Current signature analysis

Numerous applications of using MCSA in equipment health monitoring have been published among the nuclear-generation, industrial, defense industries. In most applications, stator current is monitored for diagnosis of different faults of induction motor. Randy R. Schoen et. al. [29] addressed the application of motor current signature analysis for the detection of rolling-element bearing damage in induction machines. This study investigates the efficacy of current monitoring for bearing fault detection by correlating the relationship between vibration and current frequencies caused by incipient bearing failures. In this study, the bearing failure modes are reviewed and the characteristic bearing frequencies associated with the physical construction of the bearings are defined. The effects on the stator current spectrum are described and the related frequencies determined. Experimental results which

are used to verify the relationship between the vibrational and current frequencies. The test results clearly illustrate that the stator current signature can be used to identify the presence of a bearing fault.

Randy R. Schoen [30] presented a method for on-line detection of incipient induction motor failures which requires no user interpretation of the motor current signature, even in the presence of unknown load and line conditions. A selective frequency filter learns the characteristic frequencies of the induction machine while operating under all normal load conditions. The generated frequency table is reduced to a manageable number through the use of a set of expert system rules based upon the known physical construction of the machine. This list of frequencies forms the neural network clustering algorithm inputs which are compared to the operational characteristics learned from the initial motor performance. This only requires that the machine be in “good” operating condition while training the system. Since a defect continues to degrade the current signature as it progresses over time, the system looks for those changes in the original learned spectra that are indicative of a fault condition and alarms when they have deviated by a sufficient amount. The combination of a rulebased (expert system) frequency filter and a neural network maximizes the system’s ability to detect the small spectral changes produced by incipient fault conditions. Compete failure detection algorithm was implemented and tested. An impending motor failure was simulated by introducing a rotating mechanical eccentricity to the test machine. After training the neural network, the system was able to readily detect the current spectral changes produced by the fault condition.

Schoen and Habetler [31-32] investigated the effects of a position-varying load torque on the detection of air gap eccentricity. The torque oscillations were found to cause the same harmonics as eccentricity. These harmonics are always much larger than eccentricity-related fault harmonics. Therefore, it was concluded that it is impossible to separate torque oscillations and eccentricity unless the angular position of the eccentricity fault with respect to the load torque characteristic is known.

Randy R. Schoen and Thomas G. Habetler [33] presented an analysis of the effects of position-varying loads on the current harmonic spectrum. The load torque-induced harmonics were shown to be coincidental with rotor fault-induced harmonics when the load varies synchronously with the rotor position. Furthermore, since the effect of the load and fault on a

single stator current harmonic component is spatially dependent, the fault induced portion cannot be separated from the load portion. Therefore, any on-line detection scheme which measures the spectrum of a single phase of the stator current must rely on monitoring those spectral components which are not affected by the load torque oscillations.

John S. Hsu [18] suggested a method to monitor defects such as air gap eccentricity, cracked rotor bars and the shorted stator coils in induction motors. Air-gap torque can be calculated while the motor is running. No special down time for measurement is required. Data of the air-gap torque for a motor kept periodically for comparison purposes. Since more data than just a line current are taken, this method offers other potential possibilities that cannot be handled by examining only a Line current. Experiments conducted on a 5-hp motor showed the validity and potential of this approach.

Hamid A. Toliyat et. al. [34] developed a new induction machine model for studying static rotor eccentricity. It is based directly on the geometry of the induction machine and the physical layout of all windings. The model can simulate the performance of induction machines during transients as well as at steady state, including the effects of static rotor eccentricity. Since the dynamic model of the motor includes the mechanical equation, any arbitrary time function of load torque can be specified from which the resulting stator current is calculated. To illustrate the utility of this method, a conventional three phase induction motor with 50% rotor eccentricity was simulated. Digital computer simulations have been shown to yield satisfactory results which are in close agreement with experimental results of previous studies.

