Induction motors' faults detection and localization using stator current advanced signal processing techniques

This paper presents a comparative study between two techniques of signal processing to diagnose both faults the inter-turn short circuit (ITSC) in stator windings and the unbalanced voltage supply (UVS) in induction motors. The first is considered a classical technique called Motor Current Signature Analysis (MCSA) which is based on the processing of the stator current by the Fast Fourier Transform (FFT). The second is anadvanced technique based on a Discrete Wavelet Energy Ratio (DWER) of three stator currents. The aim objective of this paper is to compare the ability and effectiveness of both techniques to detect the ITSC fault and the UVS in induction motors, and distinguishing between them. An experimental implementation tests the two diagnosis techniques.The results obtained show that the MCAS technique by the FFT analysis has a difficult to discriminate between the current harmonics due to the provide voltage unbalance and those originated by ITSC faults. Unlike the DWERtechnique, which has high sensitivity and exceptional ability to detect and distinguish between the two faults that lead to the reliability of the diagnosis system. To demonstrate that the DWER is an accurate and robust diagnosis approach are used the neural network (NN) as a tool to classify the faults (ITSC and USV) where using DWER indicators as NN input. The results obtained of combination between the DWER and NN are effective and proved its ability to detect both faults under different load conditions and distinguish between them accurately with low error (10-5).

Bearing damage is the most common fault type in induction motors. In recent years, bearing fault detection based on motor current signature analysis (MCSA) has been gained extensive attention. However, the changes in the stator current signal which is caused by the bearing fault are usually very weak. In order to detect bearing fault effectively, a method for bearing fault detection based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) analysis of motor current signals is proposed. The CEEMDAN is used to decompose the stator current signal into several independent intrinsic mode functions (IMF), then the most sensitive IMF can be extracted. The experimental results show that the proposed approach is effective for bearing outer race fault detection.

Broken rotor bar (BRB) fault is common in cage rotor induction motors. Motor current signature analysis (MCSA) has been a popular method to detect BRB faults. In the MCSA, the fault signature frequencies for BRB are close to the fundamental frequency. This spectral nearness leads to the requirement of larger observation windows, which inherently increases BRB fault detection time. This paper presents an alternative algorithm to detect BRB faults in induction motors from MCSA using two Taylor–Kalman (TK) filters in cascade with a subsampling scheme. The proposed BRB fault detection approach allows to use the TK filter to estimate lower frequencies with less computational burden in comparison to conventional TK analysis. Experimental analysis shows that nearly accurate estimates and competitive detection time can be achieved by the proposed BRB detection method. The performance of the proposed algorithm has been compared with classical spectral techniques using numerical simulations and records acquired from induction motors under real operating conditions.

This paper presents the implementation of TMS320LF2407 DSP processor Kit to identify the single phasing fault online, based on the stator current. The Induction motors are most widely used motors in industrial, commercial and residential sectors because of enormous merits of these over the types of available electric motors, as the workhorse in industrial applications. The early detection of these deteriorating conditions in incipient phase and its removal is necessary for the prevention of external failure of the induction motors, reducing repairs costs and motor outage time. The various types of external faults in induction motor are single line-to-ground fault, double line-ground fault, line-line fault, single phasing fault, locked rotor, under voltage, overvoltage, phase sequence reversal of supply voltage and mechanical overload. This proposed method is applied to a 3.3KW three phase induction motor using TMS320LF2407 DSP processor Kit to identify the single phasing fault online, based on the stator current and then the measurement results are analyzed.

Induction motors are widely used worldwide for domestic and industrial applications. Fault detection and classification techniques based on signal analysis have increased in popularity due to the growing use of induction motors in new technologies such as electric vehicles, automatic control, maintenance systems, and the inclusion of renewable energy sources in electrical systems, among others. Hence, monitoring, fault detection, and classification are topics of interest for researchers, given that the presence of a fault can lead to catastrophic consequences concerning technical and financial aspects. To detect a fault in an induction motor, several techniques based on different physical variables, such as vibrations, current signals, stray flux, and thermographic images, have been studied. This paper reviews recent investigations into physical variables, instruments, and techniques used in the analysis of faults in induction motors, aiming to provide an overview on the pros and cons of using a certain type of physical variable for fault detection. A discussion about the detection accuracy and complexity of the signals analysis is presented, comparing the results reported in recent years. This work finds that current and vibration are the most popular signals employed to detect faults in induction motors. However, stray flux signal analysis is presented as a promising alternative to detect faults under certain operating conditions where other methods, such as current analysis, may fail.

Detection of failures in induction motors is one of the most important concerns in industry. An unexpected fault in the induction motors can cause a loss of financial resources and waste of time that most companies cannot afford. The contribution of this paper is a fusion of the Empirical Mode Decomposition (EMD) and Multiple Signal Classification (MUSIC) methodologies for detection of multiple combined faults which provides an accurate and effective strategy for the motor condition diagnosis.

