# Machine Fault Signature Analysis

Authors: Pratesh Jayaswal; A. K.  Wadhwani; K. B. Mulchandani
Journal: International Journal of Rotating Machinery
 (2008-02-06)

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## Abstract

The objective of this paper is to present recent developments in the field of machine fault signature analysis with particular regard to vibration analysis. The different types of faults that can be identified from the vibration signature analysis are, for example, gear fault, rolling contact bearing fault, journal bearing fault, flexible coupling faults, and electrical machine fault. It is not the intention of the authors to attempt to provide a detailed coverage of all the faults while detailed consideration is given to the subject of the rolling element bearing fault signature analysis.


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## Body

## 1. Introduction

Machine fault problems are broad
sources of high maintenance cost and unwanted downtime across the industries.
The prime objective of maintenance department is to keep machinery and plant
equipments in good operating condition that prevents failure and production
loss. If the department organizes a predictive maintenance program, this goal
as well as cost benefits can be achieved, while accurate information at the
right time is a crucial aspect of a maintenance regimen [1]. The condition-based
maintenance strategy is being employed for uninterrupted production process in
industries. Condition-based maintenance (CBM) consists of continuously
evaluating the condition of a monitored machine and thereby successfully
identifying faults before catastrophic breakdown occurs. Numerous condition
monitoring (cm) and diagnostics methodologies are utilizing to identify the
machine faults to take corrective action. Machine fault identification can be
done with different methodologies as vibration signature analysis, lubricant
signature analysis, noise signature analysis, and temperature monitoring, with
the use of appropriate sensors, different signal conditioning, and analyzing
instruments.Vibration signature analysis
techniques for machine fault identification are the most popular among other
techniques. Vibration monitoring is based on the principle that all the system
produces vibration. When a machine is operating properly, the vibration is
small and constant, however, when faults develop and some of the dynamic
process in the machine changes, there will be changes in vibration spectrum observed.
After the review of previous published work, it is concluded that gear fault,
bearing fault, and coupling fault are studied for research purpose to fault
signature analysis. The majority of industrial machines use ball or rolling elements
bearings (REB). The vibration signals obtained from the vicinity of a bearing
assembly contain rich information about the bearing condition. Most of the
researchers have used vibration signature analysis techniques for rolling
element bearing fault identification in case of single defect on bearing
components. Time-domain and frequency-domain vibration analysis techniques were
tested but effective identification of bearing condition is, however, not so
straightforward. Several researchers have used artificial intelligence
techniques as well as time-frequency domain analysis and developed expert
diagnostics system for bearing fault identification with the use of artificial neural
network, fuzzy logic, wavelet transform, and hybrid techniques. In this paper,
a review of need and different techniques of machine fault signature analysis
are discussed. A special emphasis is
given to rolling element bearing vibration signature analysis, while other
techniques are also covered. This paper
is divided into different sections, each dealing with various aspects of the
subject. It begins with a summary of need of machine fault diagnosis followed
by a general overview of the numerous means of signature analysis.


## 2. Necessity of Machine Fault Identification

Machine fault can be defined as any
change in a machinery part or component which makes it unable to perform
its function satisfactorily or it can be defined as the termination of
availability of an item to perform its intended function. The familiar stages before
the final fault are incipient fault, distress, deterioration, and damage, all
of them eventually make the part or component unreliable or unsafe for
continued use [2]. Classification of failure causes are as follows:(i)
inherent weakness in material,
design, and manufacturing;(ii)
misuse or applying stress in
undesired direction;(iii)
gradual deterioration due to
wear, tear, stress fatigue, corrosion, and so forth. Antifriction bearings failure is a
major factor in failure of rotating machinery. Antifriction bearing defects may
be categorized as localized and distributed. The localized defects include
cracks, pits, and spalls caused by fatigue on rolling surfaces. The distributed
defect includes surface roughness, waviness, misaligned races, and off-size
rolling elements. These defects may result from manufacturing and abrasive wear
[3].Modern manufacturing plants are
highly complex. Failure of process
equipments and instrumentation increased the operating costs and resulted in
loss of production. Undetected or uncorrected malfunctions can induce failures
in related equipments and, in extreme cases, can lead to catastrophic
accidents. Early fault detection in machines can save millions of dollars on
emergency maintenance and production-loss cost. Gearbox and bearings are
essential parts of many machineries [4]. The early detection of the defects,
therefore, is crucial for the prevention of damage and secondary damage to
other parts of a machine or even a total failure of the associated large system
can be triggered [5].There are certain objectives of
machine fault identification:(i)
prevention of future failure events;(ii)
assurance of safety,
reliability, and maintainability of machineries.Machineries failures reveal a
reaction chain of cause and defect. The end of the chain is usually a
performance deficiency commonly referred to as the symptom, trouble, or simply
the problem. The machine fault signature analysis works backwards to define the
elements of the reaction chain and then proceeds to link the most probable
failure cause based on failure analysis with a root cause of an existing or
potential problem. Accurate and complete
knowledge of the causes responsible for the breakdown of a machine is necessary
to the engineer, similarly, as knowledge of a breakdown in health is to the
physician. The physician cannot assure a lasting cure unless he knows what lies at the root of
the trouble, and the future usefulness of a machine often depends on correct
understanding of the causes of failure. The proper maintenance can be done only
after the knowledge of root cause of failure.Edwards et al. [6] present a review
on fault diagnosis of rotating machinery to provide a broad review of the state
of the art in fault diagnosis techniques. The early fault detection and
diagnosis allow preventive maintenance and condition-based maintenance to be
arranged for the machine during scheduled period of downtime caused by
extensive system failures that improves the overall availability, performance
and reduces maintenance cost. For the fault diagnosis problem, it is not only
to detect fault in system, but also to isolate the fault and find out its
causes.