Stanislaw F. Legowski et. al. [35] has been demonstrated that the instantaneous electric power, proposed as a medium for signature analysis of induction motors, has definite advantages over the traditionally used current. The characteristic spectral component of the power appears directly at the frequency of disturbance, independently of the synchronous speed of the motor. This is important in automated diagnostic systems, in which the irrelevant frequency components, i.e. those at multiples of the supply frequency, are screened out.

Randy R. Schoen and Thomas G. Habetler [36] presented a method for removing the load torque effects from the current spectrum of an induction machine. They found that previously presented schemes for current-based condition monitoring ignore the load effect

or assume that it is known. Therefore, a scheme for determining machine health in the presence of a varying load torque requires some method for separating the two effects. This is accomplished by comparing the actual stator current to a model reference value which includes the load effects. The difference between these two signals provides a filtered quantity, independent of variations of the load that allows continuous on-line condition monitoring conducted without concern for the load condition. Simulation results showed the effectiveness of this model reference estimation scheme at removing the load torque effects from the monitored spectra. Experimental results illustrated the feasibility of the proposed system. They demonstrated that the characteristic spectral components are present in the difference current and that the load effects can effectively be removed from the monitored spectrum to improve their detectability.

M.E.H. Benbouzid and H. Nejjari et. al. [37] stated that preventive maintenance of electric drive systems with induction motors involves monitoring of their operation for detection of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of the system. Intensive research effort has been for sometime focused on the motor current signature analysis. This technique utilizes the results of spectral analysis of the stator current. Reliable interpretation of the spectra is difficult, since distortions of the current waveform caused by the abnormalities in the drive system are usually minute. Their investigations show that the frequency signature of some asymmetrical motor faults can be well identified using the Fast Fourier Transform (FFT), leading to a better interpretation of the motor current spectra. Laboratory experiments indicate that the FFT based motor current signature analysis is a reliable tool for induction motor asymmetrical faults detection.

W. T. Thomson et. al. [38] presented an appraisal of on-line monitoring techniques to detect airgap eccentricity in three-phase induction motors. On-line current monitoring is proposed as the most applicable method in the industrial environment. The analyses of the current spectra for different motors are presented in the study. The results verify that the interpretation of the current spectrum proposed in this study was successful in diagnosing airgap eccentricity problems.

Birsen Yazıcı and Gerald B. Kliman [39] discussed an adaptive time–frequency method to detect broken bar and bearing defects. It was shown that the time–frequency spectrum reveals the properties relevant to fault detection better than the Fourier spectrum in

the transform domain. The method is based on a training approach in which all the distinct normal operating modes of the motor are learned before the actual testing starts. This study suggests that segmenting the data into homogenous normal operating modes is necessary, because different operating modes exhibit different statistical properties due to non stationary nature of the motor current. Overlooking this fact will deteriorate the performance of the detection. The result of this study showed that signals from faulty motors are several hundred standard deviations away from the normal operating modes, which indicates the power of the proposed statistical approach. Finally, it was suggested that the proposed method is a mathematically general and powerful one which can be utilized to detect any fault that could show up in the motor current.

Jafar Milimonfared et. al. [40] presented a new method for detecting broken-rotor-bar faults by analyzing the stator-induced voltage after removing the mains. The method is attractive because source non-idealities like unbalance time harmonics will not influence the detection. Also it is clear from the nature of the test that it can be performed even with an unloaded machine. Harmonic components predicted by theoretical analysis are clearly matched by simulation results. However, due to inherent asymmetries of the machine, some of these components may already exist, even in a healthy machine. It is also apparent from the simulations and experiments that, although the number of broken bars does not have much effect on the magnitude of the harmonic components, one can distinguish between a faulty and a healthy machine. Interbar currents, dependence of the spectral amplitude on the instance of disconnection, and short length of data also adversely affect on the detection technique.

