Fault Diagnosis Of Electrical Machines Research Articles

A Study of Noise Effect in Electrical Machines Bearing Fault Detection and Diagnosis Considering Different Representative Feature Models

As the field of fault diagnosis in electrical machines has significantly attracted the interest of the research community in recent years, several methods have arisen in the literature. Also, raw data signals can be acquired easily nowadays, and, thus, machine learning (ML) and deep learning (DL) are candidate tools for effective diagnosis. At the same time, a challenging task is to identify the presence and type of a bearing fault under noisy conditions, especially when relevant faults are at their incipient stage. Since, in real-world applications and especially in industrial processes, electrical machines operate in constantly noisy environments, a key to an effective approach lies in the preprocessing stage adopted. In this work, an evaluation study is conducted to find the most suitable signal preprocessing techniques and the most effective model for fault diagnosis of 16 conditions/classes, from a low-workload (computational burden) perspective using a well-known dataset. More specifically, the reliability and resiliency of conventional ML and DL models is investigated here, towards rolling bearing fault detection, simulating data that correspond to noisy industrial environments. Diverse preprocessing methods are applied in order to study the performance of different training methods from the feature extraction perspective. These feature extraction methods include statistical features in time-domain analysis (TDA); wavelet packet decomposition (WPD); continuous wavelet transform (CWT); and signal-to-image conversion (SIC), utilizing raw vibration signals acquired under varying load conditions. The noise effect is examined and thoroughly commented on. Finally, the paper provides accumulated usual practices in the sense of preferred preprocessing methods and training models under different load and noise conditions.

State-of-the-Art Techniques for Fault Diagnosis in Electrical Machines: Advancements and Future Directions

Electrical machines are prone to various faults and require constant monitoring to ensure safe and dependable functioning. A potential fault in electrical machinery results in unscheduled downtime, necessitating the prompt assessment of any abnormal circumstances in rotating electrical machines. This paper provides an in-depth analysis as well as the most recent trends in the application of condition monitoring and fault detection techniques in the disciplines of electrical machinery. It first investigates the evolution of traditional monitoring techniques, followed by signal-based techniques such as spectrum, vibration, and temperature analysis, and the most recent trends in its signal processing techniques for assessing faults. Then, it investigates and details the implementation and evolution of modern approaches that employ intelligence-based techniques such as neural networks and support vector machines. All these applicable and state-of-art techniques in condition monitoring and fault diagnosis aid in predictive maintenance and identification and have the highly reliable operation of a motor drive system. Furthermore, this paper focuses on the possible transformational impact of electrical machine condition monitoring by thoroughly analyzing each of the monitoring techniques, their corresponding pros and cons, their approaches, and their applicability. It offers strong and useful insights into proactive maintenance measures, improved operating efficiency, and specific recommendations for future applications in the field of diagnostics.

The Edge Application of Machine Learning Techniques for Fault Diagnosis in Electrical Machines.

The advent of digitization has brought about new technologies that enable advanced condition monitoring and fault diagnosis under the Industry 4.0 paradigm. While vibration signal analysis is a commonly used method for fault detection in literature, it often involves the use of expensive equipment in difficult-to-reach locations. This paper presents a solution for fault diagnosis of electrical machines by utilizing machine learning techniques on the edge, classifying information coming from motor current signature analysis (MCSA) for broken rotor bar detection. The paper covers the process of feature extraction, classification, and model training and testing for three different machine learning methods using a public dataset to then export the results to diagnose a different machine. An edge computing approach is adopted for the data acquisition, signal processing and model implementation on an affordable platform, the Arduino. This makes it accessible for small and medium-sized companies, albeit with the limitations of a resource-constrained platform. The proposed solution has been tested on electrical machines in the Mining and Industrial Engineering School of Almadén (UCLM) with positive results.

Cover Image

The cover image is based on the Review Article Airgap and stray magnetic flux monitoring techniques for fault diagnosis of electrical machines: An overview by Jawad Faiz et al., https://doi.org/10.1049/elp2.12157.

Airgap and stray magnetic flux monitoring techniques for fault diagnosis of electrical machines: An overview

Flux-based fault diagnosis methods have attracted ever-increasing attention from technical communities because of their potential diagnostic capabilities and ability to overcome weaknesses of the traditional methods such as motor current signature analysis (MCSA). This study reviews recent literature on these methods and addresses their strengths and limitations. Different types of flux sensors that can be used for fault diagnosis applications are also discussed. Flux-based approaches are classified based on the type of utilised flux into internal (airgap flux) and external (stray flux) methods and compared with each other. The most common faults in both induction machines (IMs) and permanent magnet machines (PMMs), including inter-turn short-circuit (ITSC), airgap eccentricity (AGE), rotor broken bars (BRB), and irreversible demagnetisation (IDM) faults, are considered in this review. Finally, research gaps are identified, and some suggestions for future research in this area are provided.

