Electrical Fault Diagnosis Research Articles

Condition Monitoring and Feature Extraction of Stator Current Phasors for Enhanced Fault Diagnosis in AC Drive

AC drives are employed mainly in process plants for various applications. In most industrial applications, Induction motor drives are preferred as they are robust, reliable, and efficient. Process industries have seen a paradigm shift from manual control to automatic control. Advancements in power electronics technology have led to smooth control of the induction motor using variable frequency drives over an entire speed range. Variable Frequency Drives (VFD) comprises of Voltage source inverter and a three phase squirrel cage induction motor. Various electric faults that are incipient in the VFD cause an abrupt change in circuit parameters resulting in insulation damage, reduced efficiency, and leading to catastrophic failure of the entire system. Hence, continuous monitoring of the system parameters such as stator current, speed, and the vibration of the machine is essential to diagnose incipient faults in the system. AI techniques have been effectively used in the fault diagnosis of electrical systems. In the proposed work, simulation results of machine learning-based fault diagnosis techniques are presented. Real-time IoT-based condition monitoring of the Variable Frequency Drive is also implemented for enhanced fault diagnosis of various incipient electrical faults in AC drives. The experimental results obtained are validated with the simulation data.

Induction Motor Bearing Faults Diagnosis Using Stator Current and Vibration Analysis

Several researches claim that the vibration technique, widely used in industry, is more efficient compared to the stator current analysis in the diagnosis of mechanical faults. On the other hand, researches show that the current technique is more advantageous especially in the diagnosis of electrical faults, in addition to the simplicity of the sensor positioning. The aim of this paper is to show that both diagnosis techniques can be complementary. For this, a comparative analysis of both diagnosis techniques performances is achieved. To this end, fault diagnosis of rolling element bearings used in induction motors is taken as an example, given the importance of bearings in energy transfer. Experimental results obtained show the complementarity of both techniques and their performances according to the faulty element of bearings.

Shafting Torsional Vibration Analysis of 1000 MW Unit under Electrical Short-Circuit Fault

Taking a 1000 MW turbine generator as the research object, the short-circuit fault in electrical disturbance is analyzed. Since it is very difficult to carry out fault analysis experiments and research on actual systems, simulation analysis is one of the more effective means of electrical fault diagnosis; the simulation’s results approach the actual behavior of the system and are ideal tools for power system analysis, and can provide an empirical basis for practical applications. The short-circuit fault model of the SIMULINK power system is built to analyze the two types of faults of generator terminals short-circuit and power grid short-circuit. The impact load spectrum, fault current and speed fluctuation between low-voltage rotors were extracted and analyzed. The conclusion is that the impact value of electromagnetic torque at the generator terminal is greater than that on the power grid side. The impact value of a two-phase short-circuit at the generator terminal is the largest, and that of a three-phase short-circuit on the power grid side is the smallest. The transient impulse current of a three-phase short-circuit at any fault point is greater than that of a two-phase short-circuit; the impulse current of the grid side short-circuit is much greater than that of the generator terminal short-circuit; the speed fluctuation and fluctuation difference caused by the three-phase short-circuit in the grid side are the largest. The alternating frequency of the transient electromagnetic force of the four kinds of faults avoids the natural frequency of the torsional vibration of the shaft system, and the torsional resonance of the shaft system in the time domain of the short-circuit fault will not appear. However, after the fault is removed, the residual small fluctuation torque in the system has a potential impact on the rotor system. This research shows an analysis of the structural integrity and safe operation of turbine generator units after a short-circuit fault, which can not only be applied to engineering practice, but also provide a theoretical basis for subsequent research.

