This paper introduces KG-FLoc, a knowledge graph-enhanced framework for secondary circuit fault localization in intelligent substations. The proposed KG-FLoc innovatively formalizes secondary components (e.g., circuit breakers, disconnectors) as graph nodes and their multi-dimensional relationships (e.g., electrical connections, control logic) as edges, constructing the first comprehensive knowledge graph (KG) to structurally and operationally model secondary circuits. By reframing fault localization as a knowledge graph link prediction task, KG-FLoc identifies missing or abnormal connections (edges) as fault indicators. To address dynamic topologies and sparse fault samples, KG-FLoc integrates two core innovations: (1) a relation-aware gated unit (RGU) that dynamically regulates information flow through adaptive gating mechanisms, and (2) a hierarchical graph isomorphism network (GIN) architecture for multi-scale feature extraction. Evaluated on real-world datasets from 110 kV/220 kV substations, KG-FLoc achieves 97.2% accuracy in single-fault scenarios and 93.9% accuracy in triple-fault scenarios, surpassing SVM, RF, MLP, and standard GNN baselines by 12.4–31.6%. Beyond enhancing substation reliability, KG-FLoc establishes a knowledge-aware paradigm for fault diagnosis in industrial systems, enabling precise reasoning over complex interdependencies.

Random wavelet kernels for interpretable fault diagnosis in industrial systems

Advancing fault diagnosis in industrial systems: The power of V-nets for managing complex event sequences

From Seen to Unseen: Harnessing Temporal Dependencies and Graph Structures for Zero-Sample Fault Diagnosis in Industrial Systems

Self-adaptive selection graph pooling based fault diagnosis method under few samples and noisy environment

Valves and other actuators may fail and cause economic losses or safety accidents. To ensure the stable operation of a control system, it is necessary to identify the failures of various valves and carry out the corresponding maintenance. Several methods are designed and implemented for valve fault diagnosis in this paper. In particular, a novel fault diagnosis method based on a time-series convolution network (FDM-TSCN) is proposed, which is built on a time-series data feature extracting and convolutional neural network. FDM-TSCN can classify 18 out of 19 types of fault, while many other methods cannot. This algorithm is presented in detail and implemented as a prototype system. Comprehensive simulations are performed on valve fault datasets that are generated by the development and application of methods for actuator fault diagnosis in industrial systems (DAMADICS). The simulation results prove the effectiveness and superiority of the proposed FDM-TSCN method. All of the source codes and related data in the paper are made available, which enables other researchers to verify the work easily and may inspire them to carry out more informed research.

With the lack of failure data, class imbalance has become a common challenge in the fault diagnosis of industrial systems. The oversampling methods can tackle the class-imbalanced problem by generating the minority samples to balance the training set. However, one of the main challenges of the existing oversampling methods is how to generate high-quality minority samples. Traditional oversampling methods regard all synthetic samples as minority ones to be added to the training set without filtering. The low-quality synthetic samples would distort the distribution of the dataset and worsen the classification performance. In this article, we propose a weakly supervised oversampling method that treats all synthetic samples as unlabeled samples and develops a graph semisupervised learning algorithm to select high-quality synthetic samples, adding into the final training set as minority samples. To improve the quality of synthetic samples, we propose a cost-sensitive neighborhood component analysis dimensionality reduction method to enhance domain information validity in high-dimensional datasets. Finally, combining a boosting-based ensemble framework, we propose a new imbalanced learning framework suitable for high dimensionality and highly imbalanced fault diagnosis in industrial systems. The experimental validation is performed on five real-world wind turbine blade cracking failure datasets and compared to 15 benchmark methods. The experimental results show that average performances and robustness of the proposed framework are significantly better than those of the benchmark methods.

Lamb waves have been widely used for structural damage detection. However, practical applications of this technique are still limited. One of the main reasons is due to the complexity of Lamb wave propagation modes. Therefore, instead of directly analysing and interpreting Lamb wave propagation modes for information about health conditions of the structure, this study has proposed another approach that is based on statistical analyses of the stationarity of Lamb waves. The method is validated by using Lamb wave data from intact and damaged aluminium plates exposed to temperature variations. Four popular unit root testing methods, including Augmented Dickey–Fuller (ADF) test, Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test, Phillips–Perron (PP) test, and Leybourne–McCabe (LM) test, have been investigated and compared in order to understand and make statistical inference about the stationarity of Lamb wave data before and after hole damages are introduced to the aluminium plate. The separation between t-statistic features, obtained from the unit root tests on Lamb wave data, is used for damage detection. The results show that both ADF test and KPSS test can detect damage, while both PP and LM tests were not significant for identifying damage. Moreover, the ADF test was more stable with respect to temperature changes than the KPSS test. However, the KPSS test can detect damage better than the ADF test. Moreover, both KPSS and ADF tests can consistently detect damages in conditions where temperatures vary below 60 °C. However, their t-statistics fluctuate more (or less homogeneous) for temperatures higher than 65 °C. This suggests that both ADF and KPSS tests should be used together for Lamb wave based structural damage detection. The proposed stationarity-based approach is motivated by its simplicity and efficiency. Since the method is based on the concept of stationarity of a time series, it can find applications not only in Lamb wave based SHM but also in condition monitoring and fault diagnosis of industrial systems.

