the machines are compared with
pre-obtained signatures data base and determine the health of the machine and indicating the various faults. This
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ISSN: 2393-994X
KARPAGAM JOURNAL OF ENGINEERING RESEARCH (KJER)
Volume No.: II, Special Issue on IEEE Sponsored International Conference on Intelligent Systems and Control (ISCO’15)
methodology can extract the needed spectrum information quickly, also determine the type and damage extent of
motor accurately.
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