CONDITION MONITORING AND FAULT DIAGNOSIS OF ELECTRICAL MACHINES - A REVIEW

Subhasis Nandi Student Member, IEEE

Hamid A. Toliyat Senior Member, IEEE

Electric Machines & Power Electronics Laboratory Department of Elecuical Engineering

Texas A&M University College Station, TX 77843-3128

Abstract-Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical machines. Like adjustable speed drives, fault prognosis has become almost indispensable. The manufacturers of these drives are now keen to include diagnostic features in the software to decrease machine down time and improve salability. Prodigious improvement in signal processing hardware and software has made this possible. Primarily, these techniques depend upon locating specific harmonic components in the line current, also known as motor current signature analysis (MCSA). These harmonic components are usually different for different types of faults. However with multiple faults or different varieties of drive schemes, MCSA can become an onerous task as different types of faults and time harmonics may end np generating similar signatures. Thus other signals such as speed, torque, noise, vibration etc., are also explored for their frequency contents. Sometimes, altogether different techniques such as thermal measurements, chemical analysis, etc., are also employed to find out the nature and the degree of the fault. Going by the present trend, human involvement in the actual fault detection decision making is slowly being replaced by automated tools such as expert systems, neural networks, fuzzy logic based systems; to name a few. It is indeed evident that this area is vast in scope. Hence, keeping in mind the need for future research, a review paper describing different types of faults and the signatures they generate and their diagnostics’ schemes, will not be entirely out of place. In particular, such a renew helps to avoid repetition of past work and gives a bird’s eye-view to a new researcher in this area.

1. INTRODUCTION

The history of fault diagnosis and protection is as archaic as the machines themselves. The manufacturers and users of electrical machines initially relied on simple protections such as over-current, over-voltage, earth-fault, etc. to ensure safe and reliable operation. However, as the tasks performed by these machine grew increasingly complex; improvements were also sought in the field of fault diagnosis. It has now become very important to diagnose faults at their very inception; as unscheduled machine downtime can upset deadlines and cause heavy financial losses.

The major faults of electrical machines can broadly be classified as the following [l]: a) Stator faults resulting in the opening or shorting of one

or more of a stator phase winding, b) Abnormal connection of the stator windings,

c) Broken rotor bar or cracked rotor end-rings, d) Static and /or dynamic air-gap irregularities, e) Bent shaft (akin to dynamic eccentricity) which can

result in a rub between the rotor and stator, causing serious damage to stator core and windings, Shorted rotor field winding , and f)

g) Bearing and gearbox failures.

given below: a) Unbalanced air-gap voltages and line currents, b) Increased torque pulsations, c) Decreased average torque, d) Increased losses and reduction in efficiency, and e) Excessive heating.

The diagnostic methods to identify the above faults may involve several different types of fields of science and technology. They can be described as L1-21: a) Electromagnetic field monitoring, search coils, coils

wound around motor shafts (axial flux related detection), b) Temperature measurements, c) Infrared recognition, d) Radio frequency (RF) emissions monitoring, e) Noise and vibration monitoring, f) Chemical analysis, g) Acoustic noise measurements, h) Motor current signature analysis (MCSA), i) Model, artificial intelligence and neural network based

techniques. Of the above types of faults i) bearing, ii) the stator or

armature faults, iii) the broken rotor bar and end ring faults of induction machines and iv) the eccentricity related faults are the most prevalent ones and thus demand special attention. Thus, these faults and their diagnosis techniques will be discussed briefly in the next section. A brief introduction to fault detection using artificial intelligence. (AI) techniques has also been included.

These faults produce one or more of the symptoms as

U. VARIOUS -PIS OF FAULTS AND THEIR DETECTION TECHNIQUES

A. Bearing faults

The majority of the electrical machines use ball or rolling element bearings. Each of these bearings consists of two

0-7803-5589-X/99/$10.00 Q 1999 IEEE 197

rings, one inner and the other outer. A set of balls or rolling elements placed in raceways rotate inside these rings [Z]. Even under normal operating conditions with balanced load and good alignment, fatigue failures may take place. These faults may lead to increased vibration and noise levels. Flaking M spalling of bearings might occur when fatigue causes small pieces to break loose from the bearing.

