Proposed Fault Detection Method Research Articles

Time Frequency Analysis Based Fault Detection in PV Array Using Scaling Basis Chirplet Transform

ABSTRACTPhotovoltaic (PV) arrays have gained significant attention in recent years due to their potential for sustainable energy generation. However, the reliable operation of PV arrays is crucial for optimal performance and long‐term durability. The early detection of faults in PV arrays is vital to prevent further damage, improve maintenance strategies, and ensure uninterrupted energy production. In this study, we propose a novel fault detection method based on Time Frequency Analysis (TFA) using the Scaling Basis Chirplet Transform (SBCT). In this proposed fault detection method, PV array signal is decomposed into a set of chirplets using the SBCT. The chirplets represent localized time‐frequency components that can capture the dynamic behavior of the PV array signal. To evaluate the effectiveness of the proposed method, extensive simulations and experiments are conducted using real‐world PV array data. The SBCT with combination of various machine learning algorithms is proposed to detect faults in PV array. SBCT in combination with Support Vector Machine, Decision Tree, Random Forest, and ANN classifiers are able to detect faults in PV array with 99%, 98.5%, 99.2%, and 99.5% accuracies in no shading condition and 88%, 85%, 89%, and 89.5% accuracies in severe shading condition. The proposed method achieves high accuracy and robustness in detecting various types of faults in PV arrays, even in the presence of noise and uncertainties. The proposed fault detection method using TFA based on the SBCT offers a promising solution for efficient and reliable fault detection in PV arrays. It enables early fault detection, facilitating timely maintenance and minimizing energy losses. The proposed approach can contribute to enhancing the overall performance, reliability, and lifespan of PV arrays, thereby advancing the adoption of renewable energy sources and promoting sustainable development.

Edge Computing and Fault Diagnosis of Rotating Machinery Based on MobileNet in Wireless Sensor Networks for Mechanical Vibration.

With the rapid development of the Industrial Internet of Things in rotating machinery, the amount of data sampled by mechanical vibration wireless sensor networks (MvWSNs) has increased significantly, straining bandwidth capacity. Concurrently, the safety requirements for rotating machinery have escalated, necessitating enhanced real-time data processing capabilities. Conventional methods, reliant on experiential approaches, have proven inefficient in meeting these evolving challenges. To this end, a fault detection method for rotating machinery based on mobileNet in MvWSNs is proposed to address these intractable issues. The small and light deep learning model is helpful to realize nearly real-time sensing and fault detection, lightening the communication pressure of MvWSNs. The well-trained deep learning is implanted on the MvWSNs sensor node, an edge computing platform developed via embedded STM32 microcontrollers (STMicroelectronics International NV, Geneva, Switzerland). Data acquisition, data processing, and data classification are all executed on the computing- and energy-constrained sensor node. The experimental results demonstrate that the proposed fault detection method can achieve about 0.99 for the DDS dataset and an accuracy of 0.98 in the MvWSNs sensor node. Furthermore, the final transmission data size is only 0.1% compared to the original data size. It is also a time-saving method that can be accomplished within 135 ms while the raw data will take about 1000 ms to transmit to the monitoring center when there are four sensor nodes in the network. Thus, the proposed edge computing method shows good application prospects in fault detection and control of rotating machinery with high time sensitivity.

Development of overcurrent relay based on wavelet transform for fault detection in transmission line

This study proposes a protection relay using a microcontroller to detect and classify faults in transmission lines based on the wavelet transform. An experimental model was constructed from an actual 115 kV transmission system prototype. The current signal was observed based on the fault type, phase, and position. Clark’s transform and the discrete wavelet transform (DWT) were applied to transform signals for analysis. Moreover, the performance of fault detection based on the output signals of Clark’s transform (alpha sequence, beta sequence, and zero sequence current) was compared to the performance of the alternative proposed fault detection method, which is based on the combining factor between alpha and beta sequence current. In addition, the influence of DWT level on fault analysis is also considered and is used to confirm the accuracy of fault detection. Results show that the proposed method is efficient for fault detection and classification. This finding allows the researcher to choose the appropriate analytical method. Moreover, it can also be used as the basis for overcurrent relay algorithm design in the effort to develop more advanced technologies.