## 3. Condition-Based Maintenance

Maintenance is a combination of
science, art, and philosophy. The rationalization of maintenance requires a
deep insight into what maintenance really is. Efficient maintenance is a matter
of having the right resources in the right place at the right time. Maintenance
can be defined as the total activities carried out in order to restore or renew
an item to working condition, if fault is there. Maintenance is also defined as
combination of action carried out to return an item to or restore it to an
acceptable condition. The classification of maintenance according to timings of
action for maintenance is shown in Table1.Table 1
Timings of action for maintenance.
Timings of actionMaintenanceOperating to failureShutdown or breakdownFixed time basedPreventiveCondition basedPredictive or diagnosticEvery machine component behaves as an
individual. Failure can take place earlier or later than recommended in case of
preventive maintenance. It can be improved by condition-based maintenance.
Dileo et al. [7] present a review on the
classical approaches to maintenance and then compare them with condition-based maintenance
(CBM).The prevention of potential damage to
machinery is necessary for safe, reliable operation of process plants. Failure
prevention can be achieved by sound specification, selection, review, and
design audit routines. When failures do occur, accurate definition of root
cause is an absolute prerequisite to the prevention of future failure events
[2].Condition-based
maintenance is defined as “maintenance work initiated as a result of knowledge
of the condition of an item from routine or continuous checking.” It is carried
out in response to a significant deterioration in a unit as indicated by a
change in a monitored parameter of the unit condition or performance. Condition
reports arise from human observations, checks, and tests, or from fixed instrumentation
or alarm systems grouped under the name condition monitoring. It is here that
one can make use of predictive maintenance by using a technique called signature
analysis. Signature analysis technique is intended to continually monitor the
health of the equipment by recording systematic signals or information derived
from the form of mechanical vibrations, noise signals, acoustic and thermal
emissions, change in chemical compositions, smell, pressure, relative
displacement, and so on [8]. Mann et al. [9] present an article explores the
benefits of condition-based preventive maintenance compared to the traditional
statistical reliability approach. Nandi and Toliyat [10] present a review on condition monitoring
and fault diagnosis of electrical machines. Marcus [11] proposed
condition-based maintenance to rail vehicle for more effective maintenance.Condition-based maintenance differs
from both failure maintenance and fixed-time replacement. It requires
monitoring of some condition-indicating parameter of the unit being maintained.
This contrasts with failure maintenance, which implies that no successful
condition monitoring is undertaken and with fixed-time replacement which is
based on statistical failure data for a type of unit. In general, condition-based
maintenance is more efficient and adaptable than either of the other
maintenance actions. On indication of deterioration, that unit can be scheduled
for shutdown at a time chosen in advance of failure, yet, if the production
policy dictates, the unit can be run to failure. Alternatively, the amount of
unnecessary preventive replacement can be reduced, while if the consequences of
failure are sufficiently dire, the condition monitoring can be employed to
indicate possible impending failure well before it becomes a significant
probability. The trend monitoring method for one or group of similar machines
is possible if sufficient data of monitored parameters are available. It
relates the condition of machine(s) directly to the monitored parameters. On
the other hand, condition checking method is employed for a wide range of
diagnostics instruments apart from human senses. Some of the recent
developments in the form of CBM are proactive maintenance, reliability centered
maintenance (RCM) and total productive maintenance (TPM).


## 4. Machine Condition Monitoring

When a fault takes places, some of the machine parameters are subjected to change. The change in
the machine parameters depends upon the degree of faults and the interaction
with other parameters. In most cases, more than one parameter are subjected to
change under abnormal condition. Condition monitoring can be carried out when
the equipment is in operation, which known as on-line, or when it is off-line,
which means when it is down and not in the operation. While on-line, the
critical parameters that are possible to monitor are speed, temperature,
vibration, and sound. These may be continuously monitored or may be done
periodically. Off-line monitoring is carried out when the machine is down for
whatever reason. The monitoring in such would include crack detection, a
thoroughly check of alignment, state of balancing, the search for tell-tale
sign of corrosion, pitting, and so on.The International Standards
Organization's Technical Committee 108 (ISO/TC108) produces standards in the
area of mechanical vibration, shock, and machine condition monitoring.
ISO/TC108's Subcommittee 5 (ISO/TC108/SC5) has focused on standards for the
condition monitoring and diagnostic of machines. This subcommittee has
published ISO 13374-1:2003 which establishes general guidelines for software
specifications related to data processing, communication, and presentation of
machine condition monitoring and diagnostic information. This standard defines
the data processing and information flow needed between processing blocks in
condition monitoring systems. Machine condition
monitoring (MCM) is a vital component of preventive and predictive maintenance
programs that seek to reduce cost and avoid unplanned downtime. MCM also
contributes to health and safety by recognizing faults which may give rise to
pollution or health hazards, and also by indication of incipient faults which
could produce danger conditions. MCM setups include measurement hardware and
software that acquire and interpret signals generated by the machine being
monitored. Condition monitoring is taken to mean the use of advanced
technologies in order to determine equipment condition, and to predict potential
failure. It includes, but is not limited to, technologies such as visual
inspection, vibration measurement and analysis, temperature monitoring,
acoustic emission analysis, noise analysis, oil analysis, wear debris analysis,
motor current signature analysis, and nondestructive testing.


### 4.1. Visual Inspection

visual monitoring can sometimes
provide a direct indication of the machine’s condition without the need for
further analysis. The available techniques can range from using a simple
magnifying glass or low-power microscope. Other forms of visual monitoring
include the use of dye penetrants to provide a clear definition of any cracks
occurring on the machine surface, and the use of heat-sensitive or
thermographic paints. The condition of many transmission components can readily
be checked visually. For example, the wear on the surfaces of gear teeth gives
much information. Problems of overload, fatigue failure, wear and poor
lubrication can be differentiated from the appearance of the teeth.


