Broken Bar Fault Research Articles

Weak-fault diagnosis using state-transition-algorithm-based adaptive stochastic-resonance method

In the early fault period of high-speed train systems, the interested characteristic signals are relatively weak and easily submerged in heavy noise. In order to solve this problem, a state-transition-algorithm (STA)-based adaptive stochastic resonance (SR) method is proposed, which provides an alternative solution to the problem that the traditional SR has fixed parameters or optimizes only a single parameter and ignores the interaction between parameters. To be specific, the frequency-shifted and re-scaling are firstly used to pre-process an actual large signal to meet the requirement of the adiabatic approximate small parameter. And then, the signal-to-noise ratio is used as the optimization target, and the STA-based adaptive SR is used to synchronously optimize the system parameters. Finally, the optimal extraction and frequency recovery of a weak characteristic signal from a broken rotor bar fault are realized. The proposed method is compared with the existing methods by the early broken rotor bar experiments of traction motor. Experiment results show that the proposed method is better than the other methods in extracting weak signals, and the validity of this method is verified.

Real-time broken rotor bar fault detection and classification by shallow 1D convolutional neural networks

Application of advanced fault diagnosis and monitoring techniques allows more efficient, reliable and safe operation of many complex industrial systems. Recently, there has been a significant increase in application of various data-driven deep learning models for motor fault detection and diagnosis problems. Due to high computational complexity and large training dataset requirements of deep learning models, in this study, shallow and adaptive 1D convolutional neural networks (CNNs) are applied to real-time detection and classification of broken rotor bars in induction motors. As opposed to traditional fault diagnosis systems with separately designed feature extraction and classification blocks, the proposed system takes directly raw stator current signals as input and it can automatically learn optimal features with the proper training. The other advantages of the proposed approach are (1) its compact architecture configuration performing only 1D convolutions with a set of filters and subsampling, making it suitable for implementing with real-time circuit monitoring, (2) its requirement for a limited size of training dataset for efficient training of the classifier and (3) its cost-effective implementation. Effectiveness and feasibility of the proposed method is validated by applying it to real motor current data from an induction motor under full load.

On the broken rotor bar diagnosis using time–frequency analysis: ‘Is one spectral representation enough for the characterisation of monitored signals?’

This work enhances the knowledge of the diagnostic potential of the broken bar fault in induction motors. Since a series of studies have been published over the years regarding condition monitoring and fault diagnostics of these machines, it is essential to reach a common ground on why – sometimes – different techniques render different results. In this context, an investigation is provided with regards to the optimal window that should be adopted for the implementation of a proper time–frequency analysis of the monitored signals. On this agenda, this study attempts to set lower and upper bound limits for proper windowing from the digital signal processing point of view. This is done by proposing a formula for the lower limit, which is derived according to the specific frequencies one desires to put under inspection and which are the fault-related signatures. Finally, a discussion on the upper bound is put onwards; results from finite-element simulations are examined with the discussed approach in both the transient regime and the steady state, while experimental results verify the simulations with satisfying accuracy.

An Approach on MCSA-Based Fault Detection Using Independent Component Analysis and Neural Networks

This paper presents a novel approach on motor current signature analysis (MCSA) for broken bar fault detection of induction motors (IMs), using as input the current signal measured from one of the three motor phases. Independent component analysis (ICA) is used over the Fourier-domain spectral signals obtained from the input and its autocorrelation function. The standard deviation of spectral components within a region of interest (ROI) of an ICA signal output was found to exhibit substantial differences between damaged and healthy motors. Separation of the ROI in one, two, and three sectors leads to an improved extraction of feature vectors, which are further fed into a neural network for classification purposes. The assessment of the proposed method is carried out through several experiments using two damage levels (broken bar and half broken bar) and two load motor conditions (50% and 75%), with a classification accuracy ranging from 90% to 99%. The contribution of this paper lies in a new technique of signal processing for ICA-based feature extraction in a 3-D feature space for IM fault diagnosis.

Broken Rotor Bar Fault Detection of Induction Motors Using a Joint Algorithm of Trust Region and Modified Bare-bones Particle Swarm Optimization

