Online Fault Diagnosis Research Articles

Feature-weighted Random Forest with Boruta for Fault Diagnosis of Satellite Attitude Control Systems

The performance of random forest (RF) based satellite attitude control system (ACS) fault diagnosis methods is limited by uninformative features in high-dimensional data. To solve this problem, we proposed a feature-weighted random forest with Boruta (FWRFB) based fault diagnosis method is proposed for fault diagnosis of ACSs. Firstly, a Boruta feature selection algorithm is used to obtain a feature set and determine significant feature weights. Subsequently, a novel feature-weighted random forest (FWRF) algorithm is designed, which utilizes feature-weighted random sampling instead of simple random sampling to generate feature subsets in the RF. The FWRFB effectively utilizes the feature information while mitigating noise interference. Finally, a FWRFB-based diagnostic module is developed for online fault diagnosis of ACSs. The effectiveness of the proposed method is verified by the ACS data from a semi-physical simulation platform.

Infrared Image Object Detection Algorithm for Substation Equipment Based on Improved YOLOv8

Substations play a crucial role in the proper operation of power systems. Online fault diagnosis of substation equipment is critical for improving the safety and intelligence of power systems. Detecting the target equipment from an infrared image of substation equipment constitutes a pivotal step in online fault diagnosis. To address the challenges of missed detection, false detection, and low detection accuracy in the infrared image object detection in substation equipment, this paper proposes an infrared image object detection algorithm for substation equipment based on an improved YOLOv8n. Firstly, the DCNC2f module is built by combining deformable convolution with the C2f module, and the C2f module in the backbone is replaced by the DCNC2f module to enhance the ability of the model to extract relevant equipment features. Subsequently, the multi-scale convolutional attention module is introduced to improve the ability of the model to capture multi-scale information and enhance detection accuracy. The experimental results on the infrared image dataset of the substation equipment demonstrate that the improved YOLOv8n model achieves mAP@0.5 and mAP@0.5:0.95 of 92.7% and 68.5%, respectively, representing a 2.6% and 3.9% improvement over the baseline model. The improved model significantly enhances object detection accuracy and exhibits superior performance in infrared image object detection in substation equipment.

Fast Online Fault Diagnosis for PMSM Based on Adaptation Model

Fast Online Fault Diagnosis for PMSM Based on Adaptation Model

A Multidimensional Health Indicator Based on Autoregressive Power Spectral Density for Machine Condition Monitoring.

Condition monitoring (CM) is the basis of prognostics and health management (PHM), which is gaining more and more importance in the industrial world. CM, which refers to the tracking of industrial equipment's state of health during operations, plays, in fact, a significant role in the reliability, safety, and efficiency of industrial operations. This paper proposes a data-driven CM approach based on the autoregressive (AR) modeling of the acquired sensor data and their analysis within frequency subbands. The number and size of the bands are determined with negligible human intervention, analyzing only the time-frequency representation of the signal of interest under normal system operating conditions. In particular, the approach exploits the synchrosqueezing transform to improve the signal energy distribution in the time-frequency plane, defining a multidimensional health indicator built on the basis of the AR power spectral density and the symmetric Itakura-Saito spectral distance. The described health indicator proved capable of detecting changes in the signal spectrum due to the occurrence of faults. After the initial definition of the bands and the calculation of the characteristics of the nominal AR spectrum, the procedure requires no further intervention and can be used for online condition monitoring and fault diagnosis. Since it is based on the comparison of spectra under different operating conditions, its applicability depends neither on the nature of the acquired signal nor on a specific system to be monitored. As an example, the effectiveness of the proposed method was favorably tested using real data available in the Case Western Reserve University (CWRU) Bearing Data Center, a widely known and used benchmark.

Sensor Network Attack Synthesis against Fault Diagnosis of Discrete Event Systems.

