Fault Diagnosis System Research Articles

Research on Fault Diagnosis of Thermal Power System of Thermal Power Unit under Different Loads

Aiming at the problem of difficulty in acquiring fault data under different loads, a domain-adversarial-based fault diagnosis method for convolutional neural networks is established by combining convolutional neural networks with adversarial domain adaptive methods. The method uses convolutional neural networks to extract the fault features of the source and target domain samples, and simultaneously implements joint adjustment of the distributions of the domain-level and class-level features, aligns the feature distributions of the source and target domains by minimizing the difference in the distributions of the two domains through adversarial learning of the domains and aligns the feature distributions of each class of faults by making the distributions of the same kind of features closer through the loss of the central discriminant. With the help of a 600MW supercritical thermal power unit full-working condition simulation system, experiments are carried out under 100%, 90%, 80% and 70% of the rated load, and the results show that the method has effectiveness and superiority in the fault diagnosis of thermal system under different loads.

Design of Fault Diagnosis Method Based on Circuit Frequency Domain Characteristics Fault Dictionary Method

In recent years, with the rapid development of integrated circuits and the widespread application of smart chips, the internal structure of complex electronic systems have become increasingly complex, which greatly increases the difficulty of board-level circuit testing and fault diagnosis of complex electronic systems. At present, traditional manual testing methods are no longer able to meet the maintenance requirements of modern complex electronic system board-level circuit. Therefore, the development of intelligent and universal automatic testing systems has become an important issue in fault detection of complex electronic systems. Based on the testing requirements of the automatic test system(ATS), this paper designs the corresponding overall scheme, software scheme and hardware circuit, and studies the fault diagnosis and test of board-level circuit of complex electronic system, and proposes to apply the traditional Bode diagram to the engineering practice of ATS. Fault detection is realized by utilizing the frequency domain characteristics of the object under test.

The Application and Research of Particle Swarm Optimization Radial Basis Function Network Driven by Edge Computing in Bogie Fault Diagnosis

The bogie system is a key system that affects the safety and quality of high-speed train operation. Constructing a fault diagnosis model for the bogie system can effectively improve the safety and comfort of high-speed train operation. The traditional modeling method uses the BP neural network to fit the temperature and other parameters of the bogie system. However, because the BP neural network is prone to fall into local minima, slow convergence and poor diagnostic accuracy, this paper proposes a radial basis function network bogie fault diagnosis model based on particle swarm optimization based on edge computing. The model combines edge computing and particle swarm optimization algorithm to improve the accuracy and real-time of bogie fault diagnosis. By implementing model training and inference on edge devices, data transmission latency has been reduced and diagnostic efficiency has been improved. A particle radial basis neural network (PSRB) was designed using a highly convergent particle swarm optimization algorithm, and the parameters of the radial basis neural network (RBF Neural Networks) were optimized using the Particle Swarm Optimization algorithm. Based on the complexity of the input parameters of the bogie system, determine the input and output parameters of the model, and use particle swarm optimization algorithm to search for the optimal values of the basis function center, width, and output layer weight threshold of the RBF neural network. The composite algorithm was applied to the fault diagnosis of the bogie system, and a particle radial basis neural network bogie fault diagnosis model was designed. The simulation and experimental results of the model show that the diagnostic model can effectively improve the identification accuracy of fault diagnosis, with a minimum error accuracy of 0.0255, saving computation time. The computation time is reduced to 2.9 seconds, eliminating the influence of non target parameters on the inversion results. This model can also be used in other systems of high-speed trains and has practical application value.

Fault Diagnosis of Fuel Cells by a Hybrid Deep Learning Network Fusing Characteristic Impedance

Establishing an accurate fault diagnosis system for the proton exchange membrane (PEM) fuel cell is essential for ensuring stable performance and delaying degradation. In this paper, a novel fault diagnosis method fusing characteristic impedance for the PEM fuel cell based on a hybrid deep learning network by combing the residual network (ResNet) and long short-term memory (LSTM) is proposed. Specifically, the characteristic impedance that can reflect internal dynamics loss is fused as the feature input alongside other commonly vehicular measurement signals and decoded to form a feature matrix. The feature matrix is then transferred to realize 25 categories of fault detection, including different degrees of membrane drying, flooding, and air starvation. The results showed the accuracy of ResNet-LSTM can reach 99.632% with a good balance of computational burden and is higher than that of a single LSTM, ResNet, and convolutional neural network, as well as traditional machine learning methods because such the hybrid structure can make full use of feature learning capability of ResNet and the time series analysis capability of LSTM. Moreover, the proposed hybrid framework is further validated and compared under different proportions of training samples, noise levels, and input signals.