A precise detection of the fault feature parameter of motor current is a new research hotspot in the broken rotor bar (BRB) fault diagnosis of induction motors. Discrete Fourier transform (DFT) is the most popular technique in this field, owing to low computation and easy realization. However, its accuracy is often limited by the data window length, spectral leakage, fence effect, etc. Therefore, a new detection method based on a global optimization algorithm is proposed. First, a BRB fault current model and a residual error function are designed to transform the fault parameter detection problem into a nonlinear least-square problem. Because this optimization problem has a great number of local optima and needs to be resolved rapidly and accurately, a joint algorithm (called TR-MBPSO) based on a modified bare-bones particle swarm optimization (BPSO) and trust region (TR) is subsequently proposed. In the TR-MBPSO, a reinitialization strategy of inactive particle is introduced to the BPSO to enhance the swarm diversity and global search ability. Meanwhile, the TR is combined with the modified BPSO to improve convergence speed and accuracy. It also includes a global convergence analysis, whose result proves that the TR-MBPSO can converge to the global optimum with the probability of 1. Both simulations and experiments are conducted, and the results indicate that the proposed detection method not only has high accuracy of parameter estimation with short-time data window, e.g., the magnitude and frequency precision of the fault-related components reaches 10−4, but also overcomes the impacts of spectral leakage and non-integer-period sampling. The proposed research provides a new BRB detection method, which has enough precision to extract the parameters of the fault feature components.

Electromagnetic forces signature and vibration characteristic for diagnosis broken bars in squirrel cage induction motors

Abstract This paper develops a technique based on vibration magnitudes in vibration signals spectrums for the rotor bar breakages detection in squirrel-cage induction motors. The motor model used in the experiments is established and the skewed rotor is fully taken into account due to its effect on magnetic field. The model is used for the computation of time-domain performance characteristics, such as the magnetic field, air-gap flux density and electromagnetic force including the abnormalities. The vibration of the healthy and faulty motors is calculated based on the electromagnetic forces obtained from the transient magnetic field. The features related to rotor fault detection are estimated by Fast Fourier Transform (FFT) of the electromagnetic force and vibration displacement. According to the diagnostic method based on only a vibration sensor, these features can be easily and accurately found in vibration spectrum. In addition, the modal studies are performed by theoretical and experimental methods respectively, and its purpose is to verify that the broken bars failure will not lead to motor resonance. Experimental results, numerical calculation and analytic analysis suggest that it can provide a powerful and a general approach to the broken bars fault detection.

Advancement of Fault Diagnosis and Detection Process in Industrial Machine Environment

Machine fault diagnosis is a very important topic in industrial systems and deserves further consideration in view of the growing complexity and performance requirements of modern machinery. Currently, manufacturing companies and researchers are making a great attempt to implement efficient fault diagnosis tools. The signal processing is a key step for the machine condition monitoring in complex industrial rotating electrical machines. A number of signal processing techniques have been reported from last two decades conventionally and effectively applied on different rotating machines. Induction motor is the one of widely used in various industrial applications due to small size, low cost and operation with existing power supply. Faults and failure of the induction machine in industry can be the cause of loss of throughput and significant financial losses. As compared with the other faults with the broken rotor bar, it has significant importance because of severity which leads to a serious breakdown of motor. Detection of rotor failure has become significant fault but difficult task in machine fault diagnosis. The aim of this paper is indented to summarizes the fault diagnosis techniques with the purpose of the broken rotor bar fault detection. Keywords: machine fault diagnosis, signal processing technique, induction motor, condition monitoring.

Interval type-2 fuzzy adaptive strategy for fault tolerant control based on new faulty model design: Application to DSIM under broken rotor bars fault

Interval type-2 fuzzy adaptive strategy for fault tolerant control based on new faulty model design: Application to DSIM under broken rotor bars fault

A Survey of Broken Rotor Bar Fault Diagnostic Methods of Induction Motor

Abstract 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.

Detection of Broken Rotor Bars Fault in Induction Motors by Using an Improved MUSIC and Least-Squares Amplitude Estimation

The frequencies and amplitudes of the broken rotor bar (BRB) fault features are the basis for the accurate diagnosis of the BRB fault. However, how to accurately detect their frequency and amplitudes has always been a difficult problem for induction motor fault detection. For this problem, a new fault detection method based on an improved multiple signal classification (MUSIC) and least-squares magnitude estimation is proposed. First, since the fixed-step traversal search reduces the computational efficiency of MUSIC, a niche bare-bones particle swarm optimization (NBPSO) for multimodal peaks search is proposed to improve MUSIC, which is used to compute the frequency values of fault-related and fundamental components in stator current signal. Second, using these frequency values, a fault current signal model is established to convert the magnitude estimation problem into a linear least-squares problem. On this basis, the amplitudes and phases of fault-related and fundamental components could be estimated accurately with the singular value decomposition (SVD). A simulation signal is used to test the new method and the results show that the proposed method not only has higher frequency resolution, but also improves estimation accuracy of parameters greatly even with short data window. Finally, experiments for a real induction motor are performed, and the effectiveness and superiority of the proposed method are proved again.

