New Techniques for Fault Detection Analysis by Injecting Additional Frequency Test

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.

Broken rotor bar (BRB) faults are one of the most common faults in induction motors (IM). One or more broken bars can reduce the performance and efficiency of the IM and hence waste the electrical power and decrease the reliability of the whole mechanical system. This paper proposes an effective fault diagnosis method using the Teager–Kaiser energy operator (TKEO) for BRB faults detection based on the motor current signal analysis (MCSA). The TKEO is investigated and applied to remove the main supply component of the motor current for accurate fault feature extraction, especially for an IM operating at low load with low slip. Through sensing the estimation of the instantaneous amplitude (IA) and instantaneous frequency (IF) of the motor current signal using TKEO, the fault characteristic frequencies can be enhanced and extracted for the accurate detection of BRB fault severities under different operating conditions. The proposed method has been validated by simulation and experimental studies that tested the IMs with different BRB fault severities to consider the effectiveness of the proposed method. The obtained results are compared with those obtained using the conventional envelope analysis methods and showed that the proposed method provides more accurate fault diagnosis results and can distinguish the BRB fault types and severities effectively, especially for operating conditions with low loads.

The induction motors found in industrial and commercial applications are responsible for most of the energy consumption in the world. These machines are widely used because of their advantages like high efficiency, robustness, and practicality; nevertheless, the occurrence of unexpected faults may affect their proper operation leading to unnecessary breakdowns with economic repercussions. For that reason, the development of methodologies that ensure their proper operation is very important, and in this sense, this paper presents an evaluation of signal entropy as an alternative fault-related feature for detecting faults in induction motors and their kinematic chain. The novelty and contribution lie in calculating a set of entropy-related features from vibration and stator current signals measured from an induction motor operating under different fault conditions. The aim of this work is to identify changes and trends in entropy-related features produced by faulty conditions such as broken rotor bars, damage in bearings, misalignment, unbalance, as well as different severities of uniform wear in gearboxes. The estimated entropy-related features are compared to other classical features in order to determine the sensitivity and potentiality of entropy in providing valuable information that could be useful in future work for developing a complete methodology for identifying and classifying faults. The performed analysis is applied to real experimental data acquired from a laboratory test bench and the obtained results depict that entropy-related features can provide significant information related to particular faults in induction motors and their kinematic chain.

Detection of half broken rotor bar fault in VFD driven induction motor drive using motor square current MUSIC analysis

This paper presents recent time-frequency analysis technique called Hilbert Huang Transform (HHT) used for non-stationary and nonlinear signals. This technique is applied in combination with a pattern recognition method to detect defects in induction motor. Studied defects in our case are one broken rotor bar, two broken rotor bars and broken ring on the rotor. The aim of this study is to improve the classification results of these different rotor faults. The classification results obtained by using the HHT method and those obtained by using the temporal signals are compared.

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.

This paper demonstrates the development of a flexible fault diagnosis methodology that can detect up to ten different faults in the induction motor (IM), simultaneously. The major IM electrical faults, such as the broken rotor bar (BRB), phase unbalance (PUF), and stator winding fault (SWF), and mechanical faults, such as bearing fault (BF), unbalanced rotor (UR), bowed rotor (BR), and misaligned rotor (MR), are considered with different fault severities for the diagnosis. The experiments are conducted with three varying loads and seven different speeds, and the frequency domain vibration and current data are acquired at a relatively low sampling rate of 1 kHz. Several statistical features are extracted and then the best feature-set is selected using the wrapper model. Thereafter, a data classification tool based on the support vector machine (SVM) is used for the fault characterization. Initially, a multi-fault diagnosis is performed by training and testing the SVM at the same operating conditions (i.e., load and speed). The performance of the classifier is found to be very good at all IM operating conditions. The main focus of this study lies in overcoming the fault diagnosis, where the data are unavailable at required operating conditions. This is accomplished by employing interpolation and extrapolation strategies for different loads and speeds. The proposed methodology not only solves practical problem of unavailability of data at different operating conditions but also shows good performance and takes low computation time, which are vital requirements of an online intelligent condition monitoring system.

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.

The knowledge about fault mode behavior of an induction motor drive system is extremely important from the standpoint of improved system design, protection, and fault-tolerant control. This paper addresses the application of motor current spectral analysis for the detection and localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors. Intensive research effort has been for some time 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 induction motor are usually minute. This paper takes the initial step to investigate the efficiency of current monitoring for diagnostic purposes. The effects of stator current spectrum are described and the related frequencies determined. In the present investigation, the frequency signature of some asymmetrical motor faults are well identified using advanced signal processing techniques, such as high-resolution spectral analysis. This technique leads to a better interpretation of the motor current spectra. In fact, experimental results clearly illustrate that stator current high-resolution spectral analysis is very sensitive to induction motor faults modifying main spectral components, such as voltage unbalance and single-phasing effects.

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).

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.

The purpose of this paper is to analyze influence of presence of broken rotor bar fault in a three-phase squirrel-cage induction motor on its current and magnetic flux density spectrums. The analysis was carried out using the motor current signature analysis as well as the monitoring of stray flux of motor. The explanation of the origination of characteristic frequencies using formulas is presented in the article. The simulation was carried out on the basis of parameters of real induction motor. Results are presented in the form of graphs and tables to show the clear influence of fault on the motor.

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.

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

This paper presents recent developments in the use of a new technique for the diagnosis of faults in three-phase induction motors. The proposed diagnostic technique, which is based on the multiple reference frames theory (MRFT), has been successfully used in the diagnosis of stator faults, rotor faults and air gap eccentricity in line-connected and direct torque controlled (DTC) induction motors. The use of the MRFT represents a step forward towards the unification of the diagnostic process of faults in induction motors and drives by using the same technique to diagnose any type of faults. Due to the modular structure of the MRFT, virtually it can be used to diagnose any motor fault that has a direct impact in one or more supply currents. In addition, as it was demonstrated in a previous work, the technique is particularly adequate for an easy integration into the control system of a DTC induction motor drive at no additional cost.