Defect diagnosis in power transformer using explainable AI: A semi-supervised learning-based Partial discharge pattern approach

Battery Management Systems (BMS) are crucial for the safe and efficient operation of lithium-ion batteries in applications ranging from electric vehicles to grid storage. While Artificial Intelligence (AI) and Machine Learning (ML) have significantly advanced BMS capabilities, particularly in state estimation and fault diagnosis, the inherent 'black-box' nature of many complex models raises concerns about reliability, trustworthiness, and safety. Explainable Artificial Intelligence (XAI) offers methods to render these AI/ML models transparent and interpretable. This paper provides a comprehensive review of the application of XAI techniques within various BMS tasks. We survey the literature on XAI applied to state-of-charge (SOC), state-of-health (SOH), and remaining useful life (RUL) estimation, as well as fault detection and diagnosis, and charging management. Key XAI methodologies employed in BMS research, such as SHAP, LIME, attention mechanisms, and inherently interpretable models, are discussed. We analyze current trends, identify significant challenges including real-time implementation, evaluation of explanations, and data limitations, and suggest promising future research directions. This review aims to serve as a valuable resource for researchers and practitioners seeking to develop more transparent, reliable, and trustworthy intelligent BMS solutions.

Process monitoring is important for ensuring operational reliability and preventing occupational accidents. In recent years, data-driven methods such as machine learning and deep learning have been preferred for fault detection and diagnosis. In particular, unsupervised learning algorithms, such as auto-encoders, exhibit good detection performance, even for unlabeled data from complex processes. However, decisions generated from deep-neural-network-based models are difficult to interpret and cannot provide explanatory insight to users. We address this issue by proposing a new fault diagnosis method using explainable artificial intelligence to break the traditional trade-off between the accuracy and interpretability of deep learning model. First, an adversarial auto-encoder model for fault detection is built and then interpreted through the integration of Shapley additive explanations (SHAP) with a combined monitoring index. Using SHAP values, a diagnosis is conducted by allocating credit for detected faults, deviations from a normal state, among its input variables. The proposed diagnosis method can consider not only reconstruction space but also latent space unlike conventional method, which evaluate only reconstruction error. The proposed method was applied to two chemical process systems and compared with conventional diagnosis methods. The results highlight that the proposed method achieves the exact fault diagnosis for single and multiple faults and, also, distinguishes the global pattern of various fault types.

Fault Diagnosis using eXplainable AI: A transfer learning-based approach for rotating machinery exploiting augmented synthetic data

Abstract: Predictive maintenance (PdM) is a technique that keeps track of the condition and performance of equipment during normal operation to reduce the possibility of failures. Accurate anomaly detection, fault diagnosis, and fault prognosis form the basis of a PdM procedure. This paper aims to explore and discuss research addressing PdM using machine learning and complications using explainable artificial intelligence (XAI) techniques. While machine learning and artificial intelligence techniques have gained great interest in recent years, the absence of model interpretability or explainability in several machine learning models due to the black-box nature requires further research. Explainable artificial intelligence (XAI) investigates the explainability of machine learning models. This article overviews the maintenance strategies, post-hoc explanations, model-specific explanations, and model-agnostic explanations currently being used. Even though machine learningbased PdM has gained considerable attention, less emphasis has been placed on explainable artificial intelligence (XAI) approaches in predictive maintenance (PdM). Based on our findings, XAI techniques can bring new insights and opportunities for addressing critical maintenance issues, resulting in more informed decisions. The results analysis suggests a viable path for future studies. Conclusion: Even though machine learning-based PdM has gained considerable attention, less emphasis has been placed on explainable artificial intelligence (XAI) approaches in predictive maintenance (PdM). Based on our findings, XAI techniques can bring new insights and opportunities for addressing critical maintenance issues, resulting in more informed decisions. The results analysis suggests a viable path for future studies.

Explainable artificial intelligence of tree-based algorithms for fault detection and diagnosis in grid-connected photovoltaic systems

Once failures for large-scale transformer power occur, which will result in catastrophic economic losses and social impact. Therefore, it is necessary to design and apply a state monitoring and fault diagnosis system for large-scale transformer in order to improve the reliability and accuracy for power transformer during its running, which will benefit increasing the power enterprise economic performance, promoting economic and social development. This paper aims at large-scale power transformer, introduces Hidden Markov Models theory into power transformer fault diagnosis field, and a fault diagnosis method using the HMM is put forward. Some issues and their settling methods about the HMM applying into power transformer brings are further analysed. The fault diagnosis principle bases on the HMM is discussed in detail. The power transformer faults are classified and each of their characteristic variables is determined, accordingly, the fault diagnosis model library for power transformer is researched. Finally, the fault diagnosis system for large-scale power transformer is designed.

This paper aims at large-scale power transformer, introduces Hidden Markov Models theory into power transformer fault diagnosis field, and a fault diagnosis method using the HMM is put forward. Some issues and their settling methods about the HMM applying into power transformer brings are further analyzed. The fault diagnosis principle bases on the HMM is discussed in detail. The power transformer faults are classified and each of their characteristic variables is determined, accordingly, the fault diagnosis model library for power transformer is researched. Finally, the fault diagnosis system for large-scale power transformer is designed.