Turn‐to‐turn and phase‐to‐phase short circuit fault detection of wind turbine permanent magnet generator based on equivalent magnetic network modelling by wavelet transform approach

AbstractOne of the most common faults in electric machines is a turn‐to‐turn short circuit (TTSC), which may destroy coil insulation and demagnetise the magnet. In addition, the phase‐to‐phase short circuit (PPSC) fault, which can have even more destructive effects than the TTSC fault, is introduced and analysed. The equivalent magnetic network (EMN) method, with high modelling accuracy and a short computation time, is employed for healthy and faulty machines. The current signal under fault conditions is analysed in the dqo frame, showing the presence of the second harmonic component in its waveform. This fault detection index is processed using the signal processing technique of discrete(wavelet transform (DWT). Besides, energy analysis is used to distinguish TTSC and PPSC faults. Finally, finite element and EMN modelling results are compared with the experimental data of the prototyped permanent magnet generator. The results show that the combination of the proposed EMN method and DWT has very good accuracy and speed. Furthermore, the proposed fault detection method remains unaffected by various linear loads with different power factors.The cover image is based on the article Turn‐to‐turn and phase‐to‐phase short circuit fault detection of wind turbine permanent magnet generator based on equivalent magnetic network modelling by wavelet transform approach by Mehrage Ghods et al., https://doi.org/10.1049/elp2.12452

A two-stage Duffing equation-based oscillator and stochastic resonance for mechanical fault diagnosis

Incipient fault-induced weak impulses will not be detected easily using the existing fault extraction methods. Stochastic resonance can flexibly use noises to enhance the signal-to-noise ratio, but it lacks the ability of anti-interference to obtain robustness and reliability fault detection performance. A two-stage method is presented to diagnose faults in mechanical components. The first is to judge the fault existence according to the characteristics of the phase space trajectory diagram of the Duffing oscillator. The second is to employ stochastic resonance method based on the Duffing equation to improve the signal-to-noise ratio of the raw signal, in which the two barrier parameters in the Duffing equation-based bistable system are carefully selected using the artificial bee colony algorithm. In the present two-stage method, the existence of faults and further the fault types in mechanical components are stepwise detected to make full usages of the advantages of Duffing oscillator and Duffing equation-based stochastic resonance method. Numerical simulations and two experimental cases verify the performance of the proposed method for mechanical components fault detection in a strong noise environment.

Simulation-driven fault detection for the gear transmission system in major equipment

Scholars and engineers attach great importance to fault detection in mechanical systems due to the unpredictable faults that arise from long-term operations under complex and extreme conditions. The fact that each type of fault embodies unique characteristics makes it challenging to obtain sufficient fault samples, and conventional machine learning methods fail to provide satisfactory fault diagnosis results. To address this issue, a simulation-driven fault detection method has been proposed in this paper. Firstly, the DT model of the gear transmission system was established. An improved multi-objective sparrow search algorithm (MOSSA) was employed to update the model and obtain an adequate number of simulation fault samples as well. Secondly, a two-stage adversarial domain adaptation model with full-scale feature fusion (ADAM-FF) was utilized to align and integrate the features of simulated and generated fault samples. This enables model training and classification of combined samples, facilitating the detection of unknown faults in actual measurements. Lastly, a simulation-driven equipment health index assessment model which accurately and non-destructively evaluates the degradation status of the equipment was introduced. This model effectively quantifies the extent of equipment degradation, thereby facilitating the transfer from the simulation realm to practical engineering applications. To validate the effectiveness of the proposed fault detection method, an experimental study was conducted on the extruder gear reducer of a petrochemical enterprise. The proposed fault detection method has the potential for widespread application across a range of large-scale mechanical equipment. As such, the utilization of this method will enable proactive maintenance planning, ensure safe and stable equipment operations, and minimize energy loss.

Statistics Pattern Cointegration Analysis-Based Bit Bounce Detection for Drilling Process

Fault detection is essential for the safety of drilling process. Without proper detection and appropriate treatment of bit bounce, it may result in serious drilling accidents. The data distribution shift will lead to the nonstationary property. Yet the statistics of drilling parameters may have similar equilibrium relationships across different depths due to the existence of automatic drilling control system. To solve the issue of distribution shift, a statistics pattern cointegration analysis based bit bounce detection method for drilling process is proposed. The statistics pattern of drilling parameters is constructed. The cointegration analysis is conducted to discover equilibrium relationships among nonstationary statistics for process monitoring. The corresponding alarm deadband is designed through the analysis of alarm duration and alarm deviation. An industrial case study from an actual drilling process is conducted to demonstrate the effectiveness of the proposed fault detection method. The proposed statistics pattern cointegration analysis approach is conducive to maintaining a satisfactory fault detection performance in the case of distribution shift in the industrial process.