Other than the normal internal operating stresses, caused by vibration, inherent eccentricity, and bearing currents [39] due to solid state drives, bearings can spoiled by many other external causes such as a) Contamination and corrosion caused by pitting and

sanding action of hard and abrasive minute particles or corrosive action of water, acid etc.

b) Improper lubrication; which includes both over and under lubrication causing heating and abrasion.

c) Improper Installation of bearing. By improperly forcing the bearing onto the shaft or in the housing (due to misalignment) indentations are formed in the raceways (brinelling).

Though almost 4040% of all motor failures is bearing related, very little has been reported in literature regarding bearing related fault detection. Bearing faults might manifest themselves as rotor asymmetry faults [Z], which are usually covered under the category of eccentricity related faults. Otherwise, the ball bearing related defect? can be categorized as [I] outer bearing race defect, inner bearing race defect, ball defect and train defect and the vibration frequencies to detect these faults are given by,

for an outer bearing race defect

for an inner bearing race defect

for a ball defect

f ,[HzI = ( N I 2) f,U - bd COS(P 1 1 d,I

f,[HzI= ( N / 2 ) f , [ I + bd COSV ) d, 1

f,[ffzI= dpf, Ib,(l-[b, COS(P ) /d,IZ)

fJffzI= (f, /2) f , [1-bd c W P ) I d p ] for a train defect (1) where f, is the rotational frequency, N is the number of balls, bd and dpare the ball diameter and ball pitch diameter

respectively, and p is the contact angle of the ball (with the races).

Schoen et. al. [3] have shown that these vibration ffequencies reflect themselves in the cunent spectrum as

f b n g = If, + m. f” I (2) where m = 1,2,3 ,... and f, is one of the characteristic vibration frequencies. However, the experimental results were presented for rather extensive bearing damages (such as hole in the outer race of the bearing; brinelling induced by a vibration table). The implementation of an unsupervised on- line detection of these faults using artificial neural networks has also been described in [41.

Yazici et.al. [5] have reported of an adaptive, statistical time ffequency method for detection of bearing faults. Experiments were conducted on defective bearings with scratches on the outer races and bearing balls and cage defects. It has been claimed that all defective measurements were correctly classified as defective. However, the detection procedure required extensive training for feature extraction.

B. Stator or armature faults

These faults are usually related to insulation failure. In common parlance they are generally known as phase-to- ground or phaseto-phase faults. It is believed that these faults start as undetected turn-to-turn faults which finally grow and culminate into major ones 161. Almost 30-40 % of all reported induction motor failures falls in this category [6].

Armature or stator insulation can fail due to several reasons. Primary among these are [Z] a) High stator core or winding temperatures. b) Slack core lamination, slot wedges and joints. c) Loose bracing for end winding. d) Contamination due to oil, moisture and dirt. e) Short circuit or starting stresses. 0 Electrical discharges. g) Leakage in cooling systems.

There are a number of techniques to detect these faults. Penman et. al. [7] were able to detect turn to turn faults by analyzing the axial flux component of the machine using a large coil wound concenuically around the shaft of the machine. Even the fault position could be detected by mounting four coils symmetrically in the four quadrants of the motor at a radius of about half the distance from the shaft to the stator endwinding. The frequency components to detect in the axial flux component is given by,

(3) where p is the number of pole pairs, f i s the mains frequency, k = 1,3 and n = 1,2,3,. . .,,(2p-l) and s is the slip.

Toliyat and Lip0 [8] have shown through both modeling and experimentation that these faults result in asymmetry in the machine impedance causing the machine to draw unbalance phase currents. This is the result of negative sequence currents flowing in the line as also have been shown in [ 9 ] . However, negative sequence cunents can also be caused by voltage unbalance, machine saturation etc. Kliman et.al. [6] model these unbalances which also includes instrument asymmetries. It is reported that with these modifications it is possible even to detect a one turn ‘bolted‘ fault out of a total 648 turns. Statistical process control (SPC) techniques have also been applied to detect stator faults 1301.

C. Broken rotor bar and end ring faults

(k f n(1- s) / p ) f

Unlike stator design, cage rotor design and manufacturing has undergone little change over the years. As aresult rotor

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failures now account for around 5.10% of total induction motor failures (Bonnett [lo], Kliman [6, 111).

