Fault Diagnosis Of Complex Systems Research Articles

Empirical study on fine-tuning pre-trained large language models for fault diagnosis of complex systems

With increasing complexity and interconnectivity of the modern industrial systems, effectively diagnosing faults is a core step to Prognostic and Health Management (PHM). Language models, particularly Large Language Models (LLMs) that pre-trained on massive corpora, have demonstrated remarkable capabilities in Natural Language Processing (NLP) and its related downstream tasks. However, how to leverage these models for facilitating system fault diagnosis and verify its effectiveness is rarely explored. This paper incorporates the potential comprehension ability of pre-trained LLMs, and investigates the efficacy of fine-tuning LLMs for realizing efficient system fault diagnosis. The experiments conducted in this study involve both open-source and closed-source models, and utilize a simulation and a real fault diagnosis dataset. We find that these models consistently achieve high performance across various metrics compared to the baselines. Additionally, qualitative and quantitative analysis is performed to investigate several aspects of the approach, such as the impact of dataset size, data normalization, missing values and explainability of the diagnosis, further showcasing the potential as well as limitations of the approach.

Comparing Fault Tree Analysis methods combined with Generalized Grey Relation Analysis: A new approach and case study in the automotive industry

The failure modes of products gradually show a diversified trend with the precision and complexity of the product structure. The combination of fault tree analysis and generalized grey relational analysis is widely used in the fault diagnosis of complex systems. In this study, we utilize a method that combines fault tree analysis and generalized grey relational analysis. This method is applied to diagnose the Expansion Adhesive Debonding fault of automobile doors. Then, we analyse and compare the differences in actual fault diagnosis results. The comparison involves three analysis methods: Fault Tree Analysis combined with Absolute Grey Relation Analysis (F-AGRA), Fault Tree Analysis combined with Relative Grey Relation Analysis (F-RGRA), and Fault Tree Analysis combined with Comprehensive Grey Relation Analysis (F-CGRA). Subsequently, we compare the findings with actual production results. This comparison allows us to discuss the differences between the three methods in the fault diagnosis of complex systems. We also discuss the application occasions of these methods. This study will provide a new method for fault analysis and fault diagnosis in the actual production of the automobile manufacturing industry. This method can eliminate faults effectively and accurately and improve product quality and productivity.

Hydraulic Systems Fault Diagnosis Based on Random Forests Recursive Feature Elimination and XGBoost

This paper proposes an RFRFE-XGBoost method for fault mode recognition in hydraulic systems. The proposed method combines random forests-based recursive feature elimination (RFRFE) and extreme gradient boosting (XGBoost) to effectively identify important features and improve fault diagnosis efficiency and accuracy. The method is validated on relevant datasets and compared with existing methods, demonstrating its effectiveness and superiority. The results show that RFRFE-XGBoost can accurately recognize various fault modes and outperforms other methods in terms of classification accuracy and computational efficiency. The proposed method provides a promising approach for fault diagnosis in complex systems.

Coarse-to-Fine: Progressive Knowledge Transfer-Based Multitask Convolutional Neural Network for Intelligent Large-Scale Fault Diagnosis.

In modern industry, large-scale fault diagnosis of complex systems is emerging and becoming increasingly important. Most deep learning-based methods perform well on small number of fault diagnosis, but cannot converge to satisfactory results when handling large-scale fault diagnosis because the huge number of fault types will lead to the problems of intra/inter-class distance unbalance and poor local minima in neural networks. To address the above problems, a progressive knowledge transfer-based multitask convolutional neural network (PKT-MCNN) is proposed. First, to construct the coarse-to-fine knowledge structure intelligently, a structure learning algorithm is proposed via clustering fault types in different coarse-grained nodes. Thus, the intra/inter-class distance unbalance problem can be mitigated by spreading similar tasks into different nodes. Then, an MCNN architecture is designed to learn the coarse and fine-grained task simultaneously and extract more general fault information, thereby pushing the algorithm away from poor local minima. Last but not least, a PKT algorithm is proposed, which can not only transfer the coarse-grained knowledge to the fine-grained task and further alleviate the intra/inter-class distance unbalance in feature space, but also regulate different learning stages by adjusting the attention weight to each task progressively. To verify the effectiveness of the proposed method, a dataset of a nuclear power system with 66 fault types was collected and analyzed. The results demonstrate that the proposed method can be a promising tool for large-scale fault diagnosis.