Intelligent fault detection strategy for knowledge entities in fault semantic networks of distribution network based on siamese networks.

The advent of smart grid technologies has brought about a paradigm shift in the management and operation of distribution networks, allowing for intricate system information to be encapsulated within semantic network models. These models, while robust, are not immune to faults within their knowledge entities, which can arise from a myriad of issues, potentially leading to verification failures and operational disruptions. Addressing this critical vulnerability, our research delves into the development of a novel fault detection methodology specifically tailored for the knowledge entity variables of semantic networks in distribution networks. In our approach, we first construct a state space equation that models the behavior of knowledge entity variables in the presence of faults. This foundational framework enables us to apply an unknown input observer strategy to effectively detect anomalies within the system. To bolster the fault identification process, we introduce the innovative use of a siamese network, a neural network architecture which is proficient in differentiating between similar datasets. Through simulation scenarios, we demonstrate the efficacy of our proposed fault detection method.

Formula omitted] fault detection observer design for nonlinear conformable fractional-order systems

This study is concerned with the design of fault detection observers for nonlinear conformable fractional-order systems. An H∞/H− fault detection scheme is applied to maximize the sensitivity of faults and minimize the effect of disturbances. Based on basic fractional calculus lemmas and fractional-order Lyapunov stability theory, sufficient conditions are obtained to design a fault detection observer. Finally, the advantages and effectiveness of the proposed fault detection method are illustrated through a numerical example.

Fault Detection and Tolerant-Control Method for Sensorless Disposable Soft Manipulators

Disposable soft manipulators provide a promising solution for robot-assisted surgeries in low- and middle-income countries due to their cost advantages. The surgical applications are of high reliability requirement, while the low-cost materials and mechanical structures impair robot reliability, and therefore, some measures should be taken to enhance the reliability of disposable soft manipulators, for example, fault detection, identification and tolerant control. This paper proposes a new fault detection algorithm for locked-motor faults of soft manipulators. By leveraging the projection Jacobian matrix, it requires less actuators than conventional methods. In terms of the uncertainty introduced by the projection Jacobian, an adaptive algorithm is proposed to compensate for it. Dedicated experiments are conducted to validate our proposed fault detection method and results prove its efficacy for monitoring system health conditions and controlling the system under faulty modes.

Vibration Analysis for Fault Diagnosis in Induction Motors Using One-Dimensional Dilated Convolutional Neural Networks

Motor faults not only damage the motor body but also affect the entire production system. When the motor runs in a steady state, the characteristic frequency of the fault current is close to the fundamental frequency, so it is difficult to effectively extract the fault current components, such as the broken rotor bar. In this paper, according to the characteristics of electromagnetic force and vibration, when the rotor eccentricity and the broken bar occur, the vibration signal is used to analyze and diagnose the fault. Firstly, the frequency, order, and amplitude characteristics of electromagnetic force under rotor eccentricity and broken bar fault are analyzed. Then, the fault vibration acceleration value collected by a one-dimensional dilated convolution pair is extracted, and the SeLU activation function and residual connection are introduced to solve the problem of gradient disappearance and network degradation, and the fault motor model is established by combining average ensemble learning and SoftMax multi-classifier. Finally, experiments of normal rotor eccentricity and broken bar faults are carried out on 4-pole asynchronous motors. The experimental results show that the accuracy of the proposed method for motor fault detection can reach 99%, which meets the requirements of fault motor detection and is helpful for further application.

A data-based fault detection scheme for the stratospheric airship control system

This brief proposed an innovative fault detection method based on analytical data for the stratospheric airship control system. The control system considered is subject to both space disturbance and nonlinear characteristics; the faults of sensors and actuators are all taken into account. The proposed method is developed in two phases. In the first phase, the moving window kernel principal component analysis is employed to construct the fault detection model with the training data under normal operating conditions and update the fault detection model online until abnormal data are detected. Second, a fault detection model updating mechanism is designed to reduce computational complexity and cost with a clustering algorithm, which compounds the mean shift clustering with weighted Euclidean distance to reflect the data density distribution feature to make the updating to be adaptive. Finally, the proposed method is applied to detect fault for an illustrative simulation stratospheric airship control model. The fault detection results validate the effectiveness of proposed fault detection method for different sensor and actuator fault cases. Comparing to some extended moving window kernel principal component analysis method, the proposed method reduces the computational cost significantly.

