Hilbert Huang Transform and pattern recognition to detect defects in induction motor

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.

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.

Nowadays, many industrial applications require online systems that combine several processing techniques in order to offer solutions to complex problems as the case of detection and classification of multiple faults in induction motors. In this work, a novel digital structure to implement the empirical mode decomposition (EMD) for processing nonstationary and nonlinear signals using the full spline-cubic function is presented; besides, it is combined with an adaptive linear network (ADALINE)-based frequency estimator and a feed forward neural network (FFNN)-based classifier to provide an intelligent methodology for the automatic diagnosis during the startup transient of motor faults such as: one and two broken rotor bars, bearing defects, and unbalance. Moreover, the overall methodology implementation into a field-programmable gate array (FPGA) allows an online and real-time operation, thanks to its parallelism and high-performance capabilities as a system-on-a-chip (SoC) solution. The detection and classification results show the effectiveness of the proposed fused techniques; besides, the high precision and minimum resource usage of the developed digital structures make them a suitable and low-cost solution for this and many other industrial applications.

Fault detection in induction motors is essential for maintaining the reliability of industrial operations. In practical applications, induction motors experience gradual wear on critical components, such as rotor bars, affecting their performance. This paper introduces a new methodology for modeling predictive wear functions related to rotor faults in induction motors, providing accurate forecasts and optimal performance through Quaternion Signal Analysis in the time domain. Our approach accurately detects wear in broken rotor bars and anticipates their degradation over time. The methodology involves coupling four vibration signals from the motor, representing them as quaternion coefficients, and calculating their rotational attributes to derive a statistical mean. We employ polynomial and Fourier regression techniques to construct a predictive wear function. We assess its accuracy through root mean square error (RMSE) analysis, which improves with increased sample size and regression complexity. Our findings indicate that polynomial regression, particularly at the second degree, achieves superior RMSE results compared to Fourier regression, even within limited sample windows. This approach offers a robust framework for early fault detection and wear prediction in induction motors, supporting enhanced maintenance strategies.

This paper deals with the problem of the early stator (the short-circuits) and rotor (broken bars) faults detection of the induction motors supplied from the frequency converter. The method basing on the FFT algorithm is used for the spectral analysis of the instantaneous active power, the instantaneous reactive power and the estimated electromagnetic torque. These signals are on-line calculated, basing on the measured currents and voltages of the motor. Computations of the instantaneous active and reactive power use the theory of the electric power for non-sinusoidal power systems. In this paper results of laboratory investigations for low power motor with modeled stator short-circuit and broken rotor bars are demonstrated. Tests were realized for different supply frequencies, what enabled the evaluating of the usefulness of the proposed analysis in the diagnostics of the stator and rotor faults in the converter-fed induction motor drives.

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 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.

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.

Hilbert transformation is an ideal phase shifting tool in data signal processing. Being Hilbert transformed, the conjugated one of a signal is obtained. The Hilbert modulus is defined as the square of a signal and its conjugation. This work presents a method by which rotor faults of squirrel cage induction motors, such as broken rotor bars and eccentricity, can be diagnosed. The method is based on the spectral analysis of the stator current Hilbert Modulus of the induction motors. Theoretical analysis and experimental results demonstrate that has the same rotor fault detecting ability as the extended Park' vector approach. The vital advantage of the former is the smaller hardware and software spending compared with the existing ones.

Health monitoring of induction motors through embedded systems-simulation of broker rotor bar fault and abnormal gear teeth fault

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.

Induction motors play a vital role in industrial applications. Early detection of abnormalities in the motor would help to avoid costly breakdowns. Realizing a versatile modeling of motor winding faults is essential for developing and evaluating protection functions. Accordingly, this article presents an accurate mathematical model for stator winding faults in induction motors for both transient and steady-state analysis. This model is based on the d-q-axis theory and is valid for phase-to-ground, phase-to-phase, and turn-to-turn faults. These turn-to-turn shorts arise as one of the most prevalent and potentially destructive electrical faults in induction motors. For investigating the performance of the proposed modeling, an experimental setup is structured with a 0.5-HP, 2-pole, 380/220-V squirrel-cage induction motor. The motor stator winding was rewound for enabling a turn-to-turn fault application. The digital signal processing board (DS1003) is employed for data acquisition and recording purposes. A reasonably close agreement between the measured and simulated results has been observed, which supports the validity of the proposed analysis.

In the paper an unsupervised neural network scheme for the identification and quantification of faults in induction motors is presented. Fault identification is performed using self-organizing maps neural networks. From the clusters generated by the neural network, the quantization error of each cluster is used to determine the fault magnitude. The general scheme is based on the motor's active and reactive instantaneous powers, in order to detect and diagnose faults whose characteristic frequencies are very close each other, such as broken rotor bars and oscillating loads. This network is trained using data obtained through the experimental measurements. Additional experimental data are later applied to the network in order to validate the proposal. Finally, it is demonstrated that the proposed strategy is able to correctly identifying and quantifying both faults, thus avoiding the need for an expert to perform the task.

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.

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.