This paper investigates the problem of synthesizing network attacks against fault diagnosis in the context of discrete event systems (DESs). It is assumed that the sensor observations sent to the operator that monitors a system are tampered with by an active attacker. We first formulate the process of online fault diagnosis under attack. Then, from the attack viewpoint, we define a sensor network attacker as successful if it can degrade the fault diagnosis in the case of maintaining itself as undiscovered by the operator. To verify such an attacker, an information structure called a joint diagnoser (JD) is proposed, which describes all possible attacks in a given attack scenario. Based on the refined JD, i.e., stealthy joint diagnoser (SJD), we present an algorithmic procedure for synthesizing a successful attacker if it exists.

Design of On-line Monitoring and Fault Diagnosis Platform for Oil-immersed Shunt Reactor

Abstract With the development of society and technology, the demand for power supply and reliability is increasing. In the power system, the reactor plays an important role as a key equipment. However, due to long-term operation, the reactor is affected by factors such as loss, aging and hidden faults, and its reliability gradually decreases. The operation state of the reactor is directly related to the operation effect of the power grid and the quality of the user’s electricity. Therefore, the application of reactor online monitoring and fault diagnosis in real life is very important. This paper considers the actual operation of the coastal power grid. Based on Qt software development platform, C + + language and MySQL database, an online monitoring and fault diagnosis platform for oil-immersed shunt reactors is developed. The application of the platform takes the online monitoring and fault diagnosis method of dissolved gas in oil as the core. The platform realizes the basic information management, online monitoring, fault diagnosis and suggestions for operation and maintenance of the reactor.

Development of All Solid State Nanosecond Pulse Transformer Fault Detection Device Based on Marx Circuit

Abstract a nanosecond pulse generator with a fast leading edge for transformer fault diagnosis is developed. The device is composed of a full solid-state nanosecond pulse generator based on Marx circuit and with a tail cut switch and a field programmable logic gate array (FPGA) device. Among them, the nanosecond pulse source is mainly composed of DC power supply, control circuit and main circuit. The main circuit is a 9-level modular Marx circuit, using MOSFET as the main switch and tail cut switch; The control circuit generates a synchronous trigger pulse signal, which drives the MOSFET to work synchronously after being isolated through the optical fiber. The output nanosecond pulse voltage parameters are adjustable amplitude 0-8kv, pulse width 100˜1000ns, repetition frequency 1hz˜1000hz, rising edge 30ns and falling edge 40ns. The transformer fault diagnosis platform is built, and the transformer off-line and on-line fault diagnosis is carried out, which paves the way for further exploration of transformer fault diagnosis technology.

Research on Online Monitoring and Fault Diagnosis Technology of Intelligent Electrical Equipment

Research on Online Monitoring and Fault Diagnosis Technology of Intelligent Electrical Equipment

Turbine bearing fault diagnosis based on embedded system

Abstract Failure of the main components of the transmission mechanism of large machinery and equipment usually uses sensors to collect real-time vibration or audio information, followed by data processing on the computer to analyze whether the equipment is operating normally. In this paper, a multi-core embedded fault diagnosis platform based on ARM and DSP is designed. It is small in size. It can independently set the signal types, ranges, and filter parameters of multiple acquisition channels, and synchronously set high-precision acquisition and storage of voltage signals output from various sensors. The DSP adopts the fast Fourier algorithm to analyze the multi-channel sampling data in real time and extracts the fault characteristics through the analysis of envelope demodulation. The operation results show that the system can meet the routine online fault diagnosis of mechanical equipment transmission mechanism, and the fault analysis and diagnosis can reach 95% accuracy within one minute, with high real-time accuracy.

Transfer Learning for Bearing Fault Diagnosis based on Graph Neural Network with Dilated KNN and Adversarial Discriminative Domain Adaptation