Fault-Tolerant Control of a Three-Phase Permanent Magnet Synchronous Motor for Lightweight UAV Propellers via Central Point Drive

This paper deals with the development and the performance characterization of a novel Fault-Tolerant Control (FTC) aiming to the diagnosis and accommodation of electrical faults in a three-phase Permanent Magnet Synchronous Motor (PMSM) employed for the propulsion of a modern lightweight fixed-wing UAV. To implement the fault-tolerant capabilities, a four-leg inverter is used to drive the reference PMSM, so that a system reconfiguration can be applied in case of a motor phase fault or an inverter fault, by enabling the control of the central point of the three-phase connection. A crucial design point is to develop Fault-Detection and Isolation (FDI) algorithms capable of minimizing the system failure transients, which are typically characterized by high-amplitude high-frequency torque ripples. The proposed FTC is composed of two sections: in the first, a deterministic model-based FDI algorithm is used, based the evaluation of the current phasor trajectory in the Clarke’s plane; in the second, a novel technique for fault accommodation is implemented by applying a reference frame transformation to post-fault commands. The FTC effectiveness is assessed via detailed nonlinear simulation (including sensors errors, digital signal processing, mechanical transmission compliance, propeller loads and electrical faults model), by characterizing the FDI latency and the post-fault system performances when open circuit faults are injected. Compared with reports in the literature, the proposed FTC demonstrates relevant potentialities: the FDI section of the algorithm provides the smallest ratio between latency and monitoring samples per electrical period, while the accommodation section succeeds in both eliminating post-fault torque ripples and maintaining the mechanical power output with negligible efficiency degradation.

Artificial neural network–based fault diagnosis for induction motors under similar, interpolated and extrapolated operating conditions

The diagnosis of mechanical and electrical faults of induction motors (IMs) has been performed using artificial neural networks (ANN) for similar, interpolated and extrapolated operating speeds. The current and vibration signals of faulty and healthy IMs measured from a Machinery Fault Simulator are used in this work. In total, ten different IM fault conditions have been considered: four mechanical faults (bearing fault, unbalanced rotor, misaligned rotor, and bowed rotor), five electrical faults (broken rotor bar, phase unbalanced fault with two severity levels, and stator winding fault with two severity levels), and one healthy motor condition. An ANN model is developed in which raw time domain data of faulty IMs are used and the fault diagnosis is then performed for the motor’s various operating conditions. Initially, diagnosis is performed to predict and classify the motor faults, for the same operating conditions for which we trained ANN. The diagnosis is then extended for interpolated and extrapolated speeds in order to accomplish the diagnosis when data are not available at all the required operating speeds. From the results, it is found that the present ANN-based diagnosis is effective in the same speed case for various operating conditions (seven speeds as well as three loads). In addition, the diagnosis is found to be satisfactory for all interpolated and extrapolated speed cases. It is also observed that the present IM fault diagnosis is better in the interpolation speed cases than the extrapolation speed cases.

Deep learning based optimum fault diagnosis of electrical and mechanical faults in induction motor

Among all the motors Induction motor (IM) plays a vital role in industry and the demand for their reliability and safe operation is increasing day by day. They are reliable but they do wear out if not maintained timely which in turn will lead to excessive loss of revenue and also man power and this motivates us to develop an intelligent methodology for diagnostic of incipient faults in Induction Motor. This paper focuses on the development of optimum Deep Learning based diagnostic technique to detect the mechanical and electrical faults in Induction Motor. Here, mechanical and electrical faults of different severity level are being tested on machine fault simulator using acquired current and vibrational signals. In this work, optimum model of Deep Neural Network (DNN) based on critical vibration and current features is developed and finally used to timely and effectively detect that which kind of faults the given IM is dealing with. The results are added and discussed in the result and discussions.

Application of optical sensor array in partial discharge signal location of GIS/GIL

The optical sensor array inside the high-voltage equipment can effectively realize the on-line measurement of the high-voltage switch operation state, significantly improve the efficiency and accuracy of SF6 Electrical equipment fault diagnosis, and effectively guarantee the safety and reliability of equipment operation. Firstly, the structure and performance of the micro optical sensor are analyzed, and the system model and data processing unit of the optical sensor array are given. Furthermore, the influence characteristics of single sensor and array sensor Gaussian noise on signal are given, and the noise elimination strategy of observation signal based on Kalman filter is given. The results show that: the optical sensor has a good focusing ability for the optical signal, the light of the sensor array is reflected and propagated many times in the GIS/GIL of the high voltage switch-gear, and the internal partial discharge signal is located more accurately. The research results of this paper can provide important technical support for the GIS/GIL fault monitoring and early warning of the gas insulated electrical equipment on the transmission line.