Operation Fault Detection of Electrical and Mechanical Equipment Based on Kalman Filter

In order to realize the remote collaborative diagnosis of electromechanical equipment fault in integrated energy system, a design method of remote collaborative diagnosis system of electromechanical equipment fault in integrated energy system based on Kalman filter is proposed. The hardware of the diagnosis system designed in this paper includes main control module, fault information parameter identification module, dynamic sensor acquisition module, fault signal filtering and detection module, adaptive control module, output interface module and so on. The collected fault information of electromechanical equipment in the system is fused by multi-source sensors, the fault information feature extraction is completed by Kalman filter, the information clustering processing is completed, and the algorithm design of electromechanical equipment operation fault diagnosis is realized. The fault diagnosis algorithm based on Kalman filter is loaded into the main control module of the motor equipment fault diagnosis system to realize the identification of motor equipment fault parameters and complete the development and design of the diagnosis system. The test results show that the text diagnosis system has good adaptability, high reliability and accuracy.

Research on bearing fault diagnosis technology based on machine learning

As industrial equipment complexity continues to rise, the importance of bearings within these systems has become more critical, given their pivotal role in equipment functionality. Bearing faults can result in severe production accidents and safety issues. Hence, there is an urgent need for advanced bearing fault diagnosis technology. This study concentrates on rolling bearings, analyzing their structural characteristics and key parameters to classify fault types—inner race faults, rolling element faults, and outer race faults. Utilizing a dataset of 80 sets of bearing factory data, time and frequency domain analyses are conducted, establishing seven feature parameters (five in the time domain and two in the frequency domain). This data is organized into a 7-dimensional matrix for subsequent analysis and model development. The K-Means algorithm is chosen for its effectiveness in automatically recognizing fault patterns in rolling bearings. Training on the 7-dimensional matrix identifies four clustering centers corresponding to normal conditions, inner race faults, rolling element faults, and outer race faults. The fault diagnosis system is implemented using Python, and algorithm optimization improves efficiency. The study concludes with insights drawn from the analysis and proposes optimization methods, which contributing to advancing bearing fault diagnosis technology, particularly addressing industrial equipment reliability and safety concerns.

A Distributed System Fault Diagnosis System based on Machine Learning

Now days distributed system becomes the mainstream system of information storage and processing. Compared with traditional systems, distributed systems are larger and more complex.However, the average probability of failure is higher and the difficulty, complexity of operation and maintenance are greatly increased. Therefore, it is necessary to use efficient methods to diagnose the system. Our aim is to use the trained model to diagnose the fault data of the distributed system, so we can obtain as high diagnostic accuracy as possible, and create a web side for users to use. The technique we proposed uses the integrated learning approach of Stacking to model the superposition of the raw data. To realize this, we trained with a dataset of 10,000 pieces of data and assessed accuracy every once in a while. Our best training results are about 80.69% accurate and can be used on the web side. By training data sets and analyzing distributed system faults with Stacking technology, a model with a test accuracy of 80.69% was obtained. Through this model and the web platform we built, the fault of distributed system can be diagnosed, and the diagnosis results are better than other models.