Graph neural networks (GNNs) have emerged as a forefront in deep learning, notably influencing research in mechanical fault diagnosis. Transfer learning, particularly through domain adaptation (DA) techniques, has found application in machinery fault diagnosis by training models under one working condition and deploying them under another. While efforts have been made to integrate GNNs with DA techniques to alleviate data distribution discrepancies by investigating the inter-sample relationships, challenges persist: reliance on K-nearest neighbor (KNN) for graph generation emphasizes close relationships, neglecting distant ones; batch processing limits real-time fault diagnosis; and transfer between different-sized bearings is nearly unexplored. To address these limitations, a novel framework for GNN-based domain adaptation in machinery fault diagnosis is proposed. Initially, a convolutional neural network extracts node embeddings from the continuous wavelet transform graph of raw vibration signals. Subsequently, a graph generation layer based on dilated KNN captures both close and distant sample relationships, addressing the long-range dependency issue. Two GNN blocks are then applied for inter-sample relationships investigation and further feature extraction with the outputs directed to a linear classifier during source domain pretraining. Following pretraining, adversarial discriminative domain adaptation is leveraged to mitigate domain distribution discrepancies. Additionally, a novel graph construction method that combines existing training samples with a new single sample is proposed, enabling fault prediction with single instances for real-time online fault diagnosis. Evaluation on datasets with varying working conditions and bearings of different sizes demonstrates the superior performance of our method to other comparison methods.

Bearing fault diagnosis based on online symbolic aggregation approximation and streaming deep discriminant analysis

Rolling bearings are one of the important components of many industrial equipment, and the timely detection of faults in these bearings significantly contributes to ensuring equipment safety. To achieve real-time diagnosis of bearing faults, this paper proposes an online fault diagnosis framework that utilizes online symbolic aggregation approximation (SAX) and streaming deep discriminant analysis. This method uses the moving window approach to segment the vibration data collected online, and then uses SAX for symbolic representation. the obtained icons are input into stacked denoising convolutional autoencoder (AE) for classification. The model consists of several denoising convolutional AEs and a linear discriminant analysis (LDA) module. To accommodate online data changes, while fixing the main structure of the network, the streaming method is used to update the parameters of LDA. Compared with other traditional approaches for diagnosing bearing faults, the proposed method exhibits distinct advantages. Specifically, it utilizes a stable neural network model, updates classifier parameters in real-time, and demonstrates superior computational efficiency. The application results demonstrate the effectiveness of the proposed method.

Application of nonlinear system identification and feature extraction in fault diagnosis of the cracked cantilever beam

The cantilever beam structure is extensively applied in civil, transportation, and mechanical fields. It is generally prone to structural damage like cracks arising from material defects, operation conditions, and environmental factors. Cracks in a cantilever beam generally induce nonlinear behavior, potentially compromising structural durability and even posing threats to production safety. The present study primarily focuses on online and quantitative fault diagnosis of cracks in the cantilever beam. A simulation model is established for the cantilever beam with varying cracks, where responses corresponding to excitation signals are collected. A Nonlinear Autoregressive with Exogenous inputs (NARX) model for the cantilever beam is identified, and Nonlinear Output Frequency Response Functions weighted contribution rate (WNOFRFs) are evaluated from the established NARX model. A WNOFRFs-associated index is extracted to quantify the severity of breathing cracks. Simulation results indicate that this approach can online diagnose and quantify the severity of crack faults to a certain extent. The applicability of this approach is further verified through experiments on a cracked cantilever beam.

Online Fault Diagnosis of Harmonic Drives Using Semisupervised Contrastive Graph Generative Network via Multimodal Data

Harmonic drive is a core component of the industrial robot, its failure will directly affect the robot's performance. Moreover, as the harmonic drive often works with excessive speed and load, it may fail unpredictably. Therefore, online fault diagnosis is quite significant. In this paper, we propose an online intelligent fault diagnosis method for harmonic drives using a Semi-supervised Contrastive Graph Generative Network (SCGGN) via multimodal data. First, multimodal data (including motor current signals and encoder signals) of the harmonic drive are collected online. The Euclidean distance is used to analyze the similarity of the data in the frequency domain. Second, Multiple Graph Convolution Network (MGCN) and Hierarchical Graph Convolution Network (HGCN) are used to obtain complementary fault features from local and global views, respectively. Third, the Contrastive Learning Network is constructed to obtain high-level information through unsupervised learning and perform data clustering to obtain the multi-classification output. Finally, a combination of learnable loss functions is used to optimize the SCGGN. The presented method is tested on an industrial robot. The experimental results show that the method can achieve 86.15% accuracy with 8% of the labeled training data and 79.9% accuracy with only 0.5% of the labeled training data, which is superior to the existing methods.

