Broken Rotor Bar Fault Diagnosis Research Articles

Partial broken rotor bar fault diagnosis using signal injected and generated Hilbert method

Diagnosing induction motor BRB fault at an incipient level, that is, detecting a partial (half) BRB fault, is a challenging task, especially when motor is operating under light-load conditions. Several methods, including MUltiple SIgnal Classification (MUSIC), have been applied in literature for diagnosing this type of fault, especially under light-load conditions. However, this method is time-consuming, making it computationally expensive and unfeasible. Herein, we present a new algorithm that can reduce the computational time required for detecting half-BRB fault. The proposed algorithm captures the original current and modulates the same by injecting it with a high-amplitude signal of known frequency. Next, it applies Hilbert transform to extract the envelope of the mixed signal. This proposed method ultimately reduces the computational time in comparison to applying MUSIC algorithm directly. Experiments performed under both healthy and faulty conditions to demonstrate the validity of the proposed Signal Injected and Generated Hilbert (SInGH) method.

Sparse Bayesian Learning Approach for Broken Rotor Bar Fault Diagnosis

Sparse Bayesian Learning Approach for Broken Rotor Bar Fault Diagnosis

Development of Broken Rotor Bar Fault Diagnosis Method with Sum of Weighted Fourier Series Coefficients Square

This study proposes a broken rotor bar (BRB) fault diagnosis method for an induction motor using the sum of the weighted Fourier series coefficients squares of a complex current as a diagnosis signal. First, the sum of the squares of the Fourier series coefficients confirms the very narrow band-pass filter characteristics to derive a specific frequency component. This assists us in obtaining a BRB fault diagnosis signal that exists in a limited frequency range. Second, the magnitude of the Fourier series coefficients of the BRB fault signal is proportional to the slip frequency and load condition. A weighting factor is proposed to render the BRB fault signal irrelevant to the slip frequency and load condition. Consequently, the proposed fault diagnosis can be conducted without the slip frequency information or searching for the maximum coefficient component. Finally, the proposed fault diagnosis method is validated through experiments using a 55 kW induction motor with and without a BRB fault. It is implemented with a DSP controller at time intervals of 20, 10, 5, and 4 s for the Fourier series. The proposed diagnosis method performs well under various load conditions and shows that the derived fault signal exhibits a large difference between healthy and BRB faulty induction motors.

Broken Rotor Bar Fault Diagnosis Techniques Based on Motor Current Signature Analysis for Induction Motor—A Review

The most often used motor in commercial drives is the induction motor. While the induction motor is operating, electrical, thermal, mechanical, magnetic, and environmental stresses can result in defects. Therefore, many researchers who are involved in condition monitoring have been interested in the development of reliable and efficient fault diagnostic technologies. This paper’s goal is to provide an overview of available fault detection methods for the broken rotor bar problem, one of several defects associated to induction motors. Despite the fact that it is less common than bearing or insulator failure, this fault may cause electrical machines to fail catastrophically. It can be quite harmful, especially in large motors, and it can develop as a result of manufacturing faults, repeated starting of the machine, mechanical stress, and thermal stress. Hence, a review on rotor defect diagnosis was conducted. In order to confirm rotor bar fracture, this research provides probable defect signatures that can be extracted from the current signal. Each defect signature is reported according to (a) loading level, (b) the number of BRBs, (c) validation, and (d) methodologies.

Start-up transient analysis using CWT and ridges for broken rotor bar fault diagnosis

Start-up transient analysis using CWT and ridges for broken rotor bar fault diagnosis

Induction Motor Broken Rotor Bar Fault Diagnosis Based on Third-Order Energy Operator Demodulated Current Signal

Motor current signature analysis (MCSA) has been widely used for fault diagnosis of induction motors (IMs) because of its non-invasiveness and comparatively low cost. However, the spectral leakage of the fundamental supply frequency can obscure the fault characteristic component when IMs operating at no-load or low-load conditions. In this paper, a broken rotor bar (BRB) fault diagnosis approach based on the third-order energy operator (TOEO) demodulated current signals is presented. The TOEO approach performs effectively in reducing the negative impact of the fundamental supply frequency spectral leakage and enhancing the BRB fault features. Theoretical and experimental results show that the TOEO approach can excellently reveal the fault characteristic frequency, instead of the sideband frequencies. Besides, the results are compared with the Teager-Kaiser energy operator (TKEO). The capability of the presented approach has several advantages over the TKEO, especially when the IMs are fed by an inverter.