### 4.2. Vibration Analysis

Modern condition monitoring techniques
encompass many different themes; one of the most important and informative is the vibration analysis
of rotating machinery. Using vibration analysis, the state of a machine can be
constantly monitored and detailed analysis may be made concerning the health of
the machine and any faults which may arise or have already arisen. Machinery distress very often
manifests itself in vibration or a change in vibration pattern. Vibration
analysis is therefore, a powerful diagnostic and troubleshooting tool of major
process machinery.On-load monitoring can be performed mainly in the following three ways.(i)
Periodic field measurements with
portable instruments; this method provides information about long-term changes
in the condition of plant. The portable instruments are employed with a high
load factor and can often be placed in the care of only one man. Use of life
curves and the LEO approach assist the decision making.(ii)
Continuous monitoring with permanently
installed instruments; it
is employed when machine failures are known to occur rapidly and when the
results of such failure are totally unacceptable as in the case of turbine
generator units.(iii)
Signature analysis: scientific
collection of information, signals or signatures, diagnosis and detection of
the faults by a thorough analysis of these signatures based on the knowledge
hitherto acquired in the field, and judging the severity of faults for decision
making, all put together, is called signature analysis. The technique involves
the use of electronic instrumentation especially designed for the purpose of
varied capacities, modes of application and design features.Vibration signals are the most
versatile parameters in machine condition monitoring techniques. Periodic
vibration checks reveal whether troubles are present or impending. Vibration
signature analysis reveals which part of the machine is defective and why.
Although a number of vibration analysis
techniques have been developed for this purpose, still a lot of scope is there
to reach a stage of expertise.


### 4.3. Temperature Monitoring

Temperature monitoring consists of
measuring of the operational temperature and the temperature of component
surfaces. Monitoring operational temperature can be considered as a subset of
the operational variables for performance monitoring. The monitoring of
component temperature has been found to relate to wear occurring in machine elements,
particularly in journal bearings, where lubrication is either inadequate or
absent. The techniques for monitoring temperature of machine components can
include the use of optical pyrometers, thermocouples, thermography, and
resistance thermometers.


### 4.4. Acoustic Emission Analysis

Acoustic emission refers to the
generation of transient waves during the rapid release of energy from localized
sources within a material. The source of these emissions is closely associated
with the dislocation accompanying plastic deformation and the initiation or extension
of fatigue cracks in material under stress. The other sources of acoustic
emission are melting, phase transformations, thermal stress, cool-down cracking,
and the failure of bonds and fibers in composite materials. Acoustic emissions
are measured by piezoelectric transducers mounted on the surface of the
structure under test and loading the structure. Sensors are coupled to the
structure by means of a fluid couplant or by adhesive bonds. The output of each
piezoelectric sensor is amplified through a low-noise preamplifier, filtered to
remove any extraneous noise and furthered processed by suitable electronic
equipment.Traditionally, acoustic emissions as
a technique has been restricted to the monitoring of high cost structures due
to the expenses of the monitoring equipment. However, as equipment costs
steadily fall, the range of viable applications expands rapidly. Olsson et al. present
a frame work for fault diagnosis of industrial robots using acoustic signals
and case-based reasoning [12]. This frame work utilizes the case-based
reasoning for fault identification based on sound recording in robot fault
diagnosis. Wue et al. have developed experimental setup for online fault
detection and analysis of modern water hydraulic system [13], and suggested
that the incorporation of wavelet transformation into the analysis of acoustic
emission opens up the door for future research, which can prove to be very
relevant toward condition monitoring. Choe et al. [14] worked on neural pattern
identification of railroad wheel-bearing faults from audible acoustic signals
by comparison of FFT, continuous wavelets transform (CWT) and discrete wavelets
transform (DWT) features.


### 4.5. Noise Analysis

Noise signals are utilized for condition monitoring because noise
signals measured at regions in proximity to the external surface of machines
can contain vital information about the internal processes, and can provide valuable
information about a machine’s running condition. When machines are in a good
condition, their noise frequency spectra have characteristic shapes. As faults
begin to develop, the frequency spectra change. Each component in the frequency
spectrum can be related to a specific source within the machine. This is the
fundamental basis for using noise measurement and analysis in condition
monitoring. Sometimes the signal which is to be monitored is submerged within
some other signal and it cannot be detected by a straightforward time history
or spectral analysis. In this case, specialized signal processing techniques
have to be utilized.


### 4.6. Wear Debris Analysis

It is not possible to examine the working
parts of a complex machine on load, nor is it convenient to strip down the
machine. However, the oil which circulates through the machine carries with it
evidence of the condition of parts encountered. Examination of the oil, any
particle it has carried with it, allows monitoring of the machine on load or at
shutdown. A number of techniques are applied, some very simple, other involving
painstaking tests and expensive equipments. Presently, available lubricant
sampling or monitoring techniques like rotary particles depositor (RPD), spectrophotometer
oil analysis programme (SOAP), Ferrographic oil analysis and recent software
used techniques are available to distinguish between damage debris and normal
wear debris. Every machine ever designed undergoes a process of wear and tear in
operation, yet a battery of modern condition monitoring techniques is available
to monitor this process and trigger preventive maintenance routines which
depend on identifying any problem before it has the chance to develop to the
point of final breakdown. Now recently, engineers have been able to extend
their knowledge of conditions within operating machinery by studying the
particles of metallic debris which can be found in lubricating oil from
engines, gearboxes, final drive units and transmissions, or in hydraulic fluid,
and recording the number, size, and type of these fragments of debris.


### 4.7. Motor Current Signature Analysis

Motor current signature
analysis (MCSA) is a novel diagnostic process for condition monitoring of
electric motor-driven mechanical equipment (pumps, motor-operated valves,
compressors, and processing machinery). The MCSA process identifies,
characterizes, and trends overtime the instantaneous load variations of
mechanical equipment in order to diagnose changes in the condition of the
equipment. It monitors the instantaneous variations (noise content) in the
electric current flowing through the power leads to the electric motor that
drives the equipment. The motor itself thereby acts as a transducer, sensing
large and small, long-term and rapid, mechanical load variations, and
converting them to variations in the induced current generated in the motor
windings. This motor current noise signature is detected, amplified, and
further processed as needed to examine its time-domain and frequency-domain
(spectral) characteristics. Korde [15] demonstrates that the spectrum analysis
of the motors current and voltage signals can hence detect various faults
without disturbing its operation using FFT transformation.