Multi-Sensor Fault Diagnosis Based on Time Series in an Intelligent Mechanical System

Intelligent mechanical systems are a focused area nowadays. One of the requirements of intelligent mechanical systems is to achieve intelligent fault diagnosis through the real-time acquisition and analysis of data from various sensors installed on mechanical components. In this paper, a new fault diagnosis method is proposed to solve the problems of difficulty in integrating the fault diagnosis algorithm and locating fault parts due to the complexity of modern mechanical systems. The complexity of modern industrial intelligent systems is due to the fact that the systems are composed of multiple components and there are various connections between them. Common fault diagnosis is to design specialized fault identification algorithms for the physical characteristics of each component, and the integration of different algorithms is a major challenge for system performance. Therefore, this paper investigates a general algorithm for the fault diagnosis of complex systems using the timing characteristics of sensors and transfer entropy. The fault diagnosis algorithm is based on the prediction of multi-dimensional long time series using Autoformer, and fault identification is performed based on the deviation of the predicted value from the actual value. After fault identification, a root cause analysis method of faults based on transfer entropy is proposed. The method can locate the component where the fault occurs more accurately based on the analysis of the cause-effect relationship of each component and help maintenance personnel to troubleshoot the fault.

A general enhancement method for test strategy generation for the sequential fault diagnosis of complex systems

In order to improve the reliability, operational readiness and system safety of equipment, testability should be seriously considered in the design stage. As an important part of design for testability, test sequence generation is a binary identification problem because a minimal expected cost testing procedure must be developed in order to determine the amount of possible failure sources, if any, are present. Many algorithms have been proposed, but the generation time is long or the test cost is high when dealing with a large-scale dependency matrix. To address this issue, we propose a general enhancement method based on the SVM, the ECA* and the Monte Carlo. It can be applied to any existing algorithm and can effectively improve the performance. The available tests are classed based on the SVM according to the information of nodes, the ECA* is used to cluster states, and the morphological function of the test sequence is obtained through the Monte Carlo simulation. All this information is fused to dynamically adjust the scale of the dependency matrix and selected to modify the parameters. Experiments show that the existing algorithms have shorter calculation time and lower costs because the information is considered more comprehensively after enhancement.

A Modified Generative Adversarial Network for Fault Diagnosis in High-Speed Train Components with Imbalanced and Heterogeneous Monitoring Data

Data-driven methods are widely considered for fault diagnosis in complex systems. However, in practice the between-class imbalance due to limited faulty samples may deteriorate their classification performance. To address this issue, synthetic minority methods for enhancing data have been proved to be effective in many applications. Generative Adversarial Networks (GANs), capable of automatic features extraction, can also be adopted for augmenting the faulty samples. However, the monitoring data of a complex system may include not only continuous signals, but also discrete/categorical signals. Since the current GAN methods still have some challenges in handling such heterogeneous monitoring data, a Mixed Dual Discriminator GAN (noted as M-D2GAN) is proposed in this work. In order to render the expanded fault samples more aligned with the real situation, and improve the accuracy and robustness of the fault diagnosis model, different types of variables are generated in different ways, including: floating-point, integer, categorical, and hierarchical. For effectively considering the class imbalance problem, proper modifications are made to the GAN model, where a normal class discriminator is added. A practical case study concerning the braking system of a high-speed train is carried out to verify the effectiveness of the proposed framework. Compared to the classic GAN, the proposed framework achieves better results with respect to F-measure and G-mean metrics.