A Normalized Frequency-Domain Energy Operator for Broken Rotor Bar Fault Diagnosis

In the motor current signal, the characteristic frequency of broken rotor bar (BRB) fault is modulated by the supply frequency and it decreases with the decrease of the load, resulting it to be easily buried under light load conditions. Teager–Kaiser energy operator (TKEO) has shown better performance to detect the BRB faults than classical methods, such as envelope analysis and spectral analysis. However, the original definition of TKEO leads to its result lack of physical meanings and the causal processing in TKEO can lead to phase distortion and nonideal filter characteristics. Therefore, this article proposes a normalized frequency-domain energy operator (FDEO) for the BRB fault diagnosis, which does not require causal processing and calculates multiple differentiations in the frequency domain with equal accuracy in one operation. Furthermore, the normalized FDEO removes the influence of the supply frequency followed by the spectral analysis to extract fault features. The mathematical model of induction motor (IM) under healthy and faulty condition is studied in this article. Then, the proposed approach is experimentally validated with seeded one and two BRB faults operating under various load conditions. To verify the effectiveness, the results are compared with the TKEO, envelope analysis, and spectral analysis. It was found that the proposed method provides slightly obvious fault features with respect to the TKEO, especially when the IMs run under light load conditions with two BRB faults.

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

The health monitoring of induction motor is considered as a developing technology for online detection of faults that actually even take place at initial stage. In the current paper, an embedded system has been designed and simulated to simulate two major faults namely broken rotor bar fault and abnormal gear teeth fault. An extensive review of induction machine faults, diagnostic techniques of induction motor has been investigated in detail. Also, this paper deals with a novel effective technique for detecting the broken rotor fault and abnormal gear teeth fault of the induction motor. A precise finding of the fault characteristic parameter of motor is a new investigation hotspot in the Broken Rotor Bar fault diagnosis of induction motors. Broken Rotor Bar is one of the generally occurred faults in induction motor, which also might initiate critical motor damage to the motor if not found out in a right time. Also, gears that are popularly considered as most important components in the mechanical power conduction have received lots of concern in the area of condition monitoring over years and the current study is not obviously free from this. The experimental results and software simulation concluded that MPSO offers better optimization for both broken rotor bar and abnormal gear teeth fault.

Broken Rotor Bar Fault Diagnosis of Squirrel Cage Asynchronous Motor Based on Vibration Spectrum and Current Spectrum

In this paper, the vibration spectrum and current spectrum analysis technology are used to accurately diagnose the broken rotor bar fault of the slurry circulating pump motor. This paper elaborates on the mechanism of broken rotor bar fault, deduces the common motor pole pass frequency calculation formula, introduces the specific diagnosis process and disintegration of the broken slurry circulating pump motor’s rotor bar breakage fault. The cause of the fault is analyzed, and this type of the fault diagnosis method is also summarized.

An Improved Cyclic Modulation Spectral Analysis Based on the CWT and Its Application on Broken Rotor Bar Fault Diagnosis for Induction Motors

Induction motors (IMs) are widely used in many manufacturing processes and industrial applications. The harsh work environment, long-time enduring, and overloads mean that it is subjected to broken rotor bar (BRB) faults. The vibration signal of IMs with BRB faults consists of the reliable modulation information used for fault diagnosis. Cyclostationary analysis has been found to be effective in identifying and extracting fault feature. The estimators of cyclic modulation spectrum (CMS) and fast spectral correlation (FSC) based on the short-time fourier transform (STFT) have higher cyclic frequency resolution, which has proven efficient in demodulating second order cyclostationary (CS2) signals. However, these two estimators have limitations of processing the maximum cyclic frequency αmax that is smaller than Fs/2 (Fs is the sampling frequency) according to Nyquist’s Theorem. In addition, they have lower carrier frequency resolution due to the fixed window size used in STFT. In order to resolve the initial shortcomings of the CMS and FSC methods, in this paper, we extended the analysis of CMS algorithm based on the continuous wavelet transform (CWT), which enlarged the maximum cyclic frequency range to Fs/2 and provides higher carrier frequency resolution because the CWT has the advantage of multi-resolution analysis. The reliability and applicability of the proposed method for fault components localization were validated by CS2 simulation signals. Compared to CMS and FSC methods, the proposed approach shows better performance by analyzing vibration signals between healthy motor and faulty motor with one BRB fault under 0%, 20%, 40%, and 80% load conditions.