Development of Pure Electric Vehicle Fault Diagnosis System Based on Virtual Reality Fusion Technology

At the present stage, pure electric vehicles are developing rapidly, and all aspects of pure electric vehicles have been developed comprehensively. However, there are still some problems in the fault diagnosis system of pure electric vehicles. The fault diagnosis system of pure electric vehicles can not meet the needs of consumers. Therefore, at the current stage, the development of fault diagnosis system for pure electric vehicles is A problem. The appearance of virtual reality fusion technology can help the development of pure electric vehicle fault diagnosis system. This paper mainly discusses how to develop the fault diagnosis system of pure electric vehicle based on virtual reality fusion technology.

Classification and Detection of Wind Turbine Pitch Faults Through SCADA Data Analysis

The development of electrical control system faults can lead to increased mechanical component degradation, severe reduction of asset performance, and a direct increase in annual maintenance costs. This paper presents a highly accurate data driven classification system for the diagnosis of electrical control system faults, in particular, wind turbine pitch faults. Early diagnosis of these faults can enable operators to move from traditional corrective or time based maintenance policy towards a predictive maintenance strategy, whilst simultaneously mitigating risks and requiring no further capital expenditure. Our approach provides transparent, human-readable rules for maintenance operators which have been validated by an independent domain expert. Data from 8 wind turbines was collected every 10 minutes over a period of 28 months with 10 attributes utilised to diagnose pitch faults. Three fault classes are identified: “no pitch fault”, “potential pitch fault” and “pitch fault established”. Of the turbines, 4 are used to train the system with a further 4 for validation. Repeated random sub-sampling of the majority fault class was used to reduce computational overheads whilst retaining information content and balancing the training and validation sets. A classification accuracy of 85.50% was achieved with 14 human readable rules generated via the RIPPER inductive rule learner. Of these rules, 11 were described as “useful and intuitive” by an independent domain-expert. An expert system was developed utilising the model along with domain knowledge, resulting in a pitch fault diagnostic accuracy of 87.05% along with a 42.12% reduction in pitch fault alarms.

A multi-layer approach to DN 50 electric valve fault diagnosis using shallow-deep intelligent models

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

Fault Diagnosis and Maintenance Skills of Electrical Control System

With the advancement of industry and technology and the support of advanced technological processes, electrical control system has undergone tremendous changes. Various technologies constitute advanced technology, and their integration into electrical control system enables industrial business to be developed better. However, due to the late start of industrialization in China, there are still inadequate researches, and it is not mature enough to deal with faults in electrical control. The author first introduces the relevant content of electrical control system, then briefly introduces the electrical fault inspection and diagnosis technology, and finally discusses how to repair the electrical control system.

Electric submersible pump broken shaft fault diagnosis based on principal component analysis

Electric Submersible Pump (ESP) is currently widely employed to help enhance production for nonlinear-flowing well with high production and high water cut well. However, ESP broken shaft is common in the oil industry. The broken shaft leads to production disruptions, resulting in significant economic losses. The objective of this paper is to evaluate Principal Component Analysis (PCA) as an unsupervised machine learning technique to detect the cause of the breakage of the ESP shaft. This method was successfully applied in the Penglai block of Bohai Oilfield in China to detect the ESP shaft fracture in real-time. A two-dimensional plot of scores of Principal Component 1 and Principal Component 2 can be used to identify different clusters of the stable region, unstable region and failure region. By this means, potential ESP shaft fracture will be found when the cluster starts deviating away from the stable region. Moreover, a PCA diagnostic model is built to predict the time at which the ESP shaft fracture occurs and to determine the main decision variable most responsible for ESP broken shaft. This paper demonstrates that the application of the PCA method performs well in monitoring the ESP operation system and predicts the impending breakage of ESP shaft with high accuracy.