Guest Editorial for Artificial Intelligence for Machine Fault Diagnosis Special Section

With the rapid technological development and production requirement, machines and equipment in modern industry, such as advanced manufacturing, transportation, aerospace, and civil infrastructure, become increasingly functional and complex. Machine fault diagnosis plays a significant role for the productivity, reliability, and safety of industrial systems. In the recent decade, data-driven solutions have become more effective and promising for fault diagnosis of complex machines due to increasing data availability and processing capacity. Artificial intelligence (AI) techniques, especially deep learning approaches, are the most powerful tools in achieving accurate fault diagnosis of complex systems. However, AI-based fault diagnosis still faces great challenges in feasibility and reliability for real applications, including the lack of fault condition data, varying working conditions, insufficient generalization capability of AI models, the black-box nature of most AI methods, etc. This special section focuses on advanced and innovative solutions to address a broad view of problems about AI-based machine fault diagnosis. Six out of 18 submissions included in this special section summarize some of the research and applications in this field.

A novel fault diagnosis method based on CNN and LSTM and its application in fault diagnosis for complex systems

Fault diagnosis plays an important role in actual production activities. As large amounts of data can be collected efficiently and economically, data-driven methods based on deep learning have achieved remarkable results of fault diagnosis of complex systems due to their superiority in feature extraction. However, existing techniques rarely consider time delay of occurrence of faults, which affects the performance of fault diagnosis. In this paper, by synthetically considering feature extraction and time delay of occurrence of faults, we propose a novel fault diagnosis method that consists of two parts, namely, sliding window processing and CNN-LSTM model based on a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM). Firstly, samples obtained from multivariate time series by the sliding window processing integrates feature information and time delay information. Then, the obtained samples are fed into the proposed CNN-LSTM model including CNN layers and LSTM layers. The CNN layers perform feature learning without relying on prior knowledge. Time delay information is captured with the use of the LSTM layers. The fault diagnosis of the Tennessee Eastman chemical process is addressed, and it is verified that the predictive accuracy and noise sensitivity of fault diagnosis can be greatly improved when the proposed method is applied. Comparisons with five existing fault diagnosis methods show the superiority of the proposed method.

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

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

A BRB-Based Effective Fault Diagnosis Model for High-Speed Trains Running Gear Systems

Fault diagnosis is a key way to improve the efficient, safe and stable operation of high-speed trains. This paper proposes a fault diagnosis method based on belief rule base with mixed reliability (BRB-mr). Different from the traditional BRB, this method considers two kinds of interference factors that affect the observation data in engineering practice, including the performance of sensors and the influence of external environment, and we quantify them as static reliability and dynamic reliability of attributes in BRB. In order to integrate two kinds of reliability factors into the reasoning of BRB, a discount method is developed based on Dempster-Shafer theory (D-S theory), which is helpful for more accurate diagnosis. In this paper, the effectiveness and practicability of the method are verified by a single fault of the running gear, and the supplementary numerical data verified its feasibility in multiple fault mode diagnosis. Then this method is compared with traditional methods. The result shows BRB-mr model has stronger diagnostic ability to identify faults and it has a certain engineering application value to be extended to other complex system fault diagnosis.

System Fault Diagnosis Method Based on OSDG Model

For the difficulties of cross-link fault diagnosis in complex systems, a fault diagnosis method based on optimised signed directed graph model is proposed. First, the system structure is layered and the control bridge is used to implement a top-down hierarchical structure. The SDG is used to describe the hierarchical structure of the system, and the SDG model is also layered so the fault information is propagated unidirectionally according to the layer. The reverse search strategy based on the fault propagation matrix is used to judge the compatible path, obtain the set of candidate fault sources, use the use availability to objectively evaluate the impact of multiple fault sources to improve the efficiency of troubleshooting. The example analysis shows that the method in this paper can adapt to the fault diagnosis needs of complex systems, and has practical application value for shortening the time of system fault determination and improving the accuracy of troubleshooting.

