Fault Diagnosis System Research Articles

Fault diagnosis based on incomplete sensor variables with a hierarchical semi-supervised Gaussian mixture classifier

We propose a hierarchical semi-supervised variational semi-Bayesian Gaussian mixture classifier based on the partially incomplete and unlabeled samples for the fault diagnosis of mechanical and electrical systems. These systems are typically complex in structure and are monitored by multiple sensors simultaneously. Some sensor variables in the collected data may be incomplete due to sensor malfunctions or transmission errors. Additionally, labeling the data can be a time-consuming and labor-intensive task, resulting in many unlabeled samples. To address these challenges, the missing sensor variables and the unavailable labels are treated as hidden values and handled within the framework of the Expectation Maximization algorithm. We employ a semi-Bayesian technique with variational inference to estimate the model parameters. Specifically, we introduce prior distribution to the mean and covariance matrix to address the possible singularity of the empirical covariance matrix. The weighting coefficients are left without putting prior distribution so that their values can decay to zero, effectively removing redundant components of the mixture model. The factorized distribution is utilized to approximate the posterior distribution of the model parameters, as well as the missing labels and other latent variables. Numerical and real case studies are carried out to verify the effectiveness of the proposed technique.

An adaptive imbalance robust graph embedding broad learning system fault diagnosis for imbalanced batch processes data

Modern batch processes develop toward higher levels, and devices usually work at normal conditions with generally very few faults. Therefore, fewer fault data are collected than normal data, which makes the normal and fault modes imbalanced, and the fault diagnosis model is incapable focusing on the minority of fault samples as much as on most normal samples, consequently leading to insufficient generalization ability for the model. For that, we propose an adaptive imbalance-robust graph embedding broad learning system (AI-RGEBLS) in this paper. Firstly, it achieves adaptive correction of unbalanced samples by niche technique and synthetic minority over-sampling technique (SMOTE) with improved Mahalanobis distance. Then, broad learning system (BLS) is regularized by graph embedding, and further introduced the L2,1 norm constraint, which makes it possible to enhance the robustness of the model while considering the local manifold information of the data in the feature extraction process. Finally, the incremental learning approach is applied to the model to avoid the disastrous forgetting problem caused by training the whole model from zero. The effectiveness of the proposed method is verified by penicillin fermentation process and semiconductor etching process. It can effectively improve the speed of model training and provide better fault diagnosis for imbalanced batch processes compared with the existing methods.

EMA fault diagnosis method based on multi-information fusion of GRU and improved attention mechanism

Aiming at the problems of time series feature loss and fault information loss in traditional electromechanical servo system (EMA) fault diagnosis methods based on machine learning and deep learning, a multi-information fusion EMA fault diagnosis method based on gated recurrent unit (GRU) and improved attention mechanism is proposed. Firstly, the collected different sensor signals are divided into different channels, and the time series features of each channel signal are extracted by GRU. Then, the self-attention mechanism is introduced to further distinguish the important relationship between different time points of the signal. The multi-channel attention mechanism is further introduced to adaptively fuse the features of different channels, and finally the fault diagnosis is realized through the classifier. The experimental results based on the test bench data set show that: compared with the single sensor model, the diagnosis rate is improved 10%; compared with the model without the introduction of the attention mechanism, the diagnosis rate is improved 5.2%; compared with the classic machine learning, deep learning and the improved algorithm based on deep learning in the past two years, the diagnosis rate of the diagnosis model designed in this paper is 98.5%above, and the diagnosis effect is the best.

Derivative dynamic time warping algorithm with introduced correction for varying load fault diagnosis of nuclear power system steam turbine units

The Derivative Dynamic Time Warping (DDTW) algorithm is improved to address the multi-parameters time series classification problem faced by nuclear power system steam turbine units varying load fault diagnosis. Firstly, the entire load changing process is treated as a single sample rather than multiple time-step-samples. This ensures the complete information on the load changing processes, while avoiding interference from normal data fluctuations during faults. Secondly, Time Series Position Coefficient and Time Series Length Coefficient are proposed to correct the DDTW algorithm from two perspectives: the sequences lengths and the data positions in the sequences. This solves the singularities and timeline scaling problems, thereby preventing interference introduced by data sequences' lengths differences and ''similar data appearing at different times'' problem. The nuclear power system steam turbine unit simulation model was built to obtain load changing processes data under normal and faults statuses. In the varying load fault diagnosis test based on these data, the improved DDTW algorithm achieved an accuracy of 1.38%–12.06% higher than other methods, reaching 87.50%. Finally, The Deep Convolutional Generative Adversarial Networks (DCGAN) model was used to generate data to supplement the limited samples of complete load changing processes, and the accuracy of the novel method increased to 95.51% with the increase of data used to support the comparison.