Joint Sparsity and Collaboration Preserving Projections for Rotating Electrical Machinery Fault Diagnosis

It is always a serious challenge to deal with the influence of noise for fault detection of rotating electrical machines under varying operating environments. To extract the most discriminative features for machine fault classification, a novel dimensionality reduction algorithm called joint sparsity and collaboration preserving projections (JSCPP) is proposed in this article, based on the platform of graph embedding framework. A joint combination of sparse representation (SR) and collaborative representation (CR) is used to construct the intrinsic and penalty graphs. SR emphasizes only on the dictionary atoms highly correlated to the query sample for local optimum while CR takes every atom into consideration for global optimum, thus applying a joint combination would make it benefit from the merits of both for graph construction. After JSCPP seeks the optimal projection directions by minimizing the intra-class compactness and maximizing the inter-class separability, a nearest neighbor classifier is then used. Through two sets of vibration data with white Gaussian noise interfered, the capability of JSCPP is demonstrated by effectively classifying different defect types and severity degrees of faulty bearings. The experimental results have also proved that the classification performance of the proposed method is much superior to the other compared algorithms in both accuracy rates and reliability.

Fault Diagnosis of Tractor Start System Based on Structural Analysis Method

In order to achieve accurate positioning of the electrical failure of the tractor starting system, this paper designs a fault diagnosis system based on structural analysis. Firstly, through the analysis of the traction battery and the starting motor, this paper selects four key faults: battery resistance fault, motor armature resistance fault, motor armature fault and motor joint fault, and establishes the electrical fault model of the tractor starting system. Secondly, this paper divides the variables in the fault model into known variables, unknown variables and fault variables. Then the MATLAB dmperm command is used to analyze the observability and isolation of the unknown variables. We find that we can detecte and isolate four faults by adding a voltage sensor based on the original engine speed sensor. Then select three equation sets as the test set in all the structural minimum set of over-determined equations (MSO sets), and design the residual value of each test set as the observer of the system. The faults detected by different observers are different, but 3 observers can detect 4 faults. Finally, we design the diagnostic strategy of the system: we select [-1,1] as the feasible domain of the observation. If the value of the observer exceeds the feasible domain, the fault characteristic parameter is 1, otherwise it is 0. The fault feature vector is composed of three fault characteristic parameters and used as system fault judgment. In order to verify the correctness of the diagnostic system design, we use MATLAB to build the system simulation platform, which generated 4 faults in 3s-4s, 6s-7s, 9s-10s, 12s-13s respectively. The simulation results show that the results of the three observers are in line with the pre-test. It is determined that the fault feature vector can accurately locate the critical fault. This also shows the advantage of structural analysis for the electrical fault diagnosis of tractor starting systems, which can accurately locate system faults with only a small number of sensors.

Diagnosis and Tolerance of Common Electrical Faults in T-Type Three-Level Inverters Fed Dual Three-Phase PMSM Drives

In this paper, diagnosis and tolerant control schemes have been studied for common electrical faults in T-type three-level inverter fed dual-three phase permanent magnet synchronous motor (PMSM) drives for safety-critical applications. Based on vector space decomposition and double three-phase space vector modulation, diagnostic and tolerant control methods have been developed comprehensively for open-phase faults, open-switch faults, and short-switch faults in T-type three-level inverter fed dual-three-phase PMSM drives, in such way that misdiagnosis of different faults sharing similar faulty features can be avoided. In particular, a two-step diagnostic scheme is proposed in order to simplify the diagnostic process. The first step is to identify the faulty phase and fault category by analyzing current trajectories on harmonic subspace, and the second step is to determine the specific fault type in a small fault-searching area obtained from the first step. In the aspect of fault-tolerant control, a novel current compensation method is proposed for open-phase fault without changing the machine model, space vector diagram, and control framework. Open-switch faults and short-switch faults are tolerated by making full use of the remaining vectors after faults. The validity of the proposed fault diagnosis and tolerance schemes has been verified by experiments.