Cage rotors are of two types: cast and fabricated. Previously, cast rotors were only used in small machines. However, with the advent of cast ducted rotors; casting technology can be used even for the rotors of machines in the range of 3000 kW. Fabricated rotors are generally found in larger or special application machines. Cast rotors though more rugged than the fabricated type, can almost never be repaired once faults like cracked or broken rotor bars develop in them.

The reasons for rotor bar and end ring breakage are several. They can be caused by a) Thermal stresses due to thermal overload and unbalance,

hot spots or excessive losses, sparkjng (mainly fabricated rotors),

b) Magnetic stresses caused by electromagnetic forces, unbalanced magnetic pull, electromagnetic noise and vibration,

c) Residual stresses due to manufacturing problems, d) Dynamic stresses arising from shaft torques, centrifugal

forces and cyclic stresses, e) Environmental stresses caused by for example

contamination and abrasion of rotor material due to chemicals or moisture, Mechanical saesse due to loose laminations, fatigued parts, bearing failure etc.

Kliman [11], Thomson [12], Filippetti [13], Elkasabgy [14] used spectrum analysis of machine line current (MCSA) to detect broken bar faults. They investigate the sideband components,f,, around the fundamental for detecting broken bar faults.

While the lower sideband is specifically due to broken bar, the upper sideband is due to consequent speed oscillation. In fact, [13] shows that broken bars actually give rise to a sequence of such sidebands given by

f)

fb = (1 f 2s) f (4)

fb = (1kZks)f , k=l,2, 3, ... (5) The motor-load inertia also affects the magnitude of these

sidebands. Other spectral components that can be observed in the stator line current is given by 1111 and Gaydon [15]

where, fb = detectable broken bar frequencies; Up = 1,3,5.. Elkasabgy 1141 has also shown that broken bar faults can

also be detected by time and frequency domain analysis of induced voltages in search coils placed internally around stator tooth tip and yoke and externally on motor frame. The frequency components are given by (6) with k = l . Torque and speed signals also contain 2s’ and 4$ frequency components with broken rotor bars [13-14].Following the works of Penman [16], detection of these faults are also possible by frequency domain analysis of shaft flux or more generally axial leakage flux which is monitored by using an

external search coil wound around the shaft of a machine. The frequency components are still given by (6)with k =1,2,3. Modeling of rotor bar and end ring faults have been described in [8]. Broken bar detection using state and parameter estimation techniques have also been reported [26]. However the current spectrum and the parameter estimation approach have been compared and the former has been found more efficient [U].

As suggested in 131-321, presence of interbar currents in

n -40.

- .WO- 0 50 1W 300 350 4 w

Frequency (Hz) Frequency (Hz)

0 5 l o 8.5-

6 6.2 6.4 Time (Secs.) Frequency (Hz)

;r 182.4 0 I ; 182.2 L - l w a v)

-150 18’6 6.1 6.2 0 5 10

Time (Secs.) Frequency (Hz)

182.6r 0, - f 1 8 2 . 4 H 8.6Ou 0 I ; 182.2 L - l w a v)

18’6 6.1 6.2 0 5 10 Time (Secs.) Frequency (Hz)

Pi@ Simulated plots of normalized line cunenl specIra around fundamental and 5’ and 1’” lime harmonic (lop IOW), torque and its specua (middle IOW), speed and ils spectra @atom row) with two bars paaially broken. Slip = 0.033.

10

- 5 8 E O = z -5 1181.6

v1 -10 ’ 6 6.1 6.2 6 6.1 6.2

Time(Secs.)

-1W -50u - 3 -50 0 U1 0

-lW

0 50 1W 0 5 10 -150

Frequency (Hz) Fig 2. Simulated plots of line current and speed (top row) and their

normalized spectra (bottom row) for the IWO end “ngs panially broken. Slip = 0.036.

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uninsulated rotor cages, where the contact between the rotor core and the bars are good, might make broken bar detection difficult.