Fault isolation considering real mechanical coupling effects with application to liquid oxygen/kerosene staged combustion rocket engines

Liquid rocket engines are crucial for space exploration, necessitating reliable fault diagnosis systems. This paper focuses on fault isolation in liquid oxygen (LOX)/kerosene staged combustion rocket engines. Addressing deficiencies in current methods, we first propose a theorem to evaluate the adaptability between the analytical model and fault factors based on available sensors. Through the analysis of fault mechanisms, this paper defines fault modes using an innovative multi-factor coupling approach based on engine operational principles. Moreover, a fault isolation method with two steps is established, incorporating the optimal selection of sensors to accommodate robust requirements. This method features a clear process, low computational complexity, and comprehensive fault coverage, and it can also be applied to systems with similar complex operational mechanisms.

Advancing automobile dry clutch fault diagnosis through innovative imaging techniques and Vision transformer integration

The study investigates the significance of clutch condition monitoring in automotive transmissions to preempt mechanical failures, enhance efficiency, and mitigate risks to human safety and maintenance costs. It explores the integration of Vision Transformer (ViT) with imaging techniques, such as scalograms, spectrograms, polar plots, radar plots, and Hilbert-Huang transforms, to diagnose faults in dry friction clutches. By transforming vibration signals into image representations and utilizing ViT for fault classification, the study aims to identify the most effective imaging technique and optimal hyperparameters for accurate fault diagnosis. Experimental studies on a test rig with varying fault conditions demonstrate the effectiveness of ViT in diagnosing clutch faults when coupled with different image conversion techniques. The results highlight the potential of integrating spectrogram image processing with ViT, achieving a 100% accuracy in fault diagnosis for clutch systems, thus advancing the analysis of faults in clutch systems.

A multi-rate sensor fusion and multi-task learning network for concurrent fault diagnosis of hydraulic systems

Hydraulic systems are widely used in key modern industrial fields such as mechanical manufacturing, aerospace, and heavy machinery, and their efficient and reliable operation is crucial to ensuring production safety and efficiency. However, hydraulic systems often experience concurrent faults, such as pump failures, valve blockages, pipeline leaks, and fluid contamination, which pose significant challenges to the fault diagnosis in hydraulic systems. This paper introduces a multi-task learning network that deconstructs the challenge of concurrent fault diagnosis into specific sub-tasks, enabling the simultaneous identification and classification of multiple hydraulic components' faults. Automatic channel filtering is designed to screen out sensitive channels of each component from multi-rate sensors. A dual-flow model is used to feature extraction, which can simultaneously extract the local spatial features and global semantic information. Then, four classification models are designed to identify the extracted shared features. An uncertainty weight loss is also proposed to balance the loss of different tasks. The experimental results show that our model significantly outperforms traditional methods and other popular multi-output methods in diagnosing concurrent faults.

Bayesian optimized deep Q-network for diagnosing mine ventilation systems windage alteration fault targeting imbalanced data

Fault diagnosis of mine ventilation system is of great significance for mine safety production. Traditional machine learning algorithms have been widely applied in the field of mine ventilation systems windage alteration faults (WAFs) diagnosis, but these algorithms have poor intelligence and weak generalization ability. Therefore, this research constructs a WAFs diagnosis model (WAFs-BODQN) based on Bayesian optimized (BO) deep Q-network (DQN). Meanwhile, in order to solve the problem of poor performance of intelligent models when the dataset is imbalanced, a reinforcement learning reward function (KMeans-SDF) was designed, which combines the KMeans++ algorithm and spatial distance function (SDF). The WAFs-BODQN model with KMeans-SDF reward function has fault location diagnosis accuracies of 98.75 %, 97.50 %, and 95.00 % for mild, moderate, and extreme imbalanced datasets UB1, UB2, and UB3, respectively. This effectively avoids the "eccentricity" phenomenon of the intelligent model on majority class and achieves accurate prediction of fault locations. The accuracy of fault location diagnosis using the WAFs-BODQN model in simulation experiments and on-site empirical applications is higher than 95 %, demonstrating the intelligence, generalization, and feasibility of WAFs-BODQN model in the field of WAFs diagnosis in mine ventilation systems. By comparing the results with traditional machine learning and deep learning fault diagnosis models, it has been proven that the WAFs-BODQN model has advantages in intelligence and generalization ability. It is expected to establish a universal architecture for diagnosing WAFs, providing theoretical guidance and technical support for achieving intelligent mine ventilation.