Benbouzid et. al. [7, 37] investigated the efficacy of current spectral analysis on induction motor fault detection. The frequency signatures of some asymmetrical motor faults, including air gap eccentricity, broken bars, shaft speed oscillation, rotor asymmetry, and bearing failure, were identified. This work verified the feasibility of current spectral analysis. Current spectral analysis was applied to other types of electrical machines too. For example, Thomson [38,41] verified that the use of the current spectrum was successful in diagnosing air gap eccentricity problems in large, high-voltage, three-phase induction motors. Le Roux [42] monitored the current harmonic component at the rotating frequency (0.5 harmonic) to detect the rotor faults of a permanent magnet synchronous machine.

Alberto Bellini et. al. [43] presented the impact of control on faulted induction machine behavior. The diagnostic indexes usually used for open-loop operation are no longer effective. Simulation and experimental results show that the spectrum of the field current component in a field-oriented controlled machine has suitable features that can lead to an effective diagnostic procedure. Specifically, in the case of stator and rotor faults, the spectrum components at frequencies 2f and 2sf respectively, are quite independent of control parameters and dependent on the fault extent.

Benbouzid [5] made a review of MCSA as a medium for fault detection. This study introduces in a concise manner the motor signature analysis for the detection and localization of abnormal electrical and mechanical conditions that indicate, or may lead to a failure of induction motors. The MCSA utilizes the results of spectral analysis of the stator current for the detection of airgap eccentricity, broken rotor bars and bearing damage. It is based on the behavior of the current at the side band associated with the fault. For that, intimate knowledge of the machine construction is required. It is explained that when the load torque varies with rotor position, the current will contain spectral components, which coincide with those caused by the fault condition. The torque oscillation results in stator current harmonics that can obscure, and often overwhelm, those produced by the fault condition. Researcher concluded that Fourier analysis is very useful for many applications where the signals are stationary. However, it is not appropriate for analyzing a signal that has a transitory characteristic such as drifts, abrupt changes and frequency trends. To overcome this problem, Fourier analysis has been adapted to analyze small sections of the signal in time; this technique is known as the short time fast Fourier transform (STFFT). STFT represents a sort of compromise between time- and frequency-based views of a signal and provides information about both.

Joksimovic & Penman [44] studied the interaction between faulty stator winding and a healthy rotor cage. The faulty asymmetric stator winding may produce spatial harmonics into the air-gap field. However, all these harmonics vary at a single frequency, i.e. the supply frequency of the sinusoidal voltage source. The stator harmonics induce currents in the rotor cage and reflect back from the rotor as new air-gap field harmonics. The air-gap harmonics caused by the induced rotor currents vary at specific frequencies. The air-gap field harmonics induce electromotive forces in the stator winding and generate harmonic stator currents at

these same frequencies. These are the same frequencies at which a healthy machine produces harmonic stator currents. According to this analysis, a stator fault may generate only harmonic stator currents, which vary at the fundamental and rotor-slot harmonic frequencies. A fault in a stator winding may change the amplitudes of the stator-current harmonics, but it will not produce any new frequencies in the stator-current spectrum. This significant result implies that it may be difficult to detect a stator fault from a current spectrum using current signature analysis.

Masoud Haji, and Hamid A. Toliyat [45] developed a pattern recognition technique based on Bayes minimum error classifier to detect broken rotor bar faults in induction motors at the steady state. The proposed algorithm uses only stator currents as input without the need for any other variables. First rotor speed is estimated from the stator currents, then appropriate features are extracted. Once normalized mean and variance plus mean and covariance of each class are determined for an ac induction motor, the technique can be used in online condition monitoring of the motor. Theoretical approach plus experimental results from a 3 hp induction motor show the strength of the proposed method. Without loss of generality, the algorithm can be revised to include other faults such as eccentricity and phase unbalance.