Fig.1 and Fig.:! show simulated current, speed and torque waveforms and their related spectra with two partially broken bars and two partially broken end ring faults for a 3ph, 3hp,

-201 i -40

0 2 -60 -80

-100 30 40 50 60 70 80 90

-20

-40 0 2 -60

-80

-100

-

30 40 50 60 70 80 90

-401 II 1 0 2 -60

-80

-100 30 40 50 60 70 80 90

-20

Q -40 0 2 -60

-80

-100 30 40 50 60 70 80 90

Frequency (Hz) Expcrimental plots of nord ized line cul~ent spectra of healthy

machine (top) and with two (middle) and four bars partially broken (bottom). Slip = 0.033. These and the four subsequent plots are obtained from a machine that is similar to the one simulated. Faults were introduced by drilling the bar*.

Fig 3.

350 4w 450

-40 h A - 0 -60 n a -60 (0

-1 w 3 w 350 4w 450

Frequency (Hz)

Fig 4. Experimental plots of normalized line arrent spectra of healthy machine (top) and with four bars broken @ottom) around the 5' and 7' time harmonics. Slip = 0.033.

Frequency (Hz)

Pig 5. (top) and with four bars broken @mom). Slip = 0.033.

Experimental plots of normalized speed spectra of healthy machine

60 Hz, 4 pole, skewed 44 rotor bar induction motor. The current components given by (4-6) and the 2sf and 4sf speed related components can be clearly seen in the plots.

However, in practice, the current sidebands around fundamental may exist even when the machine is healthy, as can be seen in Fig 3. This could be due to uneven rotor bar resistance because of the die casting process, rotor asymmetry etc. Also components given by (5) may not show any marked change (Fig. 4). Hence, at least for small motors, it may be worthwhile to confm the presence of broken bars through the speed spectra Ojig.5).

D. Eccentriciry related faults

Machine eccentricity is the condition of unequal air-gap that exists between the stator and rotor ([l], Cameron 1171). When eccentricity becomes large, the resulting unbalanced radial forces (also known as unbalanced magnetic pull or UMP) can cause stator to rotor rub, and this can result in the damage of the stator and rotor. There are two types of air-gap eccentricity: the static air-gap eccentricity and the dynamic air gap eccentricity. In the case of the static air-gap eccentricity, the position of the minimal radial air-gap length is fixed in space. Static eccentricity may be caused by the ovality of the stator core or by the incorrect positioning of the rotor or stator at the commissioning stage. If thz rotor-shaft assembly is sufficiently stiff, the level of static eccentricity does not change.

In case of dynamic eccentricity, the center of the rotor is not at the center of the rotation and the position of minimum air-gap rotates with the rotor. This misalignment may be caused due to several factors such as a bent rotor shaft, bearing wear or misalignment, mechanical resonance at critical speed, etc. Dynamic eccentricity in a new machine is controlled by the total indicated reading (TIR) or "run-out" of

200

the rotor (Thomson, [191). An air-gap eccentricity of up to 10% is permissible. However, manufacturers normally keep the total eccentricity level even lower to minimize UMP and to reduce vibration and noise ,

In reality, both static and dynamic eccentricities tend to co- exist. An inherent level of static eccentricity exists even in newly manufactured machines due to manufacturing and assembly method, as has been reported by Dorrell [18]. This causes a steady UMP in one direction. With usage, this may lead to bent rotor shaft, bearing wear and tear etc. This might result in some degree of dynamic eccentricity. Unless detected early, these effects may snowball into stator to rotor hub causing a major breakdown of themachine [19].

The presence of static and dynamic eccentricity can be detected using MCSA 11,171. The equation describing the frequency components of interest is

(7)

where nd = 0 in case of static eccentricity, and nd = 1,2,3.,. in case of dynamic eccentricity ( n d is hown as eccentricity order), f is the fundamental supply frequency, R is the number of rotor slots, s is the slip, p is the number of pole pairs, k is any integer, and Y is the order of the stator time harmouics that are present in the power supply driving the motor ( Y = t 1, + 3, ? 5 , etc.). In case one of these harmonics is a multiple of three, it may not exist theoretically in the line current of a balanced three phase machine. However it has been shown by Nandi [20], Ferrah [21] that only a particular combination of machine pole pairs and rotor slot number will give rise to significant only static or only dynamic eccentricity related components. This relationship for a 3ph, integral slot, 60 desee phase belt machine is given by:

(8) where m f q = O,1,2,3 ,... and r = Oor 1, k = 1. Equation (8) assumes only the fundamental eccentricity component in the permeance or inverse air-gap function [21,43].