### 4.8. Nondestructive Testing

The principle of nondestructive testing (NDT) is to be able to use the
components or structure after examination. The inspection should not affect the
item involved, and must therefore, be nondestructive. NDT includes many
different technologies, each suitable for one or more specific inspection
tasks, with many different disciplines overlapping or complimenting others.
Thus the best technique(s), for any one application, should be decided by an
expert eddy current testing, electrical resistance testing, flux leakage
testing, magnetic testing, penetrant testing, radiographic testing, resonant
testing, thermographic testing, ultrasonic testing, and visual testing are some
of the different NDT techniques.


## 4.1. Visual Inspection

visual monitoring can sometimes
provide a direct indication of the machine’s condition without the need for
further analysis. The available techniques can range from using a simple
magnifying glass or low-power microscope. Other forms of visual monitoring
include the use of dye penetrants to provide a clear definition of any cracks
occurring on the machine surface, and the use of heat-sensitive or
thermographic paints. The condition of many transmission components can readily
be checked visually. For example, the wear on the surfaces of gear teeth gives
much information. Problems of overload, fatigue failure, wear and poor
lubrication can be differentiated from the appearance of the teeth.


## 4.2. Vibration Analysis

Modern condition monitoring techniques
encompass many different themes; one of the most important and informative is the vibration analysis
of rotating machinery. Using vibration analysis, the state of a machine can be
constantly monitored and detailed analysis may be made concerning the health of
the machine and any faults which may arise or have already arisen. Machinery distress very often
manifests itself in vibration or a change in vibration pattern. Vibration
analysis is therefore, a powerful diagnostic and troubleshooting tool of major
process machinery.On-load monitoring can be performed mainly in the following three ways.(i)
Periodic field measurements with
portable instruments; this method provides information about long-term changes
in the condition of plant. The portable instruments are employed with a high
load factor and can often be placed in the care of only one man. Use of life
curves and the LEO approach assist the decision making.(ii)
Continuous monitoring with permanently
installed instruments; it
is employed when machine failures are known to occur rapidly and when the
results of such failure are totally unacceptable as in the case of turbine
generator units.(iii)
Signature analysis: scientific
collection of information, signals or signatures, diagnosis and detection of
the faults by a thorough analysis of these signatures based on the knowledge
hitherto acquired in the field, and judging the severity of faults for decision
making, all put together, is called signature analysis. The technique involves
the use of electronic instrumentation especially designed for the purpose of
varied capacities, modes of application and design features.Vibration signals are the most
versatile parameters in machine condition monitoring techniques. Periodic
vibration checks reveal whether troubles are present or impending. Vibration
signature analysis reveals which part of the machine is defective and why.
Although a number of vibration analysis
techniques have been developed for this purpose, still a lot of scope is there
to reach a stage of expertise.


## 4.3. Temperature Monitoring

Temperature monitoring consists of
measuring of the operational temperature and the temperature of component
surfaces. Monitoring operational temperature can be considered as a subset of
the operational variables for performance monitoring. The monitoring of
component temperature has been found to relate to wear occurring in machine elements,
particularly in journal bearings, where lubrication is either inadequate or
absent. The techniques for monitoring temperature of machine components can
include the use of optical pyrometers, thermocouples, thermography, and
resistance thermometers.


## 4.4. Acoustic Emission Analysis

Acoustic emission refers to the
generation of transient waves during the rapid release of energy from localized
sources within a material. The source of these emissions is closely associated
with the dislocation accompanying plastic deformation and the initiation or extension
of fatigue cracks in material under stress. The other sources of acoustic
emission are melting, phase transformations, thermal stress, cool-down cracking,
and the failure of bonds and fibers in composite materials. Acoustic emissions
are measured by piezoelectric transducers mounted on the surface of the
structure under test and loading the structure. Sensors are coupled to the
structure by means of a fluid couplant or by adhesive bonds. The output of each
piezoelectric sensor is amplified through a low-noise preamplifier, filtered to
remove any extraneous noise and furthered processed by suitable electronic
equipment.Traditionally, acoustic emissions as
a technique has been restricted to the monitoring of high cost structures due
to the expenses of the monitoring equipment. However, as equipment costs
steadily fall, the range of viable applications expands rapidly. Olsson et al. present
a frame work for fault diagnosis of industrial robots using acoustic signals
and case-based reasoning [12]. This frame work utilizes the case-based
reasoning for fault identification based on sound recording in robot fault
diagnosis. Wue et al. have developed experimental setup for online fault
detection and analysis of modern water hydraulic system [13], and suggested
that the incorporation of wavelet transformation into the analysis of acoustic
emission opens up the door for future research, which can prove to be very
relevant toward condition monitoring. Choe et al. [14] worked on neural pattern
identification of railroad wheel-bearing faults from audible acoustic signals
by comparison of FFT, continuous wavelets transform (CWT) and discrete wavelets
transform (DWT) features.


## 4.5. Noise Analysis

Noise signals are utilized for condition monitoring because noise
signals measured at regions in proximity to the external surface of machines
can contain vital information about the internal processes, and can provide valuable
information about a machine’s running condition. When machines are in a good
condition, their noise frequency spectra have characteristic shapes. As faults
begin to develop, the frequency spectra change. Each component in the frequency
spectrum can be related to a specific source within the machine. This is the
fundamental basis for using noise measurement and analysis in condition
monitoring. Sometimes the signal which is to be monitored is submerged within
some other signal and it cannot be detected by a straightforward time history
or spectral analysis. In this case, specialized signal processing techniques
have to be utilized.