A novel method of composite multiscale weighted permutation entropy and machine learning for fault complex system fault diagnosis

A novel fault diagnosis method is proposed for rolling bearing by combining extreme-point symmetric mode decomposition (ESMD) composite multiscale weighted permutation entropy (CMWPE) and gravitational search algorithm based on multiple adaptive constraint strategy (MACGSA) optimized least squares support vector machine (LSSVM). In order to solve the problem of intrinsic mode function (IMF) modal aliasing and small differences in fault features, ESMD and CMWPE are used to obtain a more sensitive high-dimensional feature vector set. Aiming at the low accuracy of LSSVM fault diagnosis, MACGSA was used to optimize LSSVM to improve the accuracy of fault diagnosis. ESMD is used to process the rolling bearing data to obtain a series of IMFs; Then, extracting the CMWPE values of IMFs to form a high-dimensional feature vector set; Finally, the MACGSA-LSSVM model is adopted to achieve fault classification. Compared with other diagnostic methods, this method has higher diagnostic accuracy.

Propositional logic concept for fault diagnosis in complex systems

A great number of monitoring technologies have been developed especially for complex systems within the critical zones such as electric power substations, nuclear energy systems. But also, there is no single instruction or standardization in fault-focused on-line/off-line monitoring applications due to the acceleration of technological developments. Field experts have difficulty in choosing which test and measurement systems should be used in which stage of the complex systems. In this study, the propositional logic-based concept is presented, which field experts can use to manage this process. In this concept, test and measurement systems can be grouped according to the priority-order. According to the results of the graded groups on this concept, the suspected fault is verified by the cause of the occurrence. The applicability of the proposed concept has been tried to be explained by creating possible failure scenarios on the transformer. The theoretically validated concept can be used for even more fault situations. This concept can also be used in another complex systems with a large number of T&M systems where very different fault conditions can occur. 2009 Elsevier Ltd. All rights reserved.

A novel method of combining generalized frequency response function and convolutional neural network for complex system fault diagnosis.

To solve the problem of low accuracy in traditional fault diagnosis methods, a novel method of combining generalized frequency response function(GFRF) and convolutional neural network(CNN) is proposed. In order to accurately characterize system state information, this paper proposed a variable step size least mean square (VSSLMS) adaptive algorithm to calculate the second-order GFRF spectrum values under normal and fault states; In order to improve the ability of fault feature extraction, a convolution neural network (CNN) with gradient descent learning rate and alternate convolution layer and pooling layer is designed to extract the fault features from GFRF spectrum. In the proposed method, the second-order GFRF spectrum of each state of Permanent Magnet Synchronous Motor (PMSM) is obtained by VSSLMS; Then, the two-dimension GFRF spectrum, which is regarded as the gray value of the image,will be further transformed into image. Finally, the CNN is trained with learning rate by gradient descent way to realize the fault diagnosis of PMSM. Experimental results indicate that the accuracy of proposed method is 98.75%, which verifies the reliability of the proposed method in application of PMSM fault diagnosis.

The Cubic Dynamic Uncertain Causality Graph: A Methodology for Temporal Process Modeling and Diagnostic Logic Inference

To meet the demand for dynamic and highly reliable real-time fault diagnosis for complex systems, we extend the dynamic uncertain causality graph (DUCG) by proposing novel temporal causality modeling and reasoning methods. A new methodology, the Cubic DUCG, is therefore developed. It exploits an efficient scheme for compactly representing and accurately reasoning about the dynamic causalities in the system fault-spreading process. The Cubic DUCG is characterized by: 1) continuous generation of a causality graph that allows for causal connections penetrating among any number of time slices and discards the restrictive assumptions (about the underlying graph structure) upon which the existing research commonly relies; 2) a modeling scheme of complex causalities that includes dynamic negative feedback loops in a natural and intuitive manner; 3) a rigorous and reliable inference algorithm based on complete causalities that reflect real-time fault situations rather than on the cumulative aggregation of static time slices; and 4) some solutions to causality simplification and reduction, graphical transformation, and logical reasoning, for the sake of reducing the reasoning complexity. A series of fault diagnosis experiments on a nuclear power plant simulator verifies the accuracy, robustness, and efficiency of the proposed methodology.