Arkan et al. [46] presented a non-invasive online method for the detection of stator winding faults in three-phase induction motors from the observation of the negative sequence supply current. A power decomposition technique (PDT) was used to derive positive and negative sequence components of measured voltages and currents. This study carried out experimental studies, which showed that the negative sequence impedance could vary between 10 % and 50 % during an inter-turn short circuit.

Tallam, Habetler, and Harley [47] monitored the negative-sequence voltage to detect a turn-to-turn short circuit in a closed-loop drive-connected induction motor. A neural network was used to learn and to estimate the negative-sequence voltage of a healthy motor, which is used as the threshold. This helped to reduce the effects of machine non-ideality and unbalanced supply voltage. According to [47], most of the turn-to-turn short circuit-related fault signatures exist in the stator voltage because of the regulation of the drive controllers. However, the influence of mechanical load was neglected. In practice, the distribution of fault information between the stator voltage and current depends on drive controllers, as well

as mechanical load and operating conditions. Monitoring either stator current or voltage alone cannot ensure an accurate prediction of motor conditions.

Miletic and Cettolo [48] acknowledged that Motor Current Signature Analysis (MCSA) is one of the widely used diagnostic methods. This method is based on measurement of sidebands in the stator current spectrum. These sidebands are usually located close to the main supply frequency. Frequency converter causes supply frequency to slightly vary in time and, as a result, some additional harmonics in the current spectrum are induced and sidebands are reduced. These harmonics can be easily misinterpreted as the sidebands caused by the rotor faults. In this study, the experimental results of fault diagnosis carried out using standard supply and using frequency converter were compared and presented. All tests were performed on 22 kW induction motor.

In current spectral analysis, the actual harmonics measured from a running machine are always compared with known values (thresholds) obtained from a healthy motor. In practical applications, the thresholds change with motor operating conditions. Therefore, Obaid [49] proposed tracking the normal values of a healthy motor at different load conditions. For each load condition, a corresponding threshold was determined and compared with the on-line measurement to determine the motor condition. Besides the FFT technique in spectral analysis, other techniques in advanced digital signal processing and pattern recognition were applied to motor condition monitoring as well.

Mohamed El Hachemi Benbouzid, and Gerald B. Kliman [50] briefly presented signal (mainly motor current) processing techniques for induction motor rotor fault detection (mainly broken bars and bearing deterioration). The main advantages and drawbacks of the presented techniques are also briefly discussed. In many cases, the conventional steady state techniques may suffice. From the discussions, it appears that, for the most difficult cases, time-frequency and time-scale transformations, such as wavelets, provide a more optimal tool for the detection and the diagnosis of faulty induction motor rotors. On the one hand, they remedy the main drawbacks of motor current signal processing techniques for fault detection (i.e., nonstationarity). These techniques exhibit some interesting application advantages, such as for coal crushers, where speed varies rapidly and for deteriorated bearings where speed and signatures may vary in an unpredictable manner.

Szabó Loránd et.al. [51] presented some results on detecting broken rotor bars in induction motors. Five different motor conditions were studied (the healthy machine and having up to 4 broken bars), each at 9 different loads. The results of this study show that if there is any broken bar in the rotor it will directly affect the induced voltages in the stator windings and the waveform of the stator currents. Therefore the spectrum analysis of the line current (motor current signature analysis) is one of the best non-intrusive methods.

Szabó Loránd et. al. [52] utilized the result of spectral analysis of stator current to diagnose rotor faults. The diagnosis procedure was performed by using virtual instrumentation (VIs). Several virtual instruments (VIs) were built up in Labview. These VIs were used both for controlling the test measurements and data acquisition and for the data processing. The tests were carried out for seven different loads with healthy motor, and with similar motors having up to 5 broken rotor bars. The rotor bars were provoked interrupting the rotor bars by drilling into the rotor. The measured current signals were processed using the Fast Fourier Transformation (FFT). The power density of the measured phase current was plotted. The results obtained for the healthy motor and those having rotor faults were compared, especially looking for the sidebands components having the special frequencies.