Acoustic leak localization for water distribution network through time-delay-based deep learning approach

Water leakage within water distribution networks (WDNs) presents significant challenges, encompassing infrastructure damage, economic losses, and public health risks. Traditional methods for leak localization based on acoustic signals encounter inherent limitations due to environmental noise and signal distortions. In response to this crucial issue, this study introduces an innovative approach that utilizes deep learning-based techniques to estimate time delay for leak localization. The research findings reveal that while the Res1D-CNN model demonstrates inferior performance compared to the GCC-SCOT and BCC under high signal-to-noise ratio (SNR) conditions, it exhibits robust capabilities and higher accuracy in low SNR scenarios. The proposed method's efficacy was empirically validated through field measurements. This advancement in acoustic leak localization holds the potential to significantly improve fault diagnosis and maintenance systems, thereby enabling efficient management of WDNs.

Smart Grids based on Quantum Computing in the present-day Energy Systems for Fault Diagnosis

This paper's goal is to investigate how quantum computing might be used to optimize power systems and to address some of the difficulties that quantum computers may encounter, along with solutions. The fundamental ideas of quantum computing are also covered, along with how they differ from classical computations. Within the framework of smart grids, quantum computing (QC) presents itself as a next-generation alternative approach to address impending computational difficulties. QC is a relatively new but exciting technology that uses the special properties of quantum mechanics to analyze data and perform calculations. This new paradigm can solve optimization, simulation, and machine learning issues more effectively and quickly than ever before by overcoming the obstacle of computational constraints. Recent significant advancements in the development of sophisticated quantum hardware and software techniques have increased the viability of applying QC in a variety of research fields, including smart grids. It is clear that a great deal of research has already been done, and that research is remarkably ongoing. As a result, this article further defines the prospective smart grid applications and presents the research findings of the most current articles, emphasizing their recommendations for applying QC approaches for diverse smart grid applications. It states their plans, methods, and outcomes. The limits of the most recent quantum computers are also discussed in this research, along with how they might significantly affect the optimization of energy systems.

Framework for Data‒Driven Fault Diagnosis of Numerical Spacecraft Propulsion Systems

The increasing complexity of deep space missions introduces significant challenges in maintaining spacecraft health, particularly in the propulsion systems, due to the inherent communication delays with Earth. This research proposes a novel framework for the autonomous, data-driven fault diagnosis of spacecraft propulsion systems. Leveraging data generated from a spacecraft propulsion system simulation model. The study addresses the limitations imposed by computational resources and sensor installation constraints through Sequential Forward Selection (SFS) for optimized sensor placement and feature selection. The framework's effectiveness is demonstrated through implementation on a microcomputer, showing promising results in terms of diagnostic accuracy and processing speed, thus highlighting its potential for onboard spacecraft application. This study not only advances the autonomous capabilities of spacecraft in deep space but also contributes to the broader field of Prognostics and Health Management (PHM) by providing a scalable, efficient approach to fault diagnosis in critical spacecraft systems. The proposed framework demonstrates a promising approach to optimizing diagnostic tasks for spacecraft systems. However, the trade-offs observed necessitate a careful consideration of task-specific requirements and the potential need for adjustments to maintain a high level of accuracy alongside computational efficiency.