Nested SVDD in DAG SVM for induction motor condition monitoring

Nowadays, multiclass classification is considered as the leading technique on the issue of condition monitoring in induction motor which can be performed accurately and efficiently by support vectors machines. The standard multiclass SVM (MSVM) approaches consists in constructing an optimal decision hyperplane maximizing the margin of separation between the training data. However, relatively small number of outliers can obviously reduce the performance of the classical MSVMs and would have an impact on the decision boundary and the margin calculation. Support Vector Data Description (SVDD) based on hyper-spheres decision boundary is often performed to overcome this drawback. The originality of this paper is to introduce a new extension of the MSVM for broken rotor bar fault diagnosis by embedding the SVDD in the classical multiclass SVM. The experimental results prove the merit of the proposed approach with a classification rate of 100%, which is higher than any accuracy rate achieved by standard MSVM approaches.

Broken Rotor Bar Fault Detection of IM Based on the Counter-Current Braking Method

This paper presents an improved method for the broken rotor bar detection in a squirrel-cage induction motor (IM). The method is based on the spectral analysis of the transient stator current signal during the counter-current braking (CCB). Contrary to the classical CCB, the proposed method results in the low braking current, which is a small fraction of the rated value and serves as a broken bar detection test signal only. This kind of the broken rotor bar fault diagnosis is independent of loading conditions and can be carried out even for a free shaft motor. The existence of spectral components in the low CCB current signal indicating the faulty conditions is first proven with the generalized theory of symmetrical components. The method is then verified via simulations, using an IM model based on the finite-element analysis and the magnetically coupled multiple circuits approach. Afterward, the experiments are performed, showing a good agreement with both the theoretical prediction and the simulation results, confirming the presence of the fault-induced components in the stator current spectra.

Broken rotor bar fault diagnosis using fast Fourier transform applied to field-oriented control induction machine: simulation and experimental study

In the paper, a fault diagnosis of the induction motor (IM) at closed loop is presented, and the fault considered in the machine is the adjacent broken rotor bars. The technique of control uses is the vector control in order to preserve the speed regulation and compensate the fault effect and to ensure the service continuity of the machine. Several electrical and mechanical quantities (i.e., rotor speed, quadratic current components, and stator phase current) are analyzed using the fast Fourier transforms (FFT) for the detection of the rotor bars’ fault. The control strategies have been employed in Matlab/Simulink linked to real-time interface (RTI) via dSpace 1104. The simulation and experimental results show the effectiveness of machine control under rotor failure. Moreover, the FFT technique analysis on the closed-loop control signals give clear information about the presence of fault, where the quadratic component Iqs offers an advantage and becomes an appropriate characteristic for the fault detection.

Introducing the Filtered Park’s and Filtered Extended Park’s Vector Approach to detect broken rotor bars in induction motors independently from the rotor slots number

The Park’s Vector Approach (PVA), together with its variations, has been one of the most widespread diagnostic methods for electrical machines and drives. Regarding the broken rotor bars fault diagnosis in induction motors, the common practice is to rely on the width increase of the Park’s Vector (PV) ring and then apply some more sophisticated signal processing methods. It is shown in this paper that this method can be unreliable and is strongly dependent on the magnetic poles and rotor slot numbers. To overcome this constraint, the novel Filtered Park’s/Extended Park’s Vector Approach (FPVA/FEPVA) is introduced. The investigation is carried out with FEM simulations and experimental testing. The results prove to satisfyingly coincide, whereas the proposed advanced FPVA method is desirably reliable.