## 4.6. Wear Debris Analysis

It is not possible to examine the working
parts of a complex machine on load, nor is it convenient to strip down the
machine. However, the oil which circulates through the machine carries with it
evidence of the condition of parts encountered. Examination of the oil, any
particle it has carried with it, allows monitoring of the machine on load or at
shutdown. A number of techniques are applied, some very simple, other involving
painstaking tests and expensive equipments. Presently, available lubricant
sampling or monitoring techniques like rotary particles depositor (RPD), spectrophotometer
oil analysis programme (SOAP), Ferrographic oil analysis and recent software
used techniques are available to distinguish between damage debris and normal
wear debris. Every machine ever designed undergoes a process of wear and tear in
operation, yet a battery of modern condition monitoring techniques is available
to monitor this process and trigger preventive maintenance routines which
depend on identifying any problem before it has the chance to develop to the
point of final breakdown. Now recently, engineers have been able to extend
their knowledge of conditions within operating machinery by studying the
particles of metallic debris which can be found in lubricating oil from
engines, gearboxes, final drive units and transmissions, or in hydraulic fluid,
and recording the number, size, and type of these fragments of debris.


## 4.7. Motor Current Signature Analysis

Motor current signature
analysis (MCSA) is a novel diagnostic process for condition monitoring of
electric motor-driven mechanical equipment (pumps, motor-operated valves,
compressors, and processing machinery). The MCSA process identifies,
characterizes, and trends overtime the instantaneous load variations of
mechanical equipment in order to diagnose changes in the condition of the
equipment. It monitors the instantaneous variations (noise content) in the
electric current flowing through the power leads to the electric motor that
drives the equipment. The motor itself thereby acts as a transducer, sensing
large and small, long-term and rapid, mechanical load variations, and
converting them to variations in the induced current generated in the motor
windings. This motor current noise signature is detected, amplified, and
further processed as needed to examine its time-domain and frequency-domain
(spectral) characteristics. Korde [15] demonstrates that the spectrum analysis
of the motors current and voltage signals can hence detect various faults
without disturbing its operation using FFT transformation.


## 4.8. Nondestructive Testing

The principle of nondestructive testing (NDT) is to be able to use the
components or structure after examination. The inspection should not affect the
item involved, and must therefore, be nondestructive. NDT includes many
different technologies, each suitable for one or more specific inspection
tasks, with many different disciplines overlapping or complimenting others.
Thus the best technique(s), for any one application, should be decided by an
expert eddy current testing, electrical resistance testing, flux leakage
testing, magnetic testing, penetrant testing, radiographic testing, resonant
testing, thermographic testing, ultrasonic testing, and visual testing are some
of the different NDT techniques.


## 5. Vibration Signature Analysis

The word signature has been coined to
designate signal patterns which characterize the state or condition of a system
from which they are acquired. Signatures are extensively used as a diagnostic
tool for mechanical system. In many cases, some kind of signal processing is
undertaken on those signals in order to enhance or extract specific features of
such vibration signatures. It is very important to consider the type and range
of transducers used as pickup for capturing vibration signal. Signature-based
diagnostic makes extensive use of signal processing techniques involving one or
more methods to deal with the problem of improvement in the signal to noise
ratio.Vibration-based monitoring techniques
have been widely used for detection and diagnosis of bearing defects for several
decades. These methods have traditionally been applied, separately in time and
frequency domains. A time-domain analysis focuses principally on statistical
characteristics of vibration signal such as peak level, standard deviation,
skewness, kurtosis, and crest factor. A frequency domain approach uses Fourier
methods to transform the time-domain signal to the frequency domain, where
further analysis is carried out, conventionally using vibration amplitude and
power spectra. It should be noted that use of either domain implicitly excludes
the direct use of information present in the other. These techniques have been
broadly classified in three areas, namely, the following.


### 5.1. Time-Domain Analysis

The time domain refers to a display or analysis of the vibration data as a function
of time. The principal advantage of this format is that little or no data are
lost prior to inspection. This allows for a great deal of detailed analysis.
However, the disadvantage is that there is often too much data for easy and
clear fault diagnosis. Time-domain analysis of vibration signals can be
subdivided into the following categories: time-waveform analysis, time-waveform
indices, time-synchronous averaging, negative averaging, orbits, and probability
density moments.


### 5.2. Frequency Domain

The frequency domain refers to a display or analysis of the vibration data as a
function of frequency. The time-domain vibration signal is typically processed
into the frequency domain by applying a Fourier transform, usually in the form
of a fast Fourier transform (FFT) algorithm. The principal advantage of this
format is that the repetitive nature of the vibration signal is clearly
displayed as peaks in the frequency spectrum at the frequencies where the
repetition takes place. This allows for faults, which usually generate specific
characteristic frequency responses, to be detected early, diagnosed accurately,
and trended overtime as the condition deteriorates. However, the disadvantage
of frequency-domain analysis is that a significant amount of information
(transients, nonrepetitive signal components) may be lost during the
transformation process. This information is nonretrievable unless a permanent
record of the raw vibration signal has been made. The various methods of
frequency-domain vibration signature analysis are bandpass analysis, shock
pulse (spike energy), enveloped spectrum, signature spectrum, and cascades (waterfall plots).


### 5.3. The Quefrency Domain

The quefrency is the abscissa for the cepstrum which is defined as the spectrum of the
logarithm of the power spectrum. It is used to highlight periodicities that
occur in the spectrum in the same manner as the spectrum is used to highlight
periodic components occurring in the time domain [16]. One of the ways the expert
system detects bearing tones is by looking at the spectrum of a spectrum. This
process is called cepstrum analysis, “cepstrum” being a play on the
word “spectrum.”