Cross-domain transfer fault diagnosis by class-imbalanced deep subdomain adaptive network

In variable working conditions and class imbalances, fault diagnosis in gearbox systems becomes difficult due to the difficulty in learning the classification boundaries of the minority class. Moreover, the diversity in fault mode distributions under various working conditions poses significant challenges for domain adaptation. To this end, this work proposed a Class-imbalanced Deep Subdomain Adaptive Network (CIDSAN) for complex cross-domain fault diagnosis scenarios involving feature and label shifts. We design a convolutional neural network embedded with SimAM (SimAM-CNN) to extract global feature information from time-domain vibration signals. Subsequently, a novel cost-sensitive classifier is presented for class re-weighting, along with a Joint Supervised Classifier that refines intra-class and inter-class distances by re-weighting class weights in the latent space using cost-sensitive classifiers while leveraging LDAM regularization to adjust classification boundaries. Diagnostic case studies on two datasets under various working conditions validate the effectiveness and superiority of the proposed approach. The code https://github.com/Zjy0121/CIDSAN.

Research on fault diagnosis for three-phase rectifier device in direct current transmission system based on continuous wavelet transform and vision transformer

Abstract As a core component of photovoltaic power generation systems, the three-phase rectifier device plays a crucial role, and its failure can potentially reduce energy conversion efficiency and output quality. Presently, the performance of fault time-domain signal diagnosis methods based on three-phase rectifier circuits is challenging to enhance. This paper proposes a novel fault detection method for three-phase rectifier devices based on Vision Transformer, referred to as CWT-ViT, to address this issue. This method transforms time-domain fault signals into images through Continuous Wavelet Transform (CWT), subsequently inputting these images into a Vision Transformer model. Relying on its powerful self-attention mechanism and fully connected layers, it realizes the extraction and learning of rectifier device image features. A Simulink simulation model of a three-phase bridge controllable rectifier circuit is established for fault injection to collect fault signals. Fault diagnosis experiments demonstrate that the proposed diagnostic method achieves a prediction accuracy of 98.6%, maintaining a relatively high precision level. In comparison to four excellent classification models currently available: AlexNet, RepVGG, ResNet, and GoogLeNet, the proposed method demonstrates superior diagnostic performance. Additionally, this paper conducts ablation experiments to meticulously analyze the impact of each module in the fault diagnosis process. This research achieves more precise and efficient fault diagnosis in photovoltaic power generation systems, thereby reducing downtime and maintenance costs for actual equipment and enhancing the stability of photovoltaic power generation systems. This research provides an innovative, intelligent solution for the intelligent operations and maintenance of photovoltaic power generation.

Multiscale Spatial–Temporal Bayesian Graph Conv-Transformer-Based Distributed Fault Diagnosis for UAVs Swarm System

Multiscale Spatial–Temporal Bayesian Graph Conv-Transformer-Based Distributed Fault Diagnosis for UAVs Swarm System

Active Fault Diagnosis for Uncertain LPV Systems: A Zonotopic Set-Membership Approach

Active Fault Diagnosis for Uncertain LPV Systems: A Zonotopic Set-Membership Approach

Study on Fault Prediction Method of Hydropower Equipment Based on Equipment Health State Detection

Abstract With the continuous development of China’s electric power industry, the installed capacity of power plants is increasing, and hydropower units are developing in the direction of large capacity and high efficiency. As the complexity of the equipment is getting higher and higher, different equipment are more and more different, showing different characteristics when faults occur, which puts forward higher requirements for the safe and stable operation of hydropower units. In response to the above problems, this paper develops a set of human-machine closed-loop remote fault diagnosis reasoning machine system with self-learning function and a set of equipment health state monitoring and prediction system. This remote fault diagnosis system reasoning machine system can reduce the cost of unit equipment fault diagnosis, improve the diagnosis efficiency, and ensure the safe, stable and economic operation of the unit.

Bearing RUL prediction and fault diagnosis system based on parallel multi-scale MIMT lightweight model