It may also be possible to detect these components in other machines, for example for those matching (8) with k = 2 . However, some of these components are noticeable only under light load conditions.

Simulated results with a 4 pole, skewed, 43 rotor slot machine, which conforms to (8) with k = 1 are given in Fig. 6 . Similar results with a 4 pole, skewed, 42 rotor slot machine ( k = 2) are given in Fig.7. The effects of eccentricity on frequency components given by (7) seem to be much less pronounced for this machine.

It has also been ascertained that machines generating principal slot harmonics (PSH) will not give rise to these components with only static or only dynamic eccentricity. The pole pairs and rotor slot numbers for these machines (3ph, integral slot, 60 degree phase belt) are related by:

R = 2 p [ 3 ( m f q ) k r ] i . k

R =2p[3(mi.q)+r] (9)

where mlt q = O,1,2,3 ,... and r = 0 or 1 However, if both static and dynamic eccentricities exist

together, low frequency components near the fundamental [18,22] given by

can also be detected for all machines (Fig.8). These low frequency components also give rise to high frequency components as described by (7). However, these components are strong only for machines (Fig.9) whose pole pairs and rotor slot numbers are given by (8) ( k = 1 ) and (9). For machines described by (8) with k = 2 they are rather weak (Fig.10).

Modeling based approaches to detect eccentricity related components in line current have been described in [20,22]. The simulation results obtained through the models are also well supported by permeance analysis and experimental results. Fig.11 shows the experimental results for a similar, skewed, 4 pole machine with R=44 and a nominal 38.46% static and inherent dynamic eccentricity. The low frequency sidebands that are present even under healthy condition did not show appreciable change once eccentricity was introduced. However, the high frequency components showed moderate (around 5dB) increase.

Vibration signals can also be monitored to detect eccentricity related faults. The high frequency vibration components for static or dynamic eccentricity are given by [17] using an equation similar to (7) (only the values of nd and V are different). In case of mixed eccentricity, the low frequency stator vibration components are given by,

f, = I f +kf,I , k = 1 , 2 , 3 ... (10)

f" =2f +f, (11) Time stepping finite element methods have been employed

recently to compare simulated results with experimentally obtained static eccentricity components in line currents [19]. Static eccentricity has also been modeled using Winding Function Approach [36].

Other approaches, such as monitoring the stator voltage and current park's vector (Cardoso [Z3]) to detect eccentricity in induction motor, can also be found in literature. Toliyat and Al-Nanim [24] have provided simulation and experimental results for synchronous machines with dynamic eccentricity related faults.

III. ARTIFICIAL. INTELLIGENCE (AI) BASED MACHINE CONDITION MONITORING AND FAULT DIAGNOSIS

In the recent past artificial neural networks (ANN), fuzzy or neuro-fuzzy systems are being extensively used for speed, torque estimation, solid state drive control of both dc and ac machines [41]. However, they are particularly suited for ac machines' applications where the relationships between motor current and speed are non-linear.

These AI techniques are now being increasingly used for condition monitoring and fault detection of machines [4,13,41]. A neural net based fault diagnosis system utilizing

201

the stator current spectra is described in Fig 12. The preprocessor extracts the fiequency components of the sampled current data. Using the rule based frequency filters these frequency components are classified into four categories with decreasing level of importance. Based on these rules, a neural network, which has been trained for all possible operating condition oi the machine, is used to classify the incoming data. A spectral signature that falls outside the trained clusters are marked as potential motor fault. In order to prevent false diagnosis, the postprocessor sends an alarm only when fault signatures are observed persistently. This function is performed by maintaining a time history of the motor being monitored. Such a scheme has been successfully implemented [4] to diagnose bearing and. unbalanced rotor faults of induction motors.

-601

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v) - - 1 ~

-120 1200 1250 1300 1350 1400 1450

-40 I I .60t

e -80 DEC

I , ,.I , , ,..

