Bearing Fault Diagnosis Using Torque Observer in Induction Motor

Knowledge of the distinctive frequencies and amplitudes of broken rotor bar (BRB) faults in the induction motor (IM) is essential for most fault diagnosis methods. Fast Fourier transform (FFT) is widely applied to diagnose the faults within BRBs. However, this method does not provide satisfactory results if it is applied directly to the stator current signal at low slip because a high-resolution spectrum is required to separate the different components of the frequency. To address this problem, this paper proposes an efficient method based on a Hilbert fast Fourier transform (HFFT) approach, which is used to extract the envelope from the stator current using the Hilbert transform (HT) at low slip. Then, the stator current envelope is analyzed using the fast Fourier transform (FFT) to obtain the amplitude and frequency of the particular harmonic. These data were recently collected and selected as BRB fault features and were employed as adaptive neuro-fuzzy inference system (ANFIS) inputs for BRB fault autodiagnosis and classification. To identify the BRB defect by determining the number of broken bars in the rotor, two ANFIS models are proposed: ANFIS grid partitioning (ANFIS-GP) and ANFIS-subtractive clustering (ANFIS-SC). To validate the effectiveness of the proposed method, three different motors were used during experiments under various loads; the first was with one broken bar, the second was with two adjacent broken bars, and the third was a healthy motor. The obtained results confirmed the effectiveness and the robustness of the proposed method, which is based on the combination of HFFT-ANFIS-SC to diagnose the BRB faults and quantify the number of broken bars under different load conditions (under low and high slip) precisely with minimal errors (this method had an MSE of 10-14 and 10-7 for the RMSE) compared to the method based on the combination of HFFT-ANFIS-GP.

Owing to the 4.0 industrial revolution condition monitoring maintenance is widely accepted as a useful approach to avoiding plant disturbances and shutdown. Recently, Motor Current Signature Analysis (MCSA) is widely reported as a condition monitoring technique in the detection and identification of individual and multiple Induction Motor (IM) faults. However, checking the fault detection and classification with deep learning models and its comparison among themselves or conventional approaches is rarely reported in the literature. Therefore, in this work, we present the detection and identification of induction motor faults with MCSA and three Deep Learning (DL) models namely MLP, LSTM, and 1D-CNN. Initially, we have developed the model of Squirrel Cage induction motor in MATLAB and simulated it for single phasing and stator winding faults (SWF) using Fast Fourier Transform (FFT), Short Time Fourier Transform (STFT), and Continuous Wavelet Transform (CWT) to detect and identify the healthy and unhealthy conditions with phase to ground, single phasing and in multiple fault conditions using Motor Current Signature Analysis. The faults impact on stator current is presented in the time and frequency domain (i.e., power spectrum). The simulation results show that the scalogram has shown good results in time-frequency analysis for fault and showing its impact on the energy of current during individual fault and multiple fault conditions. This is further investigated with three deep learning models (i.e., MLP, LSTM, and 1D-CNN) for checking the fault detection and identification (i.e., classification) improvement in a three-phase induction motor. By simulating the three-phase induction motor in various healthy and unhealthy conditions in MATLAB, we have collected current signature data in the time domain, labeled them accordingly and created the 50 thousand samples dataset for DL models. All the DL models are trained and validated with a suitable number of architecture layers. By simulation, the multiclass confusion matrix, precision, recall, and F1-score are obtained in several conditions. The result shows that the stator current signature of the motor can be used to detect individual and multiple faults. Moreover, deep learning models can efficiently classify the induction motor faults based on time-domain data of the stator current signature. In deep learning (DL) models, the LSTM has shown better accuracy among all other three models. These results show that employing deep learning in fault detection and identification of induction motors can be very useful in predictive maintenance to avoid shutdown and production cycle stoppage in the industry.

Induction motors fed through variable speed drives (VSD) are widely used in different industrial processes. Nowadays, the industry demands the integration of smart sensors to improve the fault detection in order to reduce cost, maintenance and power consumption. Induction motors can develop one or more faults at the same time that can be produce severe damages. The combined fault identification in induction motors is a demanding task, but it has been rarely considered in spite of being a common situation, because it is difficult to identify two or more faults simultaneously. This work presents a smart sensor for online detection of simple and multiple-combined faults in induction motors fed through a VSD in a wide frequency range covering low frequencies from 3 Hz and high frequencies up to 60 Hz based on a primary sensor being a commercially available current clamp or a hall-effect sensor. The proposed smart sensor implements a methodology based on the fast Fourier transform (FFT), RMS calculation and artificial neural networks (ANN), which are processed online using digital hardware signal processing based on field programmable gate array (FPGA).