A modified transformer and adapter-based transfer learning for fault detection and diagnosis in HVAC systems

Fault detection and diagnosis (FDD) of heating, ventilation, and air conditioning (HVAC) systems can help to improve the energy saving in building energy systems. However, most data-driven trained FDD models have limited generalizability and can only be applied to specific systems. The diversity of HVAC systems and the high cost of data acquisition present challenges for the practical application of FDD. Transfer learning technology can be employed to mitigate this problem by training a model on systems with sufficient data and then transfer it to other systems with limited data. In this study, a novel transfer learning approach for HVAC FDD is proposed. First, the transformer model is modified to incorporate one encoder and two decoders connected, enabling two outputs. This modified transformer model accommodates absent features in the target domain and serves as a robust foundation for transfer learning. It has effective performance in complex systems and achieves an accuracy of 91.38% for a system with 16 faults and multiple fault severity levels. Second, the adapter-based parameter-efficient transfer learning method, facilitating the transfer of trained models simply by inserting small adapter modules, is investigated as the transfer learning strategy. Results demonstrate that this adapter-based transfer learning approach achieves satisfactory performance similar to full fine-tuning with fewer trainable parameters. It works well with limited data amount in target domain. Furthermore, the findings highlight the significance of adapters positioned near the bottom and top layers, emphasizing their critical role in facilitating successful transfer learning.

Fault Diagnosis Model of New Energy Vehicle Charging Equipment Based on Fuzzy Fault Tree Theory

This paper probes into the new energy automobile charging equipment fault diagnosis problem. Fuzzy fault tree theory is introduced as a solution, fuzzy mathematics is used to improve the accuracy of fault diagnosis, the fault diagnosis model of charging equipment is established, and the fault diagnosis system based on Internet of things technology is designed, including equipment layer, communication layer, server and client. The system aims to realize the real-time monitoring and processing of the fault of new energy vehicle charging facilities, improve the user experience and improve the safety index.

Observer‐based fault diagnosis for autonomous systems

AbstractWith the advancement in sensor and communication technology, autonomous systems have been incrementally reshaping the execution of tasks in commercial and military sectors. Since the systems are designed to complete tasks without or with minimal human intervention, fault diagnosis based on sensor data has been crucial to preventing accidents and fatalities. In this paper, fault diagnosis for autonomous systems is designed based on nonlinear adaptive observers, tested in numerical simulations, and implemented in a robotic platform. To this end, we utilize the persistence of excitation conditions on the parametric model of the faults. We derive sufficient conditions for the nonlinear adaptive observer in terms of linear matrix inequality to ensure the convergence of the estimates. Furthermore, we consider one‐sided Lipschitz conditions to obtain less conservative results. The main advantage of using the nonlinear adaptive observer is that the method converges quickly to the actual fault and requires minimum computational effort. However, solving the linear matrix inequality might not be trivial. Numerical simulations based on a single‐link flexible joint robot model and experimental tests in an autonomous quadcopter are performed to validate the effectiveness of the proposed method.

Improved Temporal Fuzzy Reasoning Spiking Neural P Systems for Power System Fault Diagnosis

Fuzzy and temporal reasoning can effectively improve the accuracy of fault diagnosis methods. However, there are challenges in practical applications, such as missing alarm messages, temporal reasoning with complex calculations, and complex modeling processes. Therefore, this study proposes an improved temporal fuzzy reasoning spiking neural P (ITFRSNP) system for power system fault diagnosis. First, the ITFRSNP system and its reasoning method are proposed to perform association reasoning between confidence degrees and temporal constraints. Second, a general fault diagnosis model and process are developed based on the ITFRSNP system to diagnose various faulty components and simplify the modeling process. In addition, a search method is provided for identifying suspected faulty components, considering the missing alarm message of the circuit breaker. Simulation results of fault cases demonstrate that the proposed method exhibits high accuracy and fault tolerance. It can precisely identify faulty components despite incorrect operations or inaccurate alarm messages of protective relays and circuit breakers. Moreover, the search method effectively narrows down the diagnostic scope without missing suspected faulty components in scenarios where alarms from boundary circuit breakers are missing, thereby enhancing the fault diagnosis efficiency. The fault diagnosis model features a straightforward structure and reasoning process with minimal computational complexity, making it suitable for real-time diagnosis of complex faults within power systems.