The paper presents the development and the practical implementation of a system for detection and diagnosis of interturn short-circuits in the stator windings of induction motors. Motor current signature analysis (MCSA) and fuzzy logic techniques are utilized in order to achieve that. After a brief description of the MCSA, the causes of short circuits are discussed and characterized with frequency relationships and frequency spectra. Subsequently, failure diagnosis techniques based on fuzzy logic are presented. Afterwards, the results of the practical implementation of fault detection and diagnosis system are shown and commented. MATLAB 6.0 and built-in toolboxes (DSP blockset, fuzzy logic and real-time workshop) are used as a development tool. The implemented and tested method showed efficiency as the practical results corresponded to the predicted with the developed system. The results obtained present a great degree of reliability, which enables them to be used as a monitoring tool for similar motors.

Double frequency tests are used for evaluating stator windings analyzing the temperature. Likewise, signal injection on induction machines is used on sensorless motor control fields to find out the rotor position. Motor Current Signature Analysis (MCSA) is the most widely used method for identifying faults in Induction Motors. MCSA focuses its efforts on the spectral analysis of stator current. Motor faults such as broken rotor bars, bearing damage and eccentricity of the rotor axis can be detected. However, the method presents some problems at low speed and low torque, mainly due to the proximity between the frequencies to be detected and the small amplitude of the resulting harmonics. This paper proposes to inject an additional voltage into the machine under test at a frequency different from the fundamental one, and then to study the resulting harmonics around the new frequencies appearing due to the composition between injected and main frequencies.

In this paper, the detection and isolation of open-switch faults in induction motor (IM) drives are addressed from a model-based perspective. Residuals are synthesized by using nonlinear observers followed from a directional characterization. First, it is observed that the IM model can be written in a recurrent decoupled structure by taking the stator currents and mechanical velocity as outputs. In this way, residuals can be insensitive to load torque and operating conditions, and simultaneous faults can be addressed. A pulsewidth modulation three-phase inverter is studied as power actuator for the IM. Hence, in order to isolate faults related to the six switching devices, a directional residual evaluation in the -frame is employed. Hence, three residuals are constructed to isolate the faulty switches. The ideas presented in this paper are validated experimentally in a test bench of 1-hp IM under single and simultaneous faults.

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Induction motors are workhorses of industry. Hence, fault diagnosis of an induction motor is vital in every industry for reduction of maintenance cost, safety, increase of production etc. Motor Current Signature Analysis is a non invasive online monitoring technique for the fault diagnosis of induction motors. In this paper, an Induction motor has been modeled using coupled circuit modeling of induction motor. This model is successfully used to simulate the induction motors at healthy and faulty cases. This model gives the complete information of stator current required for MCSA. Rotor broken bars results rotor asymmetry, which induces a frequency components around fundamental in stator current. Neural network is one of the reliable soft computing techniques. Based on the rotor harmonics and slip, a neural network method to diagnose the rotor broken faults of an induction motor is presented.

This paper presents a novel method for detecting stator winding inter-turn short circuit fault in a three-phase induction motor by vibration signature analysis using non-electrical contact type microelectromechanical systems (MEMS) accelerometer. The present work detects inter-turn short circuit by vibration analysis in a three-phase induction motor running under different load conditions. Theoretical analysis of stator winding under inter-turn short circuit condition has been presented. Experimental investigations have been carried out to detect inter-turn short circuit fault by vibration signature analysis using a MEMS accelerometer with three-phase induction motor operating under different conditions. The spectral analysis of motor vibration has been carried out using radix-2 decimation in time fast Fourier transform algorithm. The appearance of particular characteristic frequency components in vibration spectrum indicates stator winding inter-turn short circuit faults. The experimental results are found to closely match with the theoretical values, indicating that MEMS accelerometers can be successfully employed for the detection of stator winding inter-turn short circuit fault in induction motors.

Transient tracking of low and high-order eccentricity-related components in induction motors via TFD tools

Electrical machines, induction motors in particular, play a key role in domestic and industrial applications. They act as a work horse in almost every industry and are responsible for a big proportion of total generated electricity consumption worldwide. The faults in induction motors are degenerative in nature and can lead to a catastrophic situation if not diagnosed earlier. The failures can cause considerable financial loss in the form of unexpected downtime. Broken rotor bar is a very common and frequently occurring fault in most of industrial induction motors. To select a better, more accurate and reliable fault diagnostic technique, this paper presents a comprehensive literature survey on the existing motor current signature analysis (MCSA) based fault diagnostic techniques. Different well-known MCSA based fault diagnostic techniques are summarized in the form of basic theories, considering complexity of their implementation, merits and demerits.

The present paper proposes a novel data-driven Fault Detection and Diagnosis algorithm for induction motors based on Motor Current Signature Analysis. Principal Component Analysis is used to reduce the three-phase currents space in two dimensions. Then, Kernel Density Estimation is adopted to estimate the Probability Density Function of healthy and of each faulty motors, which will give typical patterns that can be used to identify each fault. Kullback-Leibler divergence is used as an index to identify the dissimilarity between two determined probability distributions, that allows the automatic identification of distinct fault types. Several simulations and experimental results are carried out using two benchmarks in order to verify the effectiveness of the proposed methodology: the first is used to prove appropriateness of the method for air gap eccentricity fault diagnosis and the second is used to prove suitability of the method for rotor broken bars and connectors fault diagnosis. Simulations and classification results prove that the proposed Fault Detection and Diagnosis procedure is able to detect and diagnose different induction motor fault types.