Advances in Fault Diagnostics and Post‐Fault Operation of Electrical Drives

Advances in Fault Diagnostics and Post‐Fault Operation of Electrical Drives

Extracting spatially global and local attentive features for rolling bearing fault diagnosis in electrical machines using attention stream networks

A health diagnosis mechanism of rolling element bearings is necessary since the most frequent faults in rotating electrical machines occur in the bearing parts. Recently, convolutional neural networks (CNNs) have redefined the state-of-the-art accuracy for bearing fault detection and identification, extracting location invariant feature mappings without the need for prior expert knowledge. With the use of convolution operations as the core of the process, CNNs consider the local spatial coherence of the input. However, one major drawback of the convolutional models is the weakness to capture global information about the input vector and to derive knowledge about the statistical properties of the latter. The authors propose a deep learning (DL) model that concatenates the features that are produced from two neural streams. Each consists of an attention mechanism that intends to learn different representations of the input vector, and so finally to produce a feature mapping that contains global and spatial locally information. Simulation results on two famous rolling element bearings fault detection benchmarks show the effectiveness of the method. In particular, the proposed DL model achieves 99.60% in the Case Western Reserve University bearing data set and 99.10% in the Paderborn University bearing data set.

Early Detection of Broken Rotor Bars in Inverter-Fed Induction Motors Using Speed Analysis of Startup Transients

The fault diagnosis of electrical machines during startup transients has received increasing attention regarding the possibility of detecting faults early. Induction motors are no exception, and motor current signature analysis has become one of the most popular techniques for determining the condition of various motor components. However, in the case of inverter powered systems, the condition of a motor is difficult to determine from the stator current because fault signatures could overlap with other signatures produced by the inverter, low-slip operation, load oscillations, and other non-stationary conditions. This paper presents a speed signature analysis methodology for a reliable broken rotor bar diagnosis in inverter-fed induction motors. The proposed fault detection is based on tracking the speed fault signature in the time-frequency domain. As a result, different fault severity levels and load oscillations can be identified. The promising results show that this technique can be a good complement to the classic analysis of current signature analysis and reveals a high potential to overcome some of its drawbacks.

An accurate inter-turn short circuit faults model dedicated to induction motors

Safety, disponibility and continuity of industrial systems are major issue in maintenance. In the last decades, these points are the important axes in the field of research. In fact, in many industrial processes research has picked up a fervent place and a particular importance in the area of fault diagnosis of electrical machines, in fact, a fault prognosis has become almost indispensable. The need of a mathematical model of three-phase induction machine, suitable for the simulation of machines behaviour under fault conditions, has received considerable attention. The paper presents a new practical and more precise model for induction motors after introducing inter turn short circuits faults. The proposed model is based on coupled magnetic circuit theory, capable to take into account any electrical asymmetry conditions. To verify the exactitude and the effectiveness of the model, simulation results for induction machine under interturn short circuit fault are presented. In spite of its simplicity, the proposed model is able to provide useful indications for diagnostic purposes. Experimental study is presented at the end of the paper to show that the proposed model predicts the induction machine behavior with a good accuracy.

An Enhanced Binary Particle Swarm Optimization for Optimal Feature Selection in Bearing Fault Diagnosis of Electrical Machines

This study proposes an effective bearing fault diagnosis model based on an optimized approach for feature selection. The measured signal of the electric motor is processed by envelope analysis and Hilbert-Huang transform techniques to extract the potential features. An enhancement of the binary particle swarm optimization algorithm through population initialization strategy based on feature weights, new updating mechanism, and the screening and replacing process create a new and effective feature selection method that improves classification accuracy and reduces data size. The optimal feature subset is provided separately for artificial neural networks, and support vector machine classifier for the final recognition task. In multiple case studies, the proposed feature selection method is evaluated against the benchmark data sets and shows performance comparable to that of other peer competitors. The effectiveness of the proposed bearing fault diagnosis model is verified on different testbeds and achieves high accuracy and robustness under noise conditions. In addition, experimental results are compared with existing fault diagnostic models, showing the high possibility of the proposed bearing fault diagnosis model.