An online fault detection and diagnosis method of sensors in district heating substations based on long short-term memory network and adaptive threshold selection algorithm

District heating system is an essential energy infrastructure. Due to the harsh working environment, sensors in district heating substations are prone to faults, which will mislead the control strategies of substations. We propose an online fault detection and diagnosis (FDD) method for sensors in substations. The proposed method establishes a Sequence-to-Sequence Prediction Model based on Long Short-Term Memory network with Spatial-Temporal Attention (abbreviated as STALSTM-seq2seq model) for each target sensor. The model can learn normal and evolving data patterns from historical time series of sensors and predict current sensor observation. Then, an adaptive threshold selection algorithm is used to autonomously determine current anomaly threshold based on the prediction error vector of the model. If the prediction error exceeds the anomaly threshold, the sensor malfunctions. Otherwise, the sensor operates normally. Through a comprehensive case study, we demonstrate that the adaptive threshold selection algorithm can fully utilize the forecasting ability of the STALSTM-seq2seq model, and thereby ensure the accuracy of sensor FDD. Overall, the online sensor FDD method achieves an average F1 score of 0.8473. The research shows that the proposed method provides an effective and robust solution to timely and accurately identify sensor faults for district heating substations.

A novel drum-shaped metastructure aided weak signal enhancement method for bearing fault diagnosis

Rolling bearings, extensively utilized in rotating machinery, have critical significance for online fault diagnosis in the domains of industrial equipment maintenance and accident prevention. Presently, fault diagnosis methods heavily rely on identifying the optimal resonance band in the high-frequency range (several kHz) to achieve high signal-to-noise ratio (SNR) fault information. However, these approaches, which necessitate high sampling rate sensing systems and complex algorithm deployments, contradict the practical requirements for cost-effective sensors and edge computing in online diagnosis. To address this contradiction, this paper introduces a novel Drum-shaped Metastructure (DMS) to enhance weak bearing fault signals, thus promoting the detection performance of conventional sensors. The DMS is constructed with a drumhead metastructure consisting of a central block mass attached with four spiral beams, of which its length can be adjusted by a tunable frequency mechanism (TFM) for different frequency responses of interest. The detail of its selective frequency enhancement characteristics is first studied through numerical simulations within a frequency range of 200 Hz to 1000 Hz. Subsequently, the effectiveness of the weak signal enhancement is verified on various bearing test systems, which utilize a prototype DMS fabricated by 3D printing. The results present a significant enhancement in the SNRs of the bearing fault signal, which is achieved by demodulating the full frequency band without the need for complex algorithms. Therefore, the proposed DMS provides a new cost-efficient approach to weak bearing fault diagnosis and online monitoring.

Fault diagnosis of power electronic circuits using optimized BP neural networks

You should leave 8 mm of space above the abstract and 10 mm after the abstract. The heading Abstract should be typed in bold 9-point Arial. The body of the abstract should be typed in normal 9-point Times in a single paragraph, immediately following the heading. The text should be set to 1 line spacing. The abstract should be centred across the page, indented 17 mm from the left and right page margins and justified. It should not normally exceed 200 wordsThree-phase rectifiers have a wide range of applications in industrial production and daily life, and failure to diagnose their faults promptly may affect the reliability of the system operation, resulting in huge safety hazards and economic losses. Therefore, it is of great significance to conduct online fault diagnosis research on the power electronic circuits within three-phase rectifiers. An optimized BP neural network algorithm is proposed for diagnosing open-circuit faults of thyristors in three-phase rectifier circuits. The output voltage waveform characteristics of the circuit, when a fault occurs, are analyzed, and the corresponding output voltage peaks at the same cycle time when different tubes are damaged under the fifth fault type are used as fault feature vectors, and the fault information is input into the BP, optimized BP neural network for training, and the trained neural network is used for fault diagnosis. The fault diagnosis accuracy was obtained by comparing the network output with the desired output. The results of simulation experiments show that the optimized BP neural network can diagnose and analyze the faults of rectifier circuits more efficiently than directly applying the BP network for fault diagnosis.