Abstract In actual industrial production, the importance of safety production is increasingly prominent, and the degradation and failure of machinery and equipment are potential sources of safety hazards. Therefore, there is a growing trend towards real-time monitoring, prediction, and diagnosis of industrial equipment to prevent unpredictable impacts on life and property safety caused by sudden failures. To address this issue, this paper proposes a real-time degradation anomaly detection based on parallel multiscale autoencoders and a lightweight model of parallel multiscale multi-input multi-task for bearing Remaining Useful Life (RUL) prediction and fault diagnosis systems. Firstly, the multiscale autoencoder method is used to simulate actual working conditions and reconstruct the original vibration signals to build abnormal degradation detection intervals. The [0, $\mu$ +3$\sigma$] interval is utilized to judge abnormal degradation based on reconstruction errors, and the First Predict Timepoint (FPT) is determined adaptively. Secondly, a method for constructing dimensionless auxiliary datasets is proposed, which adopts a multi-input form based on deep separable convolution for feature extraction of original vibration signals, kurtosis, and peak values to improve the prediction and diagnosis performance of the lightweight model. Finally, a multi-task output method combining clustering and regression is employed to achieve RUL prediction and fault diagnosis of bearings. The proposed method overcomes the problems existing in traditional bearing RUL prediction and diagnosis methods and possesses theoretical innovation and engineering practicality. Validation on two bearing datasets confirms the effectiveness of the proposed method.

Current Status of Research on Fault Diagnosis Using Machine Learning for Gear Transmission Systems

Gear transmission system fault diagnosis is crucial for the reliability and safety of industrial machinery. The combination of mathematical signal processing methods with deep learning technology has become a research hotspot in fault diagnosis. Firstly, the development and status of gear transmission system fault diagnosis are outlined in detail. Secondly, the relevant research results on gear transmission system fault diagnosis are summarized from the perspectives of time-domain, frequency domain, and time-frequency-domain analysis. Thirdly, the relevant research progress in shallow learning and deep learning in the field of fault diagnosis is explained. Finally, future research directions for gear transmission system fault diagnosis are summarized and anticipated in terms of the sparsity of signal analysis results, separation of adjacent feature components, extraction of weak signals, identification of composite faults, multi-factor combinations in fault diagnosis, and multi-source data fusion technology.

Fault Diagnosis of an Excitation System Using a Fuzzy Neural Network Optimized by a Novel Adaptive Grey Wolf Optimizer

As the excitation system is the core control component of a synchronous condenser system, its fault diagnosis is crucial for maximizing the reactive power compensation capability of the synchronous condenser. To achieve accurate diagnosis of excitation system faults, this paper proposes a novel adaptive grey wolf optimizer (AGWO) to optimize the initial weights and biases of the fuzzy neural network (FNN), thereby enhancing the diagnostic performance of the FNN model. Firstly, an improved nonlinear convergence factor is introduced to balance the algorithm’s global exploration and local exploitation capabilities. Secondly, a new adaptive position update strategy that enhances the interaction capability of the position information is proposed to improve the algorithm’s ability to jump out of the local optimum and accelerate the convergence speed. In addition, it is demonstrated that the proposed AGWO algorithm has global convergence. By selecting real fault waveforms of the excitation system for case validation, the results show that the proposed AGWO has a better convergence performance compared to the grey wolf optimizer (GWO), particle swarm optimization (PSO), whale optimization algorithm (WOA), and marine predator algorithm (MPA). Specifically, compared to the FNN and GWO-FNN models, the AGWO-FNN model improves average diagnostic accuracy on the test set by 4.2% and 2.5%, respectively. Therefore, the proposed AGWO-FNN effectively enhances the accuracy of fault diagnosis in the excitation system and exhibits stronger diagnostic capability.

Integrated testability modeling method of complex systems for fault feature selection and diagnosis strategy optimization

The efficacy of system fault diagnosis is intricately linked with the testability design of the system, particularly in intricate systems encompassing numerous components exhibiting diverse failure modes. However, current research shows a dearth of effective testability models and algorithms for generating diagnostic strategies in multi-valued attribute systems (MVAS) with uncertainty. To address this, the present paper introduces a novel method for testability modeling for complex systems. This approach incorporates signal features in lieu of raw sensor signals within the testability modeling process and accounts for the uncertainties surrounding test outcomes. The context of complex MVAS, characterized by inherent uncertainty, the paper proposes a novel method for constructing a four-value dependency matrix (D-matrix). Furthermore, the paper presents a novel sequential diagnosis strategy optimization approach based on a heuristic evaluation function for the multivalued D-matrix with uncertainty. The proposed methodology has been rigorously validated using a real-world case study involving an aero-engine fuel metering device system. Comparative experiments show that the proposed method can achieve better diagnostic performance in the shortest time.