Power transformers are vital components of electrical power systems, ensuring reliable and efficient energy transfer between high-voltage transmission and low-voltage distribution networks. However, they are prone to various faults, such as insulation breakdowns, winding deformations, partial discharges, and short circuits, which can disrupt electrical service, incur significant economic losses, and pose safety risks. Traditional fault diagnosis methods, including visual inspection, dissolved gas analysis (DGA), and thermal imaging, face challenges such as subjectivity, intermittent data collection, and reliance on expert interpretation. To address these limitations, this paper proposes a novel distributed approach for multi-fault diagnosis of power transformers based on a self-organizing neural network combined with data augmentation and incremental learning techniques. The proposed framework addresses critical challenges, including data quality issues, computational complexity, and the need for real-time adaptability. Data cleaning and preprocessing techniques improve the reliability of input data, while data augmentation generates synthetic samples to mitigate data imbalance and enhance the recognition of rare fault patterns. A two-stage classification model integrates unsupervised and supervised learning, with k-means clustering applied in the first stage for initial fault categorization, followed by a self-organizing neural network in the second stage for refined fault diagnosis. The self-organizing neural network dynamically suppresses inactive nodes and optimizes its training parameter set, reducing computational complexity without sacrificing accuracy. Additionally, incremental learning enables the model to continuously adapt to new fault scenarios without modifying its architecture, ensuring real-time performance and adaptability across diverse operational conditions. Experimental validation demonstrates the effectiveness of the proposed method in achieving accurate, efficient, and adaptive fault diagnosis for power transformers, outperforming traditional and conventional machine learning approaches. This work provides a robust framework for integrating advanced machine learning techniques into power system monitoring, paving the way for automated, real-time, and reliable transformer fault diagnosis systems.

This study presents a comprehensive methodology for developing a Fault Detection and Diagnosis (FDD) system for an industrial distillation column using advanced machine learning algorithms. Steady state and dynamic simulations in Aspen Plus® generate extensive datasets under normal and faulty conditions. Feature engineering, using the Minimum Redundancy Maximum Relevance (MRMR) algorithm, selects the most relevant features for fault detection. Various machine learning models, including Decision Trees, Support Vector Machines, k-nearest Neighbours, and Neural Networks, were trained and evaluated based on performance metrics such as accuracy, recall, precision, and F1 score. The top models were integrated into a stacked classifier system with a voting mechanism to enhance fault detection reliability. Explainable Artificial Intelligence (XAI) techniques, such as Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), were incorporated to improve model interpretability, allowing engineers to understand and validate the FDD system's decision-making process. Simulation results confirm that the proposed methodology accurately identifies and classifies faults. By integrating dynamic simulations, advanced machine learning, and XAI techniques, a robust and scalable solution is achieved for fault detection in distillation columns, improving operational reliability, safety, and reducing downtime. Future work could extend this approach to other industrial processes and explore additional machine learning algorithms to further enhance performance.

Partial discharge (PD) pattern recognition is an important tool in HV insulation diagnosis. A PD pattern recognition approach of HV power transformers based on a neural network is proposed in this paper. A commercial PD detector is firstly used to measure the 3-D PD patterns of epoxy resin power transformers. Then, two fractal features (fractal dimension and lacunarity) extracted from the raw 3-D PD patterns are presented for the neural- network-based (NN-based) recognition system. The system can quickly and stably learn to categorize input patterns and permit adaptive processes to access significant new information. To demonstrate the effectiveness of the proposed method, the recognition ability is investigated on 150 sets of field tested PD patterns of epoxy resin power transformers. Different types of PD within power transformers are identified with rather encouraged results.

An explainable artificial intelligence approach for unsupervised fault detection and diagnosis in rotating machinery

XFDDC: eXplainable Fault Detection Diagnosis and Correction framework for chemical process systems

Power transformer is one of the most crucial devices in power grid. It is significant to determine incipient faults of power transformers fast and accurately. Input features play critical roles in fault diagnosis accuracy. In order to further improve the fault diagnosis performance of power transformers, a random forest feature selection method coupled with optimized kernel extreme learning machine is presented in this study. Firstly, the random forest feature selection approach is adopted to rank 42 related input features derived from gas concentration, gas ratio and energy-weighted dissolved gas analysis. Afterwards, a kernel extreme learning machine tuned by the Aquila optimization algorithm is implemented to adjust crucial parameters and select the optimal feature subsets. The diagnosis accuracy is used to assess the fault diagnosis capability of concerned feature subsets. Finally, the optimal feature subsets are applied to establish fault diagnosis model. According to the experimental results based on two public datasets and comparison with 5 conventional approaches, it can be seen that the average accuracy of the proposed method is up to 94.5%, which is superior to that of other conventional approaches. Fault diagnosis performances verify that the optimum feature subset obtained by the presented method can dramatically improve power transformers fault diagnosis accuracy.

Fault diagnosis (FD), comprising fault detection, isolation, identification and accommodation, enables structural health monitoring (SHM) systems to operate reliably by allowing timely rectification of sensor faults that may cause data corruption or loss. Although sensor fault identification is scarce in FD of SHM systems, recent FD methods have included fault identification assuming one sensor fault at a time. However, real-world SHM systems may include combined faults that simultaneously affect individual sensors. This paper presents a methodology for identifying combined sensor faults occurring simultaneously in individual sensors. To improve the quality of FD and comprehend the causes leading to sensor faults, the identification of combined sensor faults (ICSF) methodology is based on a formal classification of the types of combined sensor faults. Specifically, the ICSF methodology builds upon long short-term memory (LSTM) networks, i.e. a type of recurrent neural networks, used for classifying ‘sequences’, such as sets of acceleration measurements. The ICSF methodology is validated using real-world acceleration measurements from an SHM system installed on a bridge, demonstrating the capability of the LSTM networks in identifying combined sensor faults, thus improving the quality of FD in SHM systems. Future research aims to decentralize the ICSF methodology and to reformulate the classification models in a mathematical form with an explanation interface, using explainable artificial intelligence, for increased transparency.

State-of-the-art review on asset management methodologies for oil-immersed power transformers