Broken Rotor Bar Fault Diagnosis in VFD Driven Induction Motors by an Improved Vibration Monitoring Technique

Broken Rotor Bar Fault Diagnosis in VFD Driven Induction Motors by an Improved Vibration Monitoring Technique

Stator and Rotor Faults Diagnosis of Squirrel Cage Motor Based on Fundamental Component Extraction Method

Nowadays, stator current analysis used for detecting the incipient fault in squirrel cage motor has received much attention. However, in the case of interturn short circuit in stator, the traditional symmetrical component method has lost the precondition due to the harmonics and noise; the negative sequence component (NSC) is hard to be obtained accurately. For broken rotor bars, the new added fault feature blanked by fundamental component is also difficult to be discriminated in the current spectrum. To solve the above problems, a fundamental component extraction (FCE) method is proposed in this paper. On one hand, via the antisynchronous speed coordinate (ASC) transformation, NSC of extracted signals is transformed into the DC value. The amplitude of synthetic vector of NSC is used to evaluate the severity of stator fault. On the other hand, the extracted fundamental component can be filtered out to make the rotor fault feature emerge from the stator current spectrum. Experiment results indicate that this method is feasible and effective in both interturn short circuit and broken rotor bars fault diagnosis. Furthermore, only stator currents and voltage frequency are needed to be recorded, and this method is easy to implement.

DWT and Hilbert Transform for Broken Rotor Bar Fault Diagnosis in Induction Machine at Low Load

In this paper a new technique for broken rotor bars diagnosis in induction machine at low load and non stationary state is proposed. The technique is used in order to remedy the problem from using the classical signal-processing technique FFT by analysis of stator current envelope. The proposed method is based from using discrete wavelet transform (DWT) and Hilbert transform. The Hilbert transform is used to extract the stator current envelope. Then this signal is processed via DWT. The efficiency of the proposed method is verified by simulation tests.

Principal Component Analysis of the start-up transient and Hidden Markov Modeling for broken rotor bar fault diagnosis in asynchronous machines

This article presents a novel computational method for the diagnosis of broken rotor bars in three phase asynchronous machines. The proposed method is based on Principal Component Analysis (PCA) and is applied to the stator’s three phase start-up current. The fault detection is easier in the start-up transient because of the increased current in the rotor circuit, which amplifies the effects of the fault in the stator’s current independently of the motor’s load. In the proposed fault detection methodology, PCA is initially utilized to extract a characteristic component, which reflects the rotor asymmetry caused by the broken bars. This component can be subsequently processed using Hidden Markov Models (HMMs). Two schemes, a multiclass and a one-class approach are proposed. The efficiency of the novel proposed schemes is evaluated by multiple experimental test cases. The results obtained indicate that the suggested approaches based on the combination of PCA and HMMs, can be successfully utilized not only for identifying the presence of a broken bar but also for estimating the severity (number of broken bars) of the fault.

Wavelet Transform based Broken Rotor-bar Fault Detection and Diagnosis Performance Evaluations

paper presents the performances evaluation of different wavelet transform under broken rotor bars fault diagnosis. In this report, stator phase current was used for wavelet analysis. Discrete wavelet transform (DWT) coefficients of stator current in a specific frequency band are derived and analyzed. Wavelets db8, db9, db10, sym7 and sym8 are employed to analyze broken bar distorted stator current. The sensitivities of these wavelets to fault signal are compared and assessed to select the most optimal one. This approach facilitates the detection and diagnosis of broken rotor bar occurrence or even number of broken bars under load variation. Simulation and experimental tests on induction motors with 1, 2, and 3 broken bars cases are conducted and demonstrate the effectiveness of the proposed approach for fault detection. Keywordsmachine, broken rotor bar, DWT, stator current

Approach Signal for Rotor Fault Detection in Induction Motors

In this paper, two approach signals are used for broken rotor bar fault diagnosis. One is based on the spectrum analysis, such as the fast Fourier transform, which utilizes the steady-state spectral components of the stator quantities. The accuracy of this technique depends on the loading conditions and constant speed of the machine. The second approach is based on the discrete wavelet transform which is considered an ideal tool for this purpose due to its suitability for the analysis of signals, the frequency spectrum of which is variable in time. These two approaches are tested in simulation and validated experimentally.