Denoising is a permanent topic and there are various denoisers proposed in the fault diagnosis of industrial systems. However, it is still ambiguous to evaluate their performance quantitatively in terms of mean square error (MSE) and further achieve their maximum gains, because it is always infeasible to obtain the MSE metric without real feature signals in the engineering practices. Therefore, leveraging Stein Unbiased Risk Estimator (SURE) theory, a bi-level nested sparse optimization framework (BiNSOF) is proposed to jointly optimize a parameterized sparse denoiser as well as its regularization parameter, further obtaining the near-optimal fault features with a minimum MSE. The inner level of BiNSOF utilizes a $\ell _{1}$ regularized sparse denoiser to describe the intrinsic sparse structure of feature information, which can be effectively addressed by popular primal-dual splitting schemes. The core of the outer optimization level is a SURE-based unbiased estimator for MSE, and the minimum MSE search problem is transformed into a quadratic optimization problem which could be fast solved by classic golden section search schemes. The proposed BiNOSP can perfectly approximate the oracle MSE without any real feature information, and further provides a reliable way to obtain the optimal hyper-parameter sets for the maximum performance gains of the sparse denoiser. The computational complexity of the advocated approach is also investigated. Moreover, its feasibility and performances are profoundly evaluated by a set of comprehensive numerical studies. Lastly, two bearing fault detection cases confirm the applicability and superiority of the proposed framework.

Optimizing kernel methods to reduce dimensionality in fault diagnosis of industrial systems

Online automatic fault diagnosis in industrial systems is essential for guaranteeing safe, reliable and efficient operations. However, difficulties associated with computational overload, ubiquitous uncertainties and insufficient fault samples hamper the engineering application of intelligent fault diagnosis technology. Geared towards the settlement of these problems, this paper introduces the method of dynamic uncertain causality graph, which is a new attempt to model complex behaviors of real-world systems under uncertainties. The visual representation to causality pathways and self-relied “chaining” inference mechanisms are analyzed. In particular, some solutions are investigated for the diagnostic reasoning algorithm to aim at reducing its computational complexity and improving the robustness to potential losses and imprecisions in observations. To evaluate the effectiveness and performance of this method, experiments are conducted using both synthetic calculation cases and generator faults of a nuclear power plant. The results manifest the high diagnostic accuracy and efficiency, suggesting its practical significance in large-scale industrial applications.

A variant of the particle swarm optimization for the improvement of fault diagnosis in industrial systems via faults estimation

peer reviewed

"En el presente trabajo se presenta un estudio sobre la aplicación de estrategias bioinspiradas para la optimización al diagnóstico de fallos en sistemas industriales. El objetivo principal es establecer una base para el desarrollo de nuevos y viables métodos de diagnóstico de fallos basados en modelos que permitan mejorar las dificultades de los métodos actuales. Estas dificultades están relacionadas, fundamentalmente, con la sensibilidad ante la presencia de fallos y la robustez ante perturbaciones externas. En el estudio se consideraron los algoritmos Evolución Diferencial y Optimización por Colonia de Hormigas. La efectividad de la propuesta es analizada mediante experimentos con el conocido problema de prueba de los dos tanques. Los experimentos consideraron presencia de ruido en la información y fallos incipientes de manera que fuera posible analizar las ventajas de la propuesta en cuanto a diagnóstico robusto y sensible. Los resultados obtenidos indican que el enfoque propuesto y, principalmente, la combinación de los dos algoritmos, caracterizan una metodología prometedora para el diagnóstico de fallos."

Fault diagnosis of industrial systems by conditional Gaussian network including a distance rejection criterion

Fault diagnosis of industrial systems is defined as the operation of faults detection and isolation. This paper presents a decentralized approach to realize the diagnosis of Discrete Event Systems (DES). This approach uses a set of diagnosers. Each diagnoser observes a part of the process and takes a local decision about the occurrence of a fault and its localisation. The construction of the local diagnosers is based on a modular modelling of the plant elements, the controller specifications and the temporal information about the actuators reactivity. In order to verify that the set of local diagnosers are capable to diagnose a set of faults within a bounded delay, a notion of co-diagnosability must be defined. All local diagnosis decisions must be merged in order to obtain one global diagnosis decision. This fusion can be realized by a coordinator. An example of manufacturing application is used to illustrate our approach.

A method for fault diagnosis of industrial systems is presented. Plant devices, sensors, actuators and diagnostic tests are described as stochastic finite-state machines. A formal composition rule of these models is given to obtain: 1) the set of admissible fault signatures; 2) their conditional probability given any fault; and 3) the conditional probability of a fault given a prescribed signature. The modularity and flexibility of this method make it suitable to deal with complex systems made by a large number of components. The method is used in an industrial automotive application, specifically the diagnosis of the throttle body and of the angular sensors measuring the throttle plate angle is described in detail.

Fault Diagnosis on Industrial Systems Based on a Multiple Model Approach

Applications of electrical tomography for gas–solids and liquid–solids flows — a review