The real-time fast Fourier transform (FFT) is the essential algorithm for signal processing in a solar radio receiver. However, field-programmable gate array (FPGA) computation resources have become the limitation of real-time processing of signals with increasing time and spectral resolutions. It is necessary to design a real-time parallel FFT algorithm with reduced resource occupation in the development of future receiving systems. In this paper, we developed a multichannel parallel FFT algorithm named the multichannel parallel real-time fast Fourier transform (MPR-FFT), which can greatly reduce FPGA resource occupation while increasing the real-time processing speed. In this algorithm, the 4L simultaneous N-point FFTs are first converted into L simultaneous 4N-point FFTs. Fusion processing is then performed to obtain the 4 ∗ L ∗ N-point spectrum. This method has been used in developing a solar radio spectrometer, which works in the frequency range of 0.5–15 GHz in the Chashan Observatory. In this spectrometer, 16 channel MPR-FFT with 8k-point data is realized in a Xilinx UltraScale KU115 FPGA. The MPR-FFT algorithm reduced the computational resources to a large extent compared to the Cooley-Tukey-based parallel FFT method; for instance, the Look-Up-Table, Look-Up-Table RAM, Flip-Flop, and Digital Signal Process slices were reduced by 37%, 50%, 17%, and 2.48%, respectively. Although the MPR-FFT consumes 14 block RAM resources more than the Cooley-Tukey-based parallel FFT, the MPR-FFT algorithm presents an overall reduction in resource usage.

Industrial applications rely heavily on induction motors (IMs). Even though any IM problem can seriously impair operation, rotor bar failures (RBFs) are among the toughest to identify because of their detection challenges. RBFs in IMs can significantly impact performance, leading to reduced efficiency, increased vibrations, and potential IM failure. This research provides a thorough analysis of diagnosing these issues by detecting RBFs and evaluating their severity using three sophisticated signal processing techniques (Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), and Discrete Wavelet Transform (DWT)). The three techniques (FFT, DWT, and STFT) are used in this work to assess the stator currents. An accurate mathematical model of the IM under RBFs serves as the basis for the simulation. The robustness of Direct Torque Control (DTC) is assessed by examining the IM's behavior in both normal and malfunctioning situations. Although the results show that DTC successfully preserves motor stability even when there are flaws, the current analysis offers some significant variation. The findings show that when it comes to identifying RBFs in IMs and determining their severity, the STFT performs better than FFT and DWT. The suggested method maintains low estimation errors and strong performance under various operating situations while providing high failure detection accuracy and the ability to discriminate between RBFs.

Induction motors are critical components for most industries. Induction motors failures may yield an unexpected interruption at the industry plant. Several conventional vibration and current analysis techniques exist by which certain faults in rotating machinery can be identified. Ever since the first motor was built, plant personnel have listened to the noises emanating from machines; with enough experience, a listener may make a fairly accurate estimate of the condition of a machine. Although there are several works that deal with vibration and current analysis for monitoring and detection of faults in induction motors, the analysis of sound signals has not been sufficiently explored as an alternative non-invasive monitoring technique. The contribution of this investigation is the development of a condition monitoring strategy that can make reliable assessment of the presence of a specific fault condition in an induction motor with a single fault present through the analysis of sound signal. The proposed methodology is based on the combination of Intrinsic Mode Functions (IMFs) and the Fast Fourier Transform (FFT) methods. Results show that the proposed methodology can be applied to sound signal analysis; there this detection technique is suited for detection of fault frequencies in induction motors.

Induction motor failures are mainly due to stator and rotor faults. In this paper, a novel method based on Park's vector approach for fault detection of induction motor is presented. Using suggested method it is possible to detect the various types of faults in squirrel induction motors. Further, paper focuses on the detection and discrimination of the simultaneous stator inter turn and broken rotor bar faults in induction motor. In order to evaluate the ability of the proposed method several experimental results are presented. The result demonstrates that the proposed method is effective and accurate and can be widely used in the induction motor fault detection.

Induction motors are critical components for most industries and the condition monitoring has become necessary to detect faults. There are several techniques for fault diagnosis of induction motors and analyzing the startup transient vibration signals is not as widely used as other techniques like motor current signature analysis. Vibration analysis gives a fault diagnosis focused on the location of spectral components associated with faults. Therefore, this paper presents a comparative study of different time-frequency analysis methodologies that can be used for detecting faults in induction motors analyzing vibration signals during the startup transient. The studied methodologies are the time-frequency distribution of Gabor (TFDG), the time-frequency Morlet scalogram (TFMS), multiple signal classification (MUSIC), and fast Fourier transform (FFT). The analyzed vibration signals are one broken rotor bar, two broken bars, unbalance, and bearing defects. The obtained results have shown the feasibility of detecting faults in induction motors using the time-frequency spectral analysis applied to vibration signals, and the proposed methodology is applicable when it does not have current signals and only has vibration signals. Also, the methodology has applications in motors that are not fed directly to the supply line, in such cases the analysis of current signals is not recommended due to poor current signal quality.

A new method for detecting broken rotor bar fault (BRB) in induction motors is proposed which is based on the combination of weighted subspace fitting (WSF) and bare-bones particle swarm optimization (BBPSO). In the circumstance of low signal-to-noise ratio and small snapshots data, WSF owns an excellent estimation performance, with a heavy computation. In order to improve the rapidity of WSF, BBPSO has been proposed to quickly search solutions of WSF. Performance of this new detection method is texted with the stator current signal of an induction motor with rotor fault, providing that the WSF-BBPSO-based method to detect rotor fault in induction motor is effective even with short-time sample and in low SNR environment, meanwhile compared with FFT and ESPRIT-SAA method to prove its superiority and feasibility.