-120p ' 1200 1250 1300 1350 1400 1450

Frequency (Hz)

Fig.6 Simulated normalized plots of the line curmot spctra of a 3ph, 3hp, 60 Hz induction motor with 38.46% Static (top) and 20% dynamic eccentricity (bottom) with Ips; R=43. Slip =0.029.

$ SEC .^ -lW

-120

L

1300 1150 1200 1250

- DEC2

-60 x } DECI 1 x .1W c

-120

1100 1150 1200 1250 1300 1350 1400

Frequency (Hz)

Rg.7 Simulated normalized plots of the line current spema of a 3ph, 3hp, 60 Hz induction mota with 38.46% &tic with Slip= 0.029 (top) and 40% dynamic eccentricity and Slip =0.00467 (bottom) with 2 e ; R=42. The other static eccentricity component is suppressed due to loading effects.

Similarly, fuzzy logic based systems has been used [41] to classify broken bar related faults by categorizing the two sideband components (4) around the fundamental component of the induction motor line cunent by a set of nine rules. Denoting the sidebands as A1 and A2 which are the two inputs of the system and n, the number of broken bars as the output of the system, an example of t h se rules is " If AI is

0

- -50

U) 0.

0 50 1W 150 - lW

fundamental 0

4 f+f' ft21r I O

- -50

0 a

-100 0 50 100 150

Frequency (Hz)

Fig.8 Simulated, normalized line current spedra of 3ph, 3hp, 60 Hz, skewed, 4 pole induction motors with different rotor slats and identical mined eccentricity (SE =38.46%, DE=20%) machine around fundamental. From top to bottomR=44,43,42. Slip=O.O29.

. I 8 , ,

1200 1250 1300 1350 1400 1450

-120t1 1200 1250 1300 1350 1400 1450

Frequency (Hz)

Pig9 Simulated, normalized high frequency line current spectra of 3ph, 3hp, 60 Hz, 4 pole induction motors with different rotor slots and identical mixed eccenllicity (SE ~38.46% DE=20%) machine. From top to bottom R 4 4 , 43. Slip=0.029.

202

1[0.5(R.1)(1-~)-1]

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r Preprocessor FFT & Averaging 4

- n Ln .1W n

.110

Rule-B ased Frequency

Filters

-120’ I

fI0.5(R+l)(l-s)+ll 1 1100 1120 1140 1160 1180 1200

Postprocessor Time History and Alarms

.I20 1200 1250 1300 1350 1400 1450

Frequency (Hz)

Fig.10 Simulated, normlimed high frequency line cumen1 spectra of 3ph, 3hp, 60 Hz, 4 pole induction motors with 42 rotor slots and mixed eccentricity (SE =38.46%, DII;ZO%) machine. Sli0;0.029.

Clutsering Neural Network

Algorithm

0

m ̂p -50 n 2 -lW -

0 50 1W 150

P s n -401 I

1300 1350 1400 1450

Psn -40 I 0

-60 - 8 -50 -80 8 -100 n -1W -

0 50 1W 150 -120 I

1300 1350 1400 1450

small and A2 is large n equals one broken b d ’ . The fuzzy logic system considered is the Mamdani type. The fuzzy inference is performed by using the fuzzy implication min- max methods and the centroid defuzzification technique is used. The membership functions for AI and A2 are small, medium and large. Other examples of motor fault detection using neural networks and fuzzy logic techniques can be found in [42].

IV. CONCLUSIONS

A brief review of bearing, stator, rotor and eccentricity related faults and their diagnosis has been presented in this paper. It is clear kom various literatures that non-invasive motor current signature analysis (MCSA) is by far the most preferred technique to diagnose fault. However, theoretical analysis and modeling of machine faults are indeed necessary to distinguish the relevant frequency components tiom the others that may be present due to time harmonics, machine saturation, etc. Other techniques for fault detection such as axial flux based measurements, vibration analysis, etc. have also been discussed. A section on automated fault detection has also been included.

ACKNOWLEDGMENT

This material is based in part upon work supported by the Texas Advanced Research Program under Grant No. 95-PO83 and by the Department of Energy under Grant No. DE-FG07- 98ID13641.

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