## 5.1. Time-Domain Analysis

The time domain refers to a display or analysis of the vibration data as a function
of time. The principal advantage of this format is that little or no data are
lost prior to inspection. This allows for a great deal of detailed analysis.
However, the disadvantage is that there is often too much data for easy and
clear fault diagnosis. Time-domain analysis of vibration signals can be
subdivided into the following categories: time-waveform analysis, time-waveform
indices, time-synchronous averaging, negative averaging, orbits, and probability
density moments.


## 5.2. Frequency Domain

The frequency domain refers to a display or analysis of the vibration data as a
function of frequency. The time-domain vibration signal is typically processed
into the frequency domain by applying a Fourier transform, usually in the form
of a fast Fourier transform (FFT) algorithm. The principal advantage of this
format is that the repetitive nature of the vibration signal is clearly
displayed as peaks in the frequency spectrum at the frequencies where the
repetition takes place. This allows for faults, which usually generate specific
characteristic frequency responses, to be detected early, diagnosed accurately,
and trended overtime as the condition deteriorates. However, the disadvantage
of frequency-domain analysis is that a significant amount of information
(transients, nonrepetitive signal components) may be lost during the
transformation process. This information is nonretrievable unless a permanent
record of the raw vibration signal has been made. The various methods of
frequency-domain vibration signature analysis are bandpass analysis, shock
pulse (spike energy), enveloped spectrum, signature spectrum, and cascades (waterfall plots).


## 5.3. The Quefrency Domain

The quefrency is the abscissa for the cepstrum which is defined as the spectrum of the
logarithm of the power spectrum. It is used to highlight periodicities that
occur in the spectrum in the same manner as the spectrum is used to highlight
periodic components occurring in the time domain [16]. One of the ways the expert
system detects bearing tones is by looking at the spectrum of a spectrum. This
process is called cepstrum analysis, “cepstrum” being a play on the
word “spectrum.”


## 6. Fault Detection from Vibration Analysis

Renwick and Babson [1] demonstrate that
the predictive maintenance using vibration analysis has achieved meaningful
results in successfully diagnosis machinery problems. The benefits of such
programs include not only evident-cost benefits such as reducing machinery downtime
and production losses, but also the more subtle long-term cost benefits which
can result from accurate maintenance scheduling.source identification and fault
detection from vibration signals associated with items which involve rotational
motion such as gears, rotors and shafts, rolling element bearings, journal
bearings, flexible couplings, and electrical machines depend upon several
factors: (i) the rotational speed of the items, (ii) the background noise and/or vibration level, (iii) the location of the monitoring transducer, (iv) the
load sharing characteristics of the item, and (v) the dynamic interaction
between the item and other items in contact with it.The main causes of mechanical
vibration are unbalance, misalignment, looseness and distortion, defective
bearings, gearing and coupling in accuracies, critical speeds, various form of
resonance, bad drive belts, reciprocating forces, aerodynamic or hydrodynamic
forces, oil whirl, friction whirl, rotor/stator misalignments, bent rotor shafts,
defective rotor bars, and so on. Some of the most common faults that can be
detected using vibration analysis are summarized in Table2 [17].Table 2
Some typical
faults and defects that can be detected with vibration analysis.
ItemFaultGearsTooth messing faults,misalignment,cracked and/or worm teeth,eccentric gearRotors and shaftUnbalanceBent shaftMisalignmentEccentric journalsLoose componentsRubsCritical speedCracked shaftBlade lossBlade resonanceRolling element bearingsPitting of race and ball/rollerSpallingOther rolling elements defectJournal bearingOil whirlOval or barreled journalJournal/bearing rubFlexible couplingMisalignmentUnbalanceElectrical machinesUnbalanced magnetic pullsBroken/damaged rotor barsAir gap geometry variationsStructural and foundation faultsStructural resonancePiping resonanceVortex sheddingWeqerich et al. developed a
nonparametric modeling technique by smart signal and demonstrate the use of
this approach for detecting faults in rotating machinery via extracted features
from vibration signals [18]. Lei et al. [19] present damage diagnosis approach
using time series analysis of vibration signals for structural health
monitoring benchmark problem.Sohn and Farrar [20] have presented a
procedure for damage detection and localization within a mechanical system
solely based on the time series analysis of vibration data. Sahinkaya et al. [21]
have worked on fault detection and tolerance in synchronous vibration control
of rotor magnetic bearing system. A simple and effective algorithm has been
developed to build fault detection and tolerances capabilities into the
open-loop adaptive control of the synchronous vibration of flexible rotors
supported or equipped with magnetic bearings.Lebold et al. [22] have presented
review of vibration-analysis methods for gearbox diagnostics and prognostics.
This review listed some of the most traditional features used for machinery
diagnostics and presented some of the signal processing parameters that impact
their sensitivity.Verma and Balan [23] present a
fundamental study on the vibration behavior of electrical machine stators using
an experimental model analysis and suggested that vibration level even at
resonance can be reduced by designing the electromagnetic forces to have
circumferential mode associated with corresponding resonance. Ocak and Loparo [24]
present algorithms for estimating the running speed and the bearing-key
frequencies of an induction motor using vibration data that can be used for
failure detection and diagnosis. Plenge et al. [25] developed optical
inspection techniques for vibration analysis and defect indication in railway.Vibration signals from gearboxes and
roller bearings share many common characteristics. First, the signals are
usually noisy. This is because the accelerometers for signals collection are mounted
on the outer surface of gearbox. The signals obtained from these accelerometers
include vibrations from meshing gears, bearings, and the equipment’s many other
parts. Second, symptoms from faulty bearings are very similar to those from
faulty gears. For example, periodic impulses may indicate either cracked teeth
of gears or damaged races or rollers of roller bearings. Such periodic
impulses, however, cannot be detected easily with the frequency spectrum
because of the heavy noise distributed in the low-frequency area [4].Lin et al. [4] have obtained
excellent results for mechanical fault detection based on the wavelet denoising
technique. The method has performed excellently when used to denoise mechanical
vibration signals with a low signal-to-noise ratio.