Process monitoring and fault diagnosis method combining bagging DPCA‐ICA with moving window Kolmogorov–Smirnov test

AbstractProcess monitoring and fault diagnosis systems are essential for ensuring the quality and safety of industrial processes. Real industrial process data always has dynamic features, autocorrelation features, and non‐Gaussian distributions, making many multivariate statistical process monitoring (MSPM) methods, such as principal component analysis (PCA), not work well. To solve the problem, a new process monitoring method (named DPCA‐ICA‐moving window Kolmogorov–Smirnov test [MWKS]) is proposed in the paper, which is a combination of DPCA and independent component analysis (ICA) with adaptive weight parameters and introduces a moving window technique and two‐sample K‐S test to obtain the K‐S distance of the commonly used Hotelling's and square predictive error as new monitoring parameters. Experimental results on the Tennessee Eastman chemical process demonstrate the effectiveness and accuracy of the proposed method, which shows it has better performance than existing commonly used methods. The average fault detection rate (FDR) is 93.131%, while the average false alarm rate (FAR) of the proposed method is only 0.1%.

Data and knowledge fusion-driven Bayesian networks for interpretable fault diagnosis of HVAC systems

Timely diagnosis and maintenance of faults in chiller units is beneficial for reducing energy consumption in buildings. In order to improve the diagnostic accuracy and provide reasonable explanations for fault occurrences of the heating, ventilation, and air conditioning (HVAC) systems in buildings, a fusion-driven Bayesian network fault diagnosis method is proposed based on broad learning system and fuzzy expert knowledge. Firstly, from the data-driven perspective, an efficient broad learning system is developed as benchmarking model to build the prior Bayesian network structure. Secondly, from the knowledge-driven perspective, the fuzzy logic system is used to fuzz expert knowledge, which then used to optimize the Bayesian network. Finally, the information obtained by experts on-site is incorporated into the optimized Bayesian network as new evidence nodes to determine the Bayesian network. The parameters of the Bayesian network are learned through Noisy-MAX processing of the conditional probability table. The performance of the proposed method is evaluated based on the fault diagnosis of a chiller system. The results demonstrate that the Bayesian network established through this method combines the advantages of data-driven and knowledge-driven approaches. It not only performs well in terms of diagnostic accuracy, with accuracy rates above 98 % for six typical faults, but also provides effective explanations for the underlying mechanisms of the faults through causal relationship diagrams.

Refrigerant leak detection in industrial vapor compression refrigeration systems using machine learning

Efficient detection of refrigerant leakage is of utmost importance for industrial refrigeration systems due to its potential to cause substantial impacts on system performance and the environment. Existing research on fault detection and diagnosis in refrigeration systems primarily revolves around solutions based on experimental or laboratory data. However, in the industrial use case, achieving accurate and early detection poses significant challenges. This paper reports on the development of a novel refrigerant leak detection method for industrial vapor compression refrigeration systems. Our method leverages real-world data obtained from operational installations, enabling us to assess its reliability and applicability. The proposed data-driven approach involves predicting the fault-free liquid level in the installation receiver and comparing the actual and predicted levels. In this work, we place emphasis on features and model selection. Dedicated metrics combined with a model comparison method are proposed to evaluate and compare the performance of commonly used regression models with two sets of features to determine the most effective one. Furthermore, we provide insights into the results obtained from the deployment of the proposed method in real-world industrial installations.

An Open-Circuit Fault Diagnosis System Based on Neural Networks in the Inverter of Three-Phase Permanent Magnet Synchronous Motor (PMSM)

Three-phase motors find extensive applications in various industries. Open-circuit faults are a common occurrence in inverters, and the open-circuit fault diagnosis system plays a crucial role in identifying and addressing these faults to enhance the safety of motor operations. Nevertheless, the current open-circuit fault diagnosis system faces challenges in precisely detecting specific faulty switches. The proposed work presents a neural network-based open-circuit fault diagnosis system for identifying faulty power switches in inverter-driven motor systems. The system leverages trained phase-to-phase voltage data from the motor to recognize the type and location of faults in each phase with high accuracy. Employing separate neural networks for each of the three phases in a three-phase permanent magnet synchronous motor, the system achieves an outstanding overall fault detection accuracy of approximately 99.8%, with CNN and CNN-LSTM architectures demonstrating superior performance. This work makes two key contributions: (1) implementing neural networks to significantly improve the accuracy of locating faulty switches in open-circuit fault scenarios, and (2) identifying the optimal neural network architecture for effective fault diagnosis within the proposed system.