An Improved Method for Discerning Broken Rotor Bar Fault and Load Oscillation in Induction Motors

A few methods for discerning broken rotor bar (BRB) fault and load oscillation in induction motors have been reported in the literature. However, they all perhaps inevitably fail in adverse cases in which these two phenomena are simultaneously present. To tackle this problem, an improved method for discerning BRB fault and load oscillation is proposed in this paper based on the following work. On the one hand, the theoretical basis is analytically extended to include such an adverse case, yielding some important findings on the spectra of the instantaneous reactive and active powers. A novel strategy is thus outlined to correctly discern BRB fault and load oscillation even when simultaneously present. On the other hand, Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) is adopted as the spectral analysis technique to deal with the instantaneous reactive and active powers, yielding a certain improvement compared to the existing methods, adopting Fast Fourier Transform (FFT). Simulation and experimental results demonstrate that the proposed method can correctly discern BRB fault and load oscillation even when simultaneously present.

Performance Analysis of Squirrel-Cage Induction Motors Under Broken Rotor Bar and Stator Inter-Turn Fault Conditions Using Analytical Modeling

Stator winding inter-turn (SWI) short-circuit fault and broken rotor bar (BRB) fault are two common faulty modes of the squirrel cage induction motors (SCIMs). These faults have various impacts on the motor performance and may be rooted in each other; therefore, they may be presented at the same time in an SCIM. This paper proposes an exact analytical model for the SCIMs, which is able to include the SWI and BRB faults presenting simultaneously or individually with variable severities. Multiple coupled circuit modeling is applied to achieve such analytical model. The magnetic saturation effect is included in the model by applying appropriate air gap function and the rotor bars skew effect is taken into account by using 2-D winding function theory. Simulation results as well as the corresponding experimental results approve the presence of the same harmonics in the stator line currents due to the proposed faults. However, more detailed study gives solution for discrimination of the faults. The attained analytical model can be applied to examine the performance of the related fault diagnosis methods and to design novel fault diagnosis techniques including the model-based ones.

Current Characteristics Analysis and Fault Injection of an Early Weak Fault in Broken Rotor Bar of Traction Motor

Aiming at the destructive and irreversible problems of the broken rotor bar fault of the traction motor, the current characteristics of the early weak fault of the single bar are analyzed, and the broken rotor bar fault simulation injection is realized on the experimental platform. Firstly, a damage factor from the change rule of the metal resistance value of a rotor bar is defined. By means of such a damage factor, the relationship between the severity of the fracture of a single rotor bar and the phase resistance of the traction motor was obtained. Through the superposition principle, the traction motor in the fault of the rotor bar was regarded as a normal motor in which the reverse current source was superimposed on the fault rotor bar. The characteristic values of the stator current fault component were obtained when the single bar had broken. Finally, the relationship between the fault characteristics component of the stator current and the fracture severity of the single rotor bar was established. On this basis, on hardware-in-the-loop fault injection benchmark of the traction drive control system based on dSPACE, the gradual injection of early weak faults in the early broken rotor bar was carried out and the results were analyzed. The experimental data demonstrated the correctness of the current characteristics analysis and fault injection.

IBPSO-Based MUSIC Algorithm for Broken Rotor Bars Fault Detection of Induction Motors

In spectrum analysis of induction motor current, the characteristic components of broken rotor bars (BRB) fault are often submerged by the fundamental component. Although many detection methods have been proposed for this problem, the frequency resolution and accuracy are not high enough so that the reliability of BRB fault detection is affected. Thus, a new multiple signal classification (MUSIC) algorithm based on particle swarm intelligence search is developed. Since spectrum peak search in MUSIC is a multimodal optimization problem, an improved bare-bones particle swarm optimization algorithm (IBPSO) is proposed first. In the IBPSO, a modified strategy of subpopulation determination is introduced into BPSO for realizing multimodal search. And then, the new MUSIC algorithm, called IBPSO-based MUSIC, is proposed by replacing the fixed-step traversal search with IBPSO. Meanwhile, a simulation signal is used to test the effectiveness of the proposed algorithm. The simulation results show that its frequency precision reaches 10−5, and the computational cost is only comparable to that of traditional MUSIC with 0.1 search step. Finally, the IBPSO-based MUSIC is applied in BRB fault detection of an induction motor, and the effectiveness and superiority are proved again. The proposed research provides a modified MUSIC algorithm which has sufficient frequency precision to detect BRB fault in induction motors.

Diagnosis of rotor broken bars faults in squirrel cage induction motor using continuous wavelet transform

PurposeSquirrel cage induction motors suffer from several faults such as rotor broken bar. One of the powerful methods to detect induction motor faults is the line current signature analysis. This paper aims to present a novel algorithm based on continuous wavelet transform (CWT) to diagnose a rotor broken bar fault.Design/methodology/approachThe proposed CWT has high flexibility in monitoring any frequency of interest in a waveform. Based on this transform, stator current frequency spectrum is analyzed to diagnose the rotor broken bar fault. The algorithm distinguishes the healthy motor from the faulted one based on a proper index. The method can be used in steady-state running time of induction motor and under different loading conditions. Experimental results are presented to show the validity of the proposed approach.FindingsThe proposed index considerably increases at the broken bars conditions compared to the healthy conditions. It can clearly diagnose the faulty conditions. The experimental results are found to be in good agreement with the theoretical and simulated results. The proposed method can reduce the noise and spectral leakage effects.Originality/valueThe main contribution of the paper are as follows: using CWT for detection of broken bar faults; introducing a proper index for diagnosing broken bars; and introducing a supplementary index to reduce the noise and spectral leakage effects.