Diagnosis of brushless synchronous generator using numerical modeling

PurposeOn-time fault diagnosis in electrical machines is a critical issue, as it can prevent the development of fault and also reduce the repairing time and cost. In brushless synchronous generators, the significance of the fault diagnosis is even more because they are widely used to generate electrical power all around the world. Therefore, this study aims to propose a fault detection approach for the brushless synchronous generator. In this approach, a novel extension of Relief feature selection method is developed.Design/methodology/approachIn this paper, by taking the advantages of the finite element method (FEM), a brushless synchronous machine is modeled to evaluate the machine performance under two conditions. These conditions include the normal condition of the machine and one diode open-circuit of the rotating rectifier. Therefore, the harmonic behavior of the terminal voltage of the machine is obtained under these situations. Then, the harmonic components are ranked by using the extension of Relief to extract the most appropriate components for fault detection. Therefore, a fault detection approach is proposed based on the ranked harmonic components and support vector machine classifier.FindingsThe proposed diagnosis approach is verified by using an experimental test. Results show that by this approach open-circuit fault on the diode rectifier can effectively be detected by the accuracy of 98.5% and by using five harmonic components of the terminal voltage [1].Originality/valueIn this paper, a novel feature selection method is proposed to select the most effective FFT components based on an extension of Relief method, and besides, FEM modeling of a brushless synchronous generator for normal and one diode open-circuit fault.

Artificial Intelligence-Based Fault Diagnosis for Condition Monitoring of Electric Motors

In the era of globalization, manufacturing industries are facing intense pressure to prevent unexpected breakdowns, reduce maintenance cost and increase plant availability. Induction motors are the most sought-after prime movers in modern-day industries due to their robustness. Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical machines. This paper presents the application of Support Vector Machine (SVM) and Artificial Neural Network (ANN)-based system to diagnose the vibration and Instantaneous Power (IP)-based responses of rolling element bearings and broken rotor bars in an induction motor. The dimensionality of the extracted features was reduced using Principal Component Analysis (PCA) and thereafter the selected features were ranked in order of relevance using the Sequential Floating Forward Selection (SFFS) method for reducing the size of input features and finding the most optimal feature set. A comparative analysis of the effectiveness of SVM and ANN is carried out using statistical parameters extracted from vibration and IP signals. The highest accuracy of 92.5% and 98.2% was achieved for vibration and IP signatures, respectively, using the proposed SFFS-based feature selection technique and ANN classification method. The results reveal that ANN has better performance than SVM and the proposed strategy can be used for automatic recognition of machine faults. The use of this type of intelligent system helps in avoiding unwanted and unplanned system shutdowns due to the failure of the motor.

Multiple coupled circuit modelling approach for squirrel cage induction machine under single‐broken‐bar fault with stator winding functions decomposed in d – q rotor reference frame

Mathematical modelling is a fundamental issue in the condition monitoring and fault diagnosis of electrical machines. This study presents a multiple coupled circuit approach for the modelling of squirrel cage induction machines (SCIMs) with a single broken bar. Derived by means of winding function, the model is capable of incorporating all the harmonics of air-gap magnetomotive force and describing transient machine behaviours. A d–q reference frame fixed on rotor is introduced. Each stator winding function is decomposed into two parts, a q-axis and a d-axis component, both of which induce currents in each rotor mesh. The q-axis and d-axis equivalent circuits are developed for both healthy and faulty cage rotors. It is also shown that a single-broken-bar fault does not affect the q-axis rotor mesh current distribution. The d-axis equivalent circuit for healthy cage rotor is modified further to determine the deviations in the mesh currents due to bar breakage. A detailed model of a 2.2 kW, four-pole, three-phase, 28-rotor-bar SCIM is implemented in MATLAB/Simulink environment, and a laboratory test bed is set up for this machine. A comparison between the simulation and experimental results verifies the effectiveness of the proposed modelling approach.

Industrial drive fault diagnosis through vibration analysis using wavelet transform

The fault diagnosis of electrical machines is significant to reduce the costs of maintenance through early detection of faults, which could be expensive to service. The idea of the research is that the advanced signal processing techniques called wavelet transform is exercised to haul out the faults from the vibration and current signal and a spectral component is being received to diagnose the condition of the machine. The existing system used wavelet transforms for the analysis of the stator faults and rotor faults are taken as a case study to prove the wavelet techniques for fault diagnosis. The proposed investigation of vibration analysis for the induction machines is done through frequency pattern using the decomposition of wavelet packet. The wavelet coefficients for the vibration have been extracted over a wide range of signals and the analysis could be percolated on the frequency domain through HAAR wavelet. In this paper, healthy and unhealthy motor with two broken ball bearings are used to estimate the vibration and current spectrum. Experimentally observed vibration and current signals are transformed into power spectral density through approximate and detailed coefficients with the help of MATLAB tools and the steady state rotor frequency was used to introduce a new frequency pattern for fault diagnosis.