Empirical mode decomposition and Hilbert–Huang transform-based eccentricity fault detection and classification with demagnetization in 120 kW interior permanent magnet synchronous motors

Permanent magnet synchronous motors (PMSMs) are extensively used in electric vehicles (EVs) owing to their high power density and efficiency. However, their safety and reliability have not been verified sufficiently. Failure of PMSMs in EVs can cause performance degradation, increased maintenance costs, and catastrophic accidents. Therefore, early detection of faults in PMSMs is necessary for an efficient and safe operation. In this study, static, dynamic, and mixed eccentricity faults in 120 kW interior permanent magnet synchronous motors (IPMSMs) were diagnosed and distinguished from uniform demagnetization fault by monitoring the Hilbert spectrum of the stator current. Experiments were carried out for different speeds, loads, and fault severities to analyze the effect of operating conditions on the proposed fault detection method. The phase current data were decomposed into set of intrinsic mode functions (IMFs) with empirical mode decomposition (EMD) method and Hilbert transform was applied for each IMFs to calculate the instantaneous frequency and energy. The resultant Hilbert spectrum exhibited instantaneous frequency distortions with an instantaneous energy surge under an eccentricity fault. The effect of thresholding in EMD was analyzed to improve the accuracy of the proposed method under high-load and high-speed conditions. Also, the experimental results showed that eccentricity and uniform demagnetization faults can be classified by monitoring the overall instantaneous energy.

Stator current signal crossing for fault diagnosis of self-excited induction generators

This paper presents a novel method for modelling and diagnosis of electrical and mechanical faults in fixed-Speed Self-Excited Induction Generators (SEIGs) operating in autonomous mode in a small-scale wind energy system. The proposed method is validated using the finite element method. After the selection of the magnetising capacitors, the self-excitation process is performed under no-load conditions. Once the stator voltage is established, a symmetrical three-phase load is connected. The fault detection method introduced here is called Stator Current Signal Crossing (SCSC). The SCSC extracts a new signal from the stator currents, that enables the detection of stator inter turn shortcircuits, broken rotor bars, and dynamic eccentricity faults in SEIGs. A spectral analysis of SCSC using the Fast Fourier Transform (FFT) algorithm is used to precisely locate the induced fault components. What sets this fault-tracking method apart from its predecessors is its exceptional ability to detect faults of any magnitude by analysing the modulation of the SCSC signal. These faults are directly identified by the presence of distinct harmonics, each indicative of a specific type of fault. This study also focuses on the SEIG in a wind energy system, whereas previous works have mainly addressed the induction machine in motor mode. In contrast, previous methods involved analysing a single current signal and isolating specific harmonics from a wide frequency range. The effectiveness of the proposed fault detection method and the self-excitation process are illustrated by simulation results and spectral analysis.

Fault Diagnosis/Separation of Surface Mounted Permanent Magnet Synchronous Machine by Current and its Homopolar Orders Analysis

Performance analysis of Surface Mounted Permanent Magnet Synchronous Machine (SM-PMSM) is investigated in this paper. The machine dynamic in both healthy and faulty cases is analyzed, where dynamic, static, and mixed eccentricities as well as inter-turn and demagnetization faults are considered by a unique model. Stator Current Signature Analysis (SCSA) is used for the fault detection, and a method is proposed for the faults diagnosis and identification. Also, a flexible Magnetic Equivalent Circuit (MEC) with adjustable accuracy is used to model saturation effect thanks to its more flexibility and shorter processing time compared to the Finite-Element Method (FEM). The Power Spectral Density (PSD) is used for the faults detection, where analysis of the homopolar orders is proposed to be able to differentiate various faults from each other. Performed validation by experimental and FEM-based results shows the effectiveness of the presented model and proposed fault detection method.