Broken Rotor Bars Fault Detection Based on Envelope Analysis Spectrum and Neural Network in Induction Motors

In this paper, a study has presented the performance of a neural networks technique to detect the broken rotor bars (BRBs) fault in induction motors (IMs). In this context, the fast Fourier transform (FFT) applied on Hilbert modulus obtained via the stator current signal has been used as a diagnostic signal to replace the FFT classic, the characteristics frequency are selected from the Hilbert modulus spectrum, in addition, the different load conditions are used as three inputs data for the neural networks. The efficiency of the proposed method is verified by simulation in MATLAB environment.

Novel approach using Hilbert Transform for multiple broken rotor bars fault location detection for three phase induction motor

This paper presents a new approach to detect the location of multiple broken rotor bars (MBRBs) in induction motor (IM) drive, running under no load and full load conditions using direct in and variable frequency drives. This technique is based on earlier work of location detection of one broken rotor bar. The techniques are tested for various fault severity levels so the detection of the exact location of the fault at early stage helps to reach sufficient time maintenance. In this paper, the authors used Hilbert Transform to extract the fault signature from the stator current envelope which is the low frequency component. Then statistical analysis is applied which produce a formula that is used to get the exact location of the fault in IM rotor.

Coupled Fluid-Thermal Analysis for Induction Motors with Broken Bars Operating under the Rated Load

Thermal stress of the rotor in a squirrel cage induction motor is generated due to the temperature rise, it is also one of the factors causing the broken bar fault because the structure of the rotor would be destroyed if the stress of the rotor bars exceed the strength limit. The coupled fluid-thermal analysis for the induction motor with healthy and broken bar rotors is performed in this paper. Much concern has been committed to establishment of the fluid model on the basis of computational fluid dynamic (CFD) theory. The heat field of the prototypes is analysed so that the effect of the asymmetrical rotor on the motor heat performance can be investigated in depth. Eventually, the efficiency of the presented model and method, for the totally enclosed fan cooled (TEFC) induction motor, can be verified through experimental results. In addition, this paper reports a quantitative analysis of the heat flux distribution of the fault rotor, and the heat flux density of the bars is investigated in detail. Then, the part most likely to break in the rotor as a result of the thermal load is identified.

An On-Line Condition Monitoring System for Incipient Fault Detection in Double-Cage Induction Motor

This paper presents a complete on-line condition monitoring system (CMS) designed to detect incipient broken rotor bar (BRB) fault in double-cage induction motor (DCIM) using the stator current signature. The novelty of this paper lies in the successful combination of one of the latest variants of wavelet techniques, the recursive stationary wavelet packet transform and the widely used tool in quality control, the statistical process control (SPC) in order to deal with several challenges in the continuous monitoring of incipient BRB fault. In fact, breakage in outer bars (OBs) of a DCIM is known to be less prone to exhibit fault signature in the stator current than that of a single-cage IM, especially during the steady motor operation mode. Furthermore, on-line CMSs should at the same time act at normal motor operating mode, including steady and transient modes and be reconfigurable with no motor size dependence. The complete on-line CMS was designed and implemented in a low-cost hardware environment and was validated with 3-kW DCIM featuring incipient OB faults. A 100% fault detection accuracy starting from 3-mm OB breakage during normal motor operating mode, with the autolearning fault threshold, demonstrates the reliability and flexibility of the proposed system.

Partial Inductance Model of Induction Machines for Fault Diagnosis.

The development of advanced fault diagnostic systems for induction machines through the stator current requires accurate and fast models that can simulate the machine under faulty conditions, both in steady-state and in transient regime. These models are far more complex than the models used for healthy machines, because one of the effect of the faults is to change the winding configurations (broken bar faults, rotor asymmetries, and inter-turn short circuits) or the magnetic circuit (eccentricity and bearing faults). This produces a change of the self and mutual phase inductances, which induces in the stator currents the characteristic fault harmonics used to detect and to quantify the fault. The development of a machine model that can reflect these changes is a challenging task, which is addressed in this work with a novel approach, based on the concept of partial inductances. Instead of developing the machine model based on the phases’ coils, it is developed using the partial inductance of a single conductor, obtained through the magnetic vector potential, and combining the partial inductances of all the conductors with a fast Fourier transform for obtaining the phases’ inductances. The proposed method is validated using a commercial induction motor with forced broken bars.