Online diagnosis of MVB termination fault based on expert system

As a train communication technology, multifunction vehicle bus (MVB) plays a vital role in the transmission of control and monitoring data. However, the termination fault on MVB bus equipment often occurs, which leads to the increase of bit error rate and packet loss rate, and seriously affects the communication quality and stable operation of the train. The traditional off-line diagnosis method has poor timeliness. Therefore, this paper proposes and implements an online diagnosis method of MVB termination fault based on expert system. Firstly, the obtained physical waveform data is preprocessed, and then the waveform characteristics are extracted. Finally, the expert system algorithm is used for diagnosis, so as to quickly and accurately detect the MVB termination fault. The experimental platform was built for online fault diagnosis test. The experimental results show that the diagnosis method has high accuracy and reliability, which is conducive to improving the diagnosis efficiency.

An intelligent online fault diagnosis system for gas turbine sensors based on unsupervised learning method LOF and KELM

The performance of gas turbine inevitably grades slowly in service. In order to obtain high-precision state assessment, an intelligent online real-time sensor fault diagnosis algorithm was proposed in this paper. This method can automatically establish an accurate diagnosis model in a short time, realize the rapid diagnosis of sensor faults, and can effectively solve the problem of operation data imbalance. Firstly, wavelet analysis quickly converts the high-dimensional time-series sensor signal into a low-dimensional feature vector. Then, the unsupervised method LOF was used to screen the abnormal sensor signals. Finally, the KELM is used to achieve fast online identify the fault modes of the sensors. This fault diagnosis system achieves a faster diagnosis speed while ensuring the accuracy. The effectiveness of the method proposed in this paper was verified on the operational data of a gas turbine, the diagnosis time is about 0.233 s, which greatly increases the diagnosis speed compared with other methods, at the same time, the proposed method can guarantee more than 95 % of diagnosis accuracy.

Reconstruction-based stacked sparse auto-encoder for nonlinear industrial process fault diagnosis

The reconstruction-based (RB) approach can effectively suppress the misdiagnosis problem due to the smearing effect in fault isolation. However, the current exploration of the RB approach for large-scale nonlinear systems is still limited. Therefore, this paper proposes a reliable and effective fault diagnosis method based on a reconstruction-based stacked sparse autoencoder (RBSSAE) for high-dimensional industrial systems. In RBSSAE, a reconstruction-based index achieved by the Steffensen iterative method is developed to check whether the given variable(s) are responsible for the faults efficiently. However, the number of possible faulty variable combinations grows exponentially with the system dimension or actual abnormal variables, causing an unbearable computational burden. Hence, the proposed RBSSAE utilizes a sequential floating forward selection approach to rapidly isolate the most decisive variable combination, meeting a requirement of online fault diagnosis. Finally, the effectiveness of the RBSSAE is verified on a numerical example and a real industrial case.

Expert knowledge modelling software design based on Signed Directed Graph with the application for PWR fault diagnosis

Online monitoring and Fault Diagnosis and Detection (FDD) can help the operator to enhance the situation awareness. The Signed Directed Graph (SDG) has the significant advantages that SDG-based FDD not only infers the incipient faults and reveals fault propagation paths but also comprehensively explains causes of failure. In this study, an expert knowledge modelling software is designed and developed based on SDG method. Firstly, the SDG-based process monitoring and FDD algorithms are derived by coupling the threshold method and the quality trend analysis method. Secondly, the expert knowledge modelling software is designed and developed not only considering the convenience of the engineer but also the effectiveness of the SDG-based algorithms. Finally, a Pressurized Water Reactor (PWR) is applied to demonstrate the SDG modelling and verify the SDG models. The case study demonstrates that the developed expert knowledge modelling software can effectively diagnose the incipient faults and infer variable impact as the fault propagation paths