- Use of efficient signal processing tools (SPTs) to extract proper indices for fault detection in induction motors (IMs) is the essential part of any fault recognition procedure. The Part1 of the two parts paper focuses on Fourier-based techniques including fast Fourier transform and short time Fourier transform. In this paper, all utilized SPTs which have been employed for fault fetection in IMs are analyzed in details. Then, their competency and their drawbacks for extracting indices in transient and steady state modes are criticized from different aspects. The considerable experimental results are used to certificate demonstrated discussion. Different kinds of faults, including eccentricity, broken bar and bearing faults as major internal faults, in IMs are investigated. The use of efficient signal processing tools (SPTs) to extract proper indices for faultdetection in induction motors (IMs) is the essential part of any fault recognition procedure. In thefirst part of the present paper, we focus on Fourier-based techniques, including fast Fourier transformand short time Fourier transform. In this paper, all utilized SPTs which have been employed forfault detection in IMs are analyzed in detail. Then, their competency and their drawbacks to extractindices in transient and steady state modes are criticized from different aspects. Different kinds offaults, namely, eccentricity, broken bar, and bearing faults as the major internal faults in IMs, areinvestigated.

An early diagnosis of faults prevents financial losses and downtime in the industry. In this paper, the authors present the early fault diagnostic technique of inter-turn short-circuit fault in induction motors, so the Park’s Vector Difference Approach (PVDA) is introduced. The fault current vector ring subtracts the three current vector ring of normal operation at a certain voltage to get a petal. Thus, the amplitude of 3 Hz components of the petal with the Fast Fourier transform (FFT) is the characteristic quantity of the fault motor, through which stator winding fault is detected as well as the degree of the stator winding fault. Experiments were performed at different loads and unbalanced voltage. Efficiency of the proposed method is validated experimentally on a 0.1 kW motor. The results are proved to coincide satisfying, whereas the proposed advanced Park’s Vector Difference Approach method is desirably reliable.

The detection of electrical and mechanical faults in induction machine (IM) by analysis of stator current methods has been widely explored. These techniques have become the reference method for the faults diagnosis and are called Motor Current Signature Analysis (MCSA); these methods are based on Fast Fourier Transform (FFT), Hilbert transform (HT) and discrete wavelet transform (DWT) are applied to detect the broken rotor bars faults in induction motor. The Hilbert transform is used to extract the stator current envelope for the stator current and processes via DWT. The main objectives of HT is the removal of the fundamental component to allow a clearer vision of the fault frequencies. This technique is effective for the stationary signal processing. The analysis and fault signature of the IM is performed in healthy and faulty states. In this paper, the simulation and experimental results are presented for the diagnosis and detecting of the broken bars faults at the rotor level.

The diagnosis of motor failures using an on-line method has been the aim of many researchers and studies. Several spectral analysis techniques have been developed and are used to facilitate on-line diagnosis methods in industry. This paper discusses the first application of a motor flux spectral analysis to the identification of broken rotor bar (BRB) faults in induction motors using a multiple signal classification (MUSIC) technique as an on-line diagnosis method. The proposed method measures the leakage flux in the radial direction using a radial flux sensor which is designed as a search coil and is installed between stator slots. The MUSIC technique, which requires fewer number of data samples and has a higher detection accuracy than the traditional fast Fourier transform (FFT) method, then calculates the motor load condition and extracts any abnormal signals related to motor failures in order to identify BRB faults. Experimental results clearly demonstrate that the proposed method is a promising candidate for an on-line diagnosis method to detect motor failures.

The traditional Hilbert method to detect broken rotor bar fault in induction motors is reviewed and its major drawbacks are clearly revealed, namely, deteriorating or even completely failing when a motor operating at low slip due to the fixed constraints of fast Fourier transform (FFT) is used in this method. To overcome this, the estimation of signal parameters via rotational invariance technique (ESPRIT) is then introduced to replace FFT, and an improved Hilbert method is thus presented by conjugating the Hilbert transform and ESPRIT together. Experimental results of a small motor in a laboratory and a large motor operating on an industrial site are reported to demonstrate the effectiveness of the improved Hilbert method.

Three-phase induction motors play a key role in industrial operations. However, their failure can result in serious operational problems. This study focuses on the early identification of faults through the accurate diagnosis and classification of faults in three-phase induction motors using artificial intelligence techniques by analyzing current, temperature, and vibration signals. Experiments were conducted on a test bench, simulating real operating conditions, including stator phase unbalance, bearing damage, and shaft unbalance. To classify the faults, an Auto-Regressive Neural Network with Exogenous Inputs (NARX) was developed. The parameters of this network were determined through a process of selecting the best network by using the scanning method with multiple training and validation iterations with the introduction of new data. The results of these tests showed that the network exhibited excellent generalization across all evaluated situations, achieving the following accuracy rates: motor without fault = 94.2%, unbalanced fault = 95%, bearings with fault = 98%, and stator with fault = 95%.