## 7. REB Failure and Its Vibration Analysis

Each of the rolling element bearings used
in industries consists of two rings, one inner and the other outer. A set of
balls or rolling elements placed in raceways rotate inside these rings. Even
when properly applied and maintained, the bearing will still be subjected to
one cause of failure, fatigue of bearing material. Fatigue is the result of
shear stresses cyclically applied immediately below the load-carrying surfaces
and is observed as spalling away of surface metal. However, material fatigue is
not the only cause of spalling. There are causes of premature spalling. So,
although the observer can identify spalling, he must be able to discern between
spalling produced at the normal end of
bearing’s useful life and that triggered by causes found in the three major
classifications of premature spalling as lubrication, mechanical damage, and
material defects. Most bearing failures
can be attributed to one or more of the following causes as defective bearing
seats on shafts and in housings, misalignment, faulty mounting practice,
incorrect shaft and housing fit, inadequate lubrication, ineffective sealing,
and vibration, while the bearing is not rotating, passage of electric current
through the bearing [2]. As one of the most essential parts in rotating
machinery rolling element bearings are often subjected to high stress and
operate under severe conditions, Their integrity becomes an issue particularly
in key machinery. A machine could be seriously jeopardized, if defects occur to
those rolling element bearing during service [5]. A new approach for the
categorization of bearing faults was introduced by Stack et al. [26]. A common
way in which bearing faults are often classified according to the location of
the fault (an inner race/outer race/ball/cage fault). In this research,
bearing faults were grouped into one of two categories, as single point defects
and generalized roughness. The single point defects are defined as visible defects
that appear on the raceways, rolling elements, or cage.A single point defect produces one of
the four characteristics fault frequencies depending on which surface of the
bearing contains the fault. In spite of the name, a bearing can possess
multiple single point defects. The other group of bearing faults, generalized
roughness, refers to an unhealthy bearing whose damage is not apparent to the
unaided eye. Example of this failure mode includes deformation or warping of the
rolling elements or raceways and overall surface roughness due to heating,
contaminated lubricant, or electric discharge machining. The effects produced
by this failure mode are difficult to predict, and there are no characteristics
fault frequencies with this type of fault.In rolling element bearing failure
analysis, the low-frequency phenomenon is the impact caused by a defect of a
bearing. The high-frequency carrier is a combination of the natural frequencies
of the associated rolling element or even of the machine [27]. There are a
number of factors that contribute to the complexity of the bearing signature.
First, variation of bearing geometry and assembly make it impossible to
precisely determine bearing characteristics frequencies. Secondly, locations of
bearing defects cause different behavior in the transient response of the
signal, which is easily buried in wide band response and noisy signals.
Thirdly, signature appears to be very different with the same type of defect at
different stages of damage, severity. Finally, operating speed and loads of the
shaft greatly affect the way and the amount a machine vibrates.Several researchers worked on the
subject of rolling element bearing defect detection and diagnosis through
vibration analysis. Time domain, frequency domain, time-frequency domain based
on short time Fourier transform (STFT) and wavelet transform and advanced
signal processing techniques have been implemented and tested. Time-domain
analysis focuses on dealing directly with the time-domain waveform of vibration
signals. The indices RMS, peak value, and crest factor are often used to
quantify the time signal. The statistical parameters such as kurtosis and
skewness values are robust to varying bearing operating condition and are good
indicators of incipient defects. The disadvantage, however, is that as the
defect spreads across the bearing surfaces the values of these parameters drop
back to normal [28].The frequency domain, spectrum of the
vibration signal reveals frequency characteristics of vibration. If the
frequencies of the impulse occurrence are close to one of the bearing
characteristic frequencies, such as ball pass inner race frequency, ball pass
outer race frequency, ball spin frequencies, it may indicate a defect related
fault in the bearing. Fast Fourier transform is used in conventional frequency-domain
signature analysis techniques for conversion of time-domain signal in frequency-domain
signal. Other frequency-domain techniques are generally used are the calculation
of power spectral density, bandpass analysis, envelope analysis. The
effectiveness of bandpass-analysis method relies on a suitable choice of narrow-band
frequencies around the selected resonance. In envelope analysis, signals are
filtered through bandpass filter and filtered signal is demodulated with the
help of full-wave rectification or via Hilbert transform and then spectrum
analyzed. The passband and envelope analysis techniques are useful to detect
rolling element bearing faults when signals are noisy due to severity of fault
or due to associated noise from other sources as shaft misalignment, unbalance,
and looseness.The fast Fourier transform has
drawback, when signal is nonstationary or noisy, even in FFT, time information
is lost. Many researchers have used short-time Fourier transform (STFT) to
overcome the time information problem but low-resolution problem exists in
STFT. The wavelets transform is currently used to overcome both the time
information and low resolution problems. A major advantage of the wavelets
transform is that this method can exhibit the local features of the signals and
give account of how energy is distributed over frequencies changes from one
instant to the next.The confidence of bearing fault diagnosis
can be improved by using a range of failure indicators including performance
indices, oil analysis, thermography, and motor current readings in conjunction
with vibration analysis. These indicators are generally assimilated and
analyzed by human expert but a computational expert system, based on neural
network, fuzzy logic, and rule based logic, as well as hybrid techniques
containing elements of all three methods,
is being used and continually improved in order to
automate the process.The neural network technology
provides an attractive complement to traditional vibration analysis because of
the potential of neural networks to operate in real-time mode and to handle
data that may be distorted or noisy [29]. Neural networks have proven the ability
in the area of nonlinear pattern classification and can correctly identify the
different causes of bearing vibration [30]. The fuzzy logic has proven ability
in mimicking human decisions, and the bearing fault diagnosis problem has
typically been solved by an experienced engineer. The fuzzy logic is promising
for automation in the area of bearing vibration diagnosis, if the input data is
well processed [31]. The advantages of the fuzzy logic approach include the
possibility to change the linguistic rules into decisions by copying the
procedure and thinking of a human analyzer. The rules that include uncertainty
and inaccuracy are changed into numbers describing the severity or the
probability of fault. The rules and membership functions can be tuned to find
the good sensitivity of the diagnostic system.