Induction machine faults detection using stator current parametric spectral estimation

Current spectrum analysis is a proven technique for fault diagnosis in electrical machines. Current spectral estimation is usually performed using classical techniques such as periodogram (FFT) or its extensions. However, these techniques have several drawbacks since their frequency resolution is limited and additional post-processing algorithms are required to extract a relevant fault detection criterion. Therefore, this paper proposes a new parametric spectral estimator that fully exploits the faults sensitive frequencies. The proposed technique is based on the maximum likelihood estimator (MLE) and offers high-resolution capabilities. Based on this approach, a fault criterion is derived for detecting several fault types.The proposed technique is assessed using simulation signals, issued from a coupled electromagnetic circuits approach-based simulation tool for mechanical unbalance and electrical asymmetry faults detection. It is afterward validated using experiments on a 0.75-kW induction machine test bed for the particular case of bearing faults.

Application of Rogowski Search Coil for Stator Fault Diagnosis in Electrical Machines

A high precision Rogowski search coil (RSC) is proposed for monitoring of rotating electrical machines. The RSC includes several sets of printed circuit board Rogowski coil, in which any set includes two printed circuit boards with imprinted coils located adjacent to each other. The RSC offers a number of advantages including compact size, great accuracy, high bandwidth, low cost, and it is applicable even for already fabricated electric machine. Performance of the RSC is evaluated by some experiments.

Application of artificial intelligence to stator winding fault diagnosis in Permanent Magnet Synchronous Machines

This paper proposes a new methodology to solve the problem of fault diagnosis in electrical machines. The fault diagnosis method presented in this paper is, first, able to provide information about the location of a short-circuit fault in a stator winding. Secondly, the method enables the estimation of fault severity by specifying the number of short-circuited turns during a fault. A cluster of Focused Time-Lagged neural networks are combined with the Particle Swarm Optimization algorithm for proposed fault diagnosis method. This method is applied to the stator windings of a Permanent Magnet Synchronous Machine. Each neural network, in the cluster, is trained to correlate the zero-current component to the number of short-circuited turns in the stator windings. The zero-current component, different from the zero-sequence current, are obtained by summing the instantaneous values of current on all phases of the stator winding during the diagnosis procedure. The neural networks are trained offline with the Extended Kalman Filter method using fault data from both computer simulations and an actual Permanent Magnet Synchronous Machine. The use of the Extended Kalman Filter method, for training, ensures that the neural network cluster used can be re-trained online to make the fault diagnosis system adapt to changing operational conditions. Results from both computer simulation and actual machine data are presented to show the performance of the neural network cluster and the Particle Swarm Optimization algorithm.

Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks

Bearing degradation is the most common source of faults in electrical machines. In this context, this work presents a novel monitoring scheme applied to diagnose bearing faults. Apart from detecting local defects, i.e., single-point ball and raceway faults, it takes also into account the detection of distributed defects, such as roughness. The development of diagnosis methodologies considering both kinds of bearing faults is, nowadays, subject of concern in fault diagnosis of electrical machines. First, the method analyzes the most significant statistical-time features calculated from vibration signal. Then, it uses a variant of the curvilinear component analysis, a nonlinear manifold learning technique, for compression and visualization of the feature behavior. It allows interpreting the underlying physical phenomenon. This technique has demonstrated to be a very powerful and promising tool in the diagnosis area. Finally, a hierarchical neural network structure is used to perform the classification stage. The effectiveness of this condition-monitoring scheme has been verified by experimental results obtained from different operating conditions.

A Novel Industrial Wireless Sensor Network for Condition Monitoring and Fault Diagnosis of Electrical Machines

A novel industrial wireless sensor network (IWSN) for condition monitoring and fault diagnosis of electrical machines is presented, in which on-sensor fault diagnosis based on principal component analysis is explored to address the tension between the high system requirements of electrical machine monitoring and the resource constrained characteristics of IWSN sensor nodes. The prototype system is evaluated with a single phase induction motor monitoring system. Normal motor working conditions and two types of motor faults, ie. loose feet and mass imbalance, are monitored to validate the feasibility of the proposed system. The results show that using on-sensor fault diagnosis can reduce transmission data by 99.8%, decrease energy consumption, and prolong node lifetime from 106 to 153 h, an increase of 44%. The experimental results also indicate that the proposed approach has high fault diagnosis accuracy.

A novel industrial wireless sensor network for condition monitoring and fault diagnosis of electrical machines

A novel industrial wireless sensor network (IWSN) for condition monitoring and fault diagnosis of electrical machines is presented, in which on-sensor fault diagnosis based on principal component analysis is explored to address the tension between the high system requirements of electrical machine monitoring and the resource constrained characteristics of IWSN sensor nodes. The prototype system is evaluated with a single phase induction motor monitoring system. Normal motor working conditions and two types of motor faults, ie. loose feet and mass imbalance, are monitored to validate the feasibility of the proposed system. The results show that using on-sensor fault diagnosis can reduce transmission data by 99.8%, decrease energy consumption, and prolong node lifetime from 106 to 153 h, an increase of 44%. The experimental results also indicate that the proposed approach has high fault diagnosis accuracy.