Fault Diagnosis of Train Body Sign Abnormal Pattern with Deep Learning Based Target Detection

<p>With the development of high-speed trains in recent years, security issues have received more attention. Automatic visual inspection of the train operation system for detecting abnormalities has become a fundamental element to guarantee the safety of the train operation. Train body sign patterns like the loss and fracture of signs and lock catch (SLC) on the electrical box cover (EBC) affect the regular operation of the train electrical system. In this paper, to ensure the safe operation of the train, a novel method combining a faster region-based convolutional neural network (Faster R-CNN) and similarity metrics is proposed to detect the abnormality of SLCs on train EBC. First, the positions of body train signs of multiple sizes are located by Faster R-CNN. Then, the regions of interest (ROI) are cut out and resized to the same size as the corresponding template images. Finally, by similarity measures, the status of the train body sign pattern is judged by comparing with the given threshold similarity value between ROIs and the template images. It is worth noting that the combination of Faster R-CNN and cosine similarity renders high accuracy in small target detection and strong robustness in image similarity comparison. The effectiveness of the proposed fault detection method and its superiority over the other types of combined methods are verified by actual experiments on the train of Guangzhou Metro Line 2.</p> <p> </p>

Fault Detection for Motor Drive Control System of Industrial Robots Using CNN-LSTM-based Observers

The complex working conditions and nonlinear characteristics of the motor drive control system of industrial robots make it difficult to detect faults. In this paper, a deep learning-based observer, which combines the convolutional neural network (CNN) and the long short-term memory network (LSTM), is employed to approximate the nonlinear driving control system. CNN layers are introduced to extract dynamic features of the data, whereas LSTM layers perform time-sequential prediction of the target system. In terms of application, normal samples are fed into the observer to build an offline prediction model for the target system. The trained CNN-LSTM-based observer is then deployed along with the target system to estimate the system outputs. Online fault detection can be realized by analyzing the residuals. Finally, an application of the proposed fault detection method to a brushless DC motor drive system is given to verify the effectiveness of the proposed scheme. Simulation results indicate the impressive fault detection capability of the presented method for driving control systems of industrial robots.

Switch Fault Detection and Localization for T-Type Converter

Over the past decade, multilevel converters have received considerable interest in medium voltage applications. A large number of active switches in a multilevel converter increases the probability of switch failure and decreases the reliability of the system. An open switch fault can lead to a malfunction of the whole system and should be diagnosed as soon as possible. In this paper, an open-switch fault diagnosis method for a five-level H-Bridge T-type converter is presented. The proposed fault detection method and the fault localization algorithm are based on the switching patterns and the converter output voltage level observation. This approach is capable to detect and localize an open circuit fault for all switches. First, it is validated by simulations in Matlab Simulink and Simscape toolbox environments. Experimental results are also presented and discussed to validate the simulation results.

Long–short-term memory encoder–decoder with regularized hidden dynamics for fault detection in industrial processes

The ability of recurrent neural networks (RNN) to model nonlinear dynamics of high dimensional process data has enabled data-driven RNN-based fault detection algorithms. Previous studies have focused on detecting faults by identifying the discrepancies in data distribution between the faulty and normal data, as reflected in prediction errors generated by RNN models. However, in industrial processes, variations in data distribution can also result from changes in normal control setpoints and compensatory control adjustments in response to disturbances, making it hard to differentiate between normal and faulty conditions. This paper proposes a fault detection method utilizing a long short-term memory (LSTM) encoder–decoder structure with regularized hidden dynamics and reversible instance normalization (RevIN) to compactly represent high-dimensional measurements for effective monitoring. During training, the hidden states of the model are regularized to form a low-dimensional latent space representation of the original multivariate time series data. As a result, the prediction errors of the latent states can be used to monitor the abnormal dynamic variations, while the reconstruction errors of the measured variables are used to monitor the abnormal static variations. Furthermore, the proposed indices can reflect operating conditions, even when the distribution of test data changes, which helps distinguish faults from normal adjustments and disturbances that controllers can settle. Data from numerical simulation and the Tennessee Eastman process are used to illustrate the effectiveness of the proposed fault detection method.

A Novel Two-Stage Fault-Detection Method Based on Constrained RVM and Integrating LDA With Minimax Probability Machine

This article presents a novel two-stage fault-detection (FD) method composed of a preclassifier and a reclassifier for complex industrial processes, where the preclassifier is developed by combining linear discriminant analysis and minimax probability machine to reduce dimensionality and classify fully separable data with low computation time. For overlapping data that cannot be separated by the preclassifier, a reclassifier is designed by constructing a constrained relevance vector machine (RVM), according to Neyman–Pearson principle, to decrease the missed alarm rate. The reclassifier has a lower computational load than traditional RVM due to the amount and dimensionality of reclassified data reduced by the first stage, thereby a balance between detection accuracy and computational burden of the whole FD method can be achieved. Finally, an industrial benchmark of Tennessee–Eastman process is utilized to verify the effectiveness of the proposed FD method.