A novel method of combining nonlinear frequency spectrum and deep learning for complex system fault diagnosis

A novel fault diagnosis method is proposed for complex systems by combining nonlinear frequency spectrum and stacked denoising auto-encoders (SDAE). In order to solve the problem of large calculation amount of generalized frequency response functions (GFRF), one-dimensional nonlinear output frequency response functions (NOFRF) are used to obtain nonlinear frequency spectrum. In order to overcome the problem of weak ability of fault features extraction, stacked denoising auto-encoders (SDAE) neural network is adopted to extract the fault features from nonlinear frequency spectrum. In this novel method, four orders nonlinear frequency spectrum of each state of Permanent Magnet Synchronous Motor (PMSM) are obtained by identification algorithm; Then, choosing suitable sampling points from four orders frequency spectrum to construct high-dimensional data; Finally, stacked denoising auto-encoders (SDAE) neural network is designed to realize the output of fault classification. Simulations indicate that the proposed method has good real-time performance and high diagnosis accuracy.

An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems

Abstract The machine fault diagnosis is being considered in a larger-scale complex system with numerous measurements from diverse subsystems or components, where the collected data is with disparate characteristics and needs more prevailing methods for data preprocessing, feature extraction and selection. This work presents a novel diagnosis framework that combines the spatiotemporal pattern network (STPN) approach with convolutional neural networks (CNN) to build a hybrid ST-CNN scheme. The proposed framework is tested on two data sets for diagnosing unseen operating conditions and fault severities respectively, to evaluate its generalization ability, which is essential for the application in machine fault diagnosis as not all of the aforementioned scenarios have sufficient labeled data to train a model. The results show that the proposed ST-CNN framework outperforms or is comparable to shallow methods (support vector machine and random forest) and 1D CNN. Through visualizing the activations, it is verified that the spatial features can elevate the diagnosis accuracy, and more general features are determined by the proposed approach to form an adaptive classifier for diverse operating conditions and different fault severities.

Fisher Discriminative Sparse Representation Based on DBN for Fault Diagnosis of Complex System

Fault detection and diagnosis in the chemical industry is a challenging task due to the large number of measured variables and complex interactions among them. To solve this problem, a new fault diagnosis method named Fisher discriminative sparse representation (FDSR), based on deep belief network (DBN), is proposed in this paper. We used DBN to extract the features of all faulty and normal modes. Features extracted by the DBN were used to learn subdictionaries, then the overcomplete dictionary was constructed by cascading all subdictionaries in order, and each dictionary atom corresponded to class labels. The Fisher discrimination criterion (FDC) was applied to the dictionary learning to ensure smaller within-class scatter but greater between-class scatter. The quadratic programming method was applied to estimate the sparse coefficients simultaneously class by class. Therefore, both the reconstruction error and sparse coefficients were discriminative, so that the reconstruction error after sparse coding can be used for pattern classification. An experiment performed on the Tennessee Eastman (TE) process indicated that compared with the traditional monitoring methods, the FDSR based on DBN produced more interpretable results and achieved superior performance for feature extraction and classification in the field of complex system fault diagnosis. T-distributed stochastic neighbor embedding (t-SNE) appropriately visualized the performance of the proposed method and produced reliable fault diagnosis results.

Investigation of Bayesian network for reliability analysis and fault diagnosis of complex systems with real case applications

Reliability is critical for complex engineering systems. Traditionally, reliability analysis and fault diagnosis of complex engineering systems is based on reliability block diagram and fault tree. These methods are limited either on the flexibility for system characterization or on the capability for quantitative analysis. Recently, the Bayesian network has been introduced in reliability engineering, and it has been demonstrated with great flexibility. In this article, the Bayesian network is investigated for reliability analysis and fault diagnosis of complex engineering systems through two real cases. It includes the case of a high-speed train representing the complex system with standardized components and the case of a critical subsystem of a high-power solid-state laser representing the complex system with highly customized components. In particular, Bayesian networks are constructed to model the reliability of these systems, where transformations of reliability block diagram and fault tree into Bay...