Reference Signal Injection in Induction Motors Drives to Electrical Failures Detection

This paper shows an alternative method to electrical fault diagnosis in three-phase induction motors using reference signal injection. The proposal is based on the insertion of search reference harmonic signals in the supply voltage by the frequency inverter module of the induction machine and the observation of the signals of the electric current of the motor, preprocessed through the FFT and machine signature component analysis. In order to obtain a greater precision of fault classification and state of degradation through intelligent systems, results are validated by different algorithms whose performance are compared in the following methods: k nearest neighbors, naive Bayes, support vector machine, multilayer perceptron and decision tree. The practical results for the tests with controlled simulation of the electrical faults of broken rotor bars and short circuit in stator windings show the capacity that this insertion technique has when it is used as a tool to diagnose three-phase induction motors. The five proposals of intelligent systems result in a classification with success rates of $$93\%$$ to $$100\%$$ in different classes, separated into types of faults and according to the degradation intensity.

Optical properties and decomposition mechanisms of SF6 at different partial discharge determined by infrared spectroscopy

Probing the decomposition products of Sulfur hexafluoride (SF6) under partial discharge provides important information for fault diagnosis of Gas Insulated Switchgear (GIS). Here, the effects of discharging time, gas pressure, and discharging voltage on SF6 decomposition products have been investigated by Fourier transform infrared (FTIR) spectroscopy. The infrared spectra of decomposition products such as CF4, SOF2, and SO2F2 have been obtained. It can be found that the CF4, SOF2, and SO2F2 concentrations increase over increased discharging time up to 96 hours. The SO2F2 concentration increases while the discharging voltage is raised from 20 kV to 46 kV, due to the rapid deceleration to the lower energy of electron capture and dissociative attachment. The SO2F2 concentration is reduced while the gas pressure is increased from 0.3 MPa to 0.4 MPa. The present results about the SF6 decomposition can be useful for electrical fault diagnosis.

Modeling and detecting the stator winding inter turn fault of permanent magnet synchronous motors using stator current signature analysis

Many researchers have been attracted by the challenges of electrical machines fault detection, diagnosis and monitoring, which provide early warning that could help schedule necessary maintenance to avoid catastrophic consequence. Considerable papers have presented reviews and compared conditions monitoring and fault diagnosis methods for induction machines, but none for permanent magnet machines. In this paper a dynamic fault model for a rotor surface mounted PMSM machine under inter-turn insulation failure is derived. this model allows to study the location and severity of the stator winding fault using electrical circuit magnetically coupled. To achieve this objective, a precise mathematical model that can describe both healthy and fault conditions is developed. To detect stator winding short circuit faults automatically and to estimate the severity of the fault, power spectral density (PSD) was used. It was found that the amplitude of the third harmonic of the current was the most distinctive characteristic for detecting the short circuit fault ratio of the SPMSM.

2-D Magnetomechanical Transient Study of a Motor Suffering a Bar Breakage

The analysis of the vibration response of electrical machines has importance in noise prediction and more recently, diagnosis of electrical faults, especially in the industrial environment, where it is a well-known technique. This work assesses the performance of a strongly coupled two-dimensional (2-D) magnetomechanical approach, as directly available in multiphysics software, for the simulation of an induction machine under heavy operational conditions: a direct-on-line startup. Both healthy and broken bar states are simulated in a time span long enough to allow the detailed study of the varying frequency components. The results yield, in addition to the usual electrical and magnetic quantities, electromagnetic-induced vibration components in the stator. A comparison with current and vibration experimental data is also performed showing a good agreement with variable frequency components and certain limitations concerning their amplitude.

De-embedding Unmatched Connectors for Electric Cable Fault Diagnosis

In order to make accurate reflectometry measurements on electric cables for fault diagnosis, connector de-embedding is a procedure for compensating measurement distortions caused by unmatched connectors. The key step in such a procedure is the characterization of the connectors, which is realized through measurements on a pair of connectors linked by a short cable segment. The analysis for deducing the characteristics of a single connector from measurements made on an assembled pair is known as the bisection problem. In this paper, after recalling the underdetermined nature of the bisection problem, a practically effective de-embedding procedure is proposed based on a particular regularization technique. Numerical examples are presented to illustrate the effectiveness of this procedure.