## 8. Advanced Signal Processing Techniques in Vibration Analysis

With the development of soft computing
techniques such as artificial neural network (ANN) and fuzzy logic, there is a
growing interest in applying these approaches to the different areas of engineering.
These systems gained popularity over other methods, as they are model free
estimators capable of synthesizing nonlinear and noisy systems. The fuzzy logic
was developed as a mean for representing, manipulating, and utilizing uncertain
information (information that is usually expressed in linguistic terms). The
recent surge of interest is in merging or combining NN and fuzzy logic system
into a functional system to overcome their individual weaknesses.Wavelet analysis is an emerging field
of mathematics that has provided new tool and algorithms for the type of
problems encountered in process monitoring. Wavelet transform (WT) is a
mathematical approach that decomposes a time-domain signal into different
frequency groups. Wavelet algorithms process data at different scales and
resolutionsThe monitoring and diagnosis of
machinery is a well-established discipline, but much progress remains to be
made in automating diagnosis as well as developing low-cost reliable
technologies which can be applied cost-effectively in the majority of
production environment. Developments in microtechnology and artificial
intelligence have driven the trends toward more extensive onboard diagnostics.
Recent systems have relied on artificial intelligence techniques to strengthen
the robustness of diagnostics systems. Four artificial techniques have been
widely applied as expert system, neural networks, fuzzy logic, and model-based
systems [9]. Different kinds of artificial intelligence method have become
common in fault diagnosis and condition monitoring. For example, fuzzy logic
and neural networks have been used in modeling and decision making in
diagnostics schemes. Neural networks-based classifications are used in
diagnosis of rolling element bearings.Shikari and Sadiwala worked on automation in condition-based
maintenance using vibration analysis [32]. In this work, importance of
intelligent system in CBM is focused. Dyke [33] describes an example of the
application of the DLI engineering ExpertAlert expert automated diagnostics system to successful diagnosis of machine tool
spindle bearing problems. Sima [34] proposes a strictly neural expert system
architecture that enables the creation of the knowledge base automatically by
learning from example inferences. Bandyopadhya et al. [35] have developed an
expert system for real-time condition monitoring using vibration analysis for
turbine bearing. Poyhonen et al. [36] have applied support vector classification
to fault diagnostics of an electrical machine.Zhenya et al. proposed a multilayer
feed forward network-based machine state identification method. They represent
certain fuzzy relationship between the fault symptoms and causes, with highly
nonlinearity between the input and the output of the network [37]. The rolling
element bearing signals are investigated accordingly to the principle that the
wavelet can extract the signal envelope by Jun and Liao. A wavelet-based self-information
extracting envelope method was applied, application of the method demonstrates
that the method is effective to extract the rolling bearing signal envelope and
is useful to analysis the bearing faults [27].Four approaches based on bispectral
and wavelet analysis of vibration signals are investigated as signal processing
techniques for application of a number of induction motor rolling element
bearing faults by Yang et al. [38]. A general methodology for machinery fault
diagnosis through a pattern recognition technique is developed by Sun et al. [28],
this involves data acquisition, feature extraction, mapping for feature fusion,
and piecewise-linear classification and diagnosis. They conclude, to increase
the sensitivity and reliability of pattern recognition, one is encouraged to
include as many feature parameters as possible without concern the redundancy
or numerical singularities.Satish and Sharma [39] demonstrate a
novel and cost-effective approach for diagnosis and prognosis of bearing faults
in small and medium-size induction motor. In this work, a fuzzy back-propagation
network was developed by combining neural network with fuzzy logic to identify
the present condition of the bearing and to estimate the remaining life of the
motor.Fan and Zuo [40] proposed an effective
method to extract modulating signal and to detect the early gear fault. In this
new fault detection method, combination of Hilbert transform and wavelet-packet
transform were used. Both simulated signals and real vibration signals
collected from a gearbox dynamics simulator were used to verify the proposed
methodDuraisamy et al. [41] have described
a comparative study of membership functions for design of fuzzy logic fault
diagnosis system for single-phase induction motor.Intelligent systems cover a wide
range of techniques related to hard science such as modeling and control
theory, and soft science such as the artificial intelligence. Intelligent
systems, including neural networks, fuzzy logic, and wavelet techniques,
utilize the concepts of biological systems and human cognitive capabilities.
These three systems have been recognized as a robust and alternative to some of
the classical modeling and control methods [24].


## 9. Conclusions

In this paper, authors have been presented a brief review of 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. After the review of literature on
machine fault signature analysis, the following points are concluded.(i)
Prevention of potential failure
is required for reliable and safe operations of machineries and the prevention
of catastrophic failure can be done by appropriate maintenance. Condition-based
maintenance is the best suitable technique to avoid unwanted futuristic
failures through condition monitoring or signature analysis for rotating
machineries. Vibration signature analysis is the best suitable technique
available for fault identification.(ii)
Among all machine components
rolling contact bearing is needed more attention towards signature analysis.
The lot of scope is available in bearing fault signature analysis through
vibration data for multiple points or generalized faults.(iii)
Vibration analog signal can be
converted in discrete data for further investigation and various time-domain
and frequency-domain features can be used for further investigations. The
Hilbert and wavelets transform have tremendous scope in machine fault signature
analysis.(iv)
Expert system based on ANN and fuzzy
logic can be developed for robust fault categorization with the use of
extracted features from vibration signal.These conclusions motivate further
research to incorporate other parameters and symptoms with vibration features
to develop more robust expert systems for machine faults signature analysis.



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*Source: 583982-2008-03-13.xml*