Asynchronous Fault Diagnosis of Stochastic Discrete-Event Systems in Industrial Applications

Asynchronous Fault Diagnosis of Stochastic Discrete-Event Systems in Industrial Applications

A zero-shot fault attribute transfer learning method for compound fault diagnosis of power circuit breakers

Diagnosis of compound mechanical faults for power circuit breakers (CBs) is a challenging task. In traditional fault diagnosis methods, however, all fault types need to be collected in advance for the training of diagnosis model. Such processes have poor generalization capabilities for industrial scenarios with no or few data when faced with new faults. In this study, we propose a novel zero-shot learning method named DSR-AL to address this problem. An unsupervised neural network, namely, depthwise separable residual convolutional neural network, is designed to directly learn features from 3D time-frequency images of CB vibration signals. Then we build fault attribute learners (ALs), for transferring fault knowledge to the target faults. Finally, the ALs are used to predict the attribute vector of the target faults, thus realizing the recognition of previously unseen faults. The orthogonal experiments are designed and conducted on real industrial switchgear to validate the effectiveness of the proposed diagnosis framework. Results show that it is feasible to diagnose target faults without using their samples for training, which greatly saves the costs of collecting fault samples. This will help to accurately identify the various faults that may occur during CB’s life cycle, and facilitate the application of intelligent fault diagnosis system.

Fuzzy RBF neural network fault diagnosis method based on knowledge and data fusion for the recoil system

To improve the accuracy of fault diagnosis for recoil systems under multiple operating conditions, a fuzzy RBF neural network (Radial Basis Function, RBF) fault diagnosis method based on knowledge and data fusion is proposed. A kinetic model for the recoil system is first established to describe the system’s behavior. Next, fuzzy RBF neural network is used to establish the relationship between abnormal operating parameters and fault causes, achieving a fault cause diagnosis accurately based on the integration of expert experience knowledge and system operation data. A study case demonstrate that the algorithm has strong knowledge and data fusion capabilities and can effectively identify faults in recoil system.

A building electrical system fault diagnosis method based on random forest optimized by improved sparrow search algorithm

Addressing the problems of manual dependence and low accuracy of traditional building electrical system fault diagnosis, this paper proposes a novel method, which is based on random forest (RF) optimized by improved sparrow search algorithm (ISSA-RF). Firstly, the method utilizes a fault collection platform to acquire raw signals of various faults. Secondly, the features of these signals are extracted by time-domain and frequency-domain analysis. Furthermore, principal component analysis is employed to reduce the dimensionality of the extracted features. Finally, the reduced features are input into ISSA-RF for classification. In ISSA-RF, the ISSA is used to optimize the parameters of the RF. The parameters for ISSA optimization are n_estimators and min_samples_leaf. In this case, the accuracy of the proposed method can reach 98.61% through validation experiment. In addition, the proposed method also exhibits superior performance compared with traditional fault classification algorithms and the latest building electrical fault diagnosis algorithms.

Remote Fault Diagnosis for the Powertrain System of Fuel Cell Vehicles Based on Random Forest Optimized with a Genetic Algorithm.

To enhance the safety and reliability of fuel cell vehicles, a remote monitoring system based on 5th generation (5G) mobile networks and controller area networks (CANs) was designed, and a random forest (RF) algorithm for the fault diagnosis for eight typical malfunctions of its powertrain system was incorporated. Firstly, the information on the powertrain system was obtained through a 5G-based monitoring terminal, and the Alibaba Cloud IoT platform was utilized for data storage and remote monitoring. Secondly, a fault diagnosis model based on the RF algorithm was constructed for fault classification; its parameters were optimized with a genetic algorithm (GA), and it was applied on the Alibaba Cloud PAI platform. Finally, the performance of the proposed RF fault diagnosis model was evaluated by comparing it with three other classification models: random search conditioning, grid search conditioning, and Bayesian optimization. Results show that the model accuracy, F1 score, and kappa value of the optimized RF fault classification model are higher than the other three. The model achieves an F1 value of 97.77% in identifying multiple typical faults of the powertrain system, as validated by vehicle malfunction data. The method demonstrates the feasibility of remote monitoring and fault diagnosis for the powertrain system of fuel cell vehicles.