Explainable AI guided unsupervised fault diagnostics for high-voltage circuit breakers

• URL: http://arxiv.org/abs/2507.19168v1
• Date: Fri, 25 Jul 2025 11:14:56 GMT
• Title: Explainable AI guided unsupervised fault diagnostics for high-voltage circuit breakers
• Authors: Chi-Ching Hsu, Gaëtan Frusque, Florent Forest, Felipe Macedo, Christian M. Franck, Olga Fink,
• Abstract summary: Commercial high-voltage circuit breaker (CB) condition monitoring systems rely on directly observable physical parameters.<n>We propose a novel unsupervised fault detection and segmentation framework for CBs based on vibration and acoustic signals.<n>We provide an unsupervised explainability-guided fault diagnostics approach using XAI to offer domain experts potential indications of the aged or faulty components.
• Score: 6.498237940960343
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Commercial high-voltage circuit breaker (CB) condition monitoring systems rely on directly observable physical parameters such as gas filling pressure with pre-defined thresholds. While these parameters are crucial, they only cover a small subset of malfunctioning mechanisms and usually can be monitored only if the CB is disconnected from the grid. To facilitate online condition monitoring while CBs remain connected, non-intrusive measurement techniques such as vibration or acoustic signals are necessary. Currently, CB condition monitoring studies using these signals typically utilize supervised methods for fault diagnostics, where ground-truth fault types are known due to artificially introduced faults in laboratory settings. This supervised approach is however not feasible in real-world applications, where fault labels are unavailable. In this work, we propose a novel unsupervised fault detection and segmentation framework for CBs based on vibration and acoustic signals. This framework can detect deviations from the healthy state. The explainable artificial intelligence (XAI) approach is applied to the detected faults for fault diagnostics. The specific contributions are: (1) we propose an integrated unsupervised fault detection and segmentation framework that is capable of detecting faults and clustering different faults with only healthy data required during training (2) we provide an unsupervised explainability-guided fault diagnostics approach using XAI to offer domain experts potential indications of the aged or faulty components, achieving fault diagnostics without the prerequisite of ground-truth fault labels. These contributions are validated using an experimental dataset from a high-voltage CB under healthy and artificially introduced fault conditions, contributing to more reliable CB system operation.

Related papers

• Fault detection and diagnosis for the engine electrical system of a space launcher based on a temporal convolutional autoencoder and calibrated classifiers [0.0]
  This paper outlines a first step toward developing an onboard fault detection and diagnostic capability for the next generation of reusable space launchers.<n>Unlike existing approaches in the literature, our solution is designed to meet a broader range of key requirements.<n>The proposed solution is based on a temporal convolutional autoencoder to automatically extract low-dimensional features from raw sensor data.
  arXiv  Detail & Related papers  (2025-07-17T11:50:29Z)
• CANTXSec: A Deterministic Intrusion Detection and Prevention System for CAN Bus Monitoring ECU Activations [53.036288487863786]
  We propose CANTXSec, the first deterministic Intrusion Detection and Prevention system based on physical ECU activations.<n>It detects and prevents classical attacks in the CAN bus, while detecting advanced attacks that have been less investigated in the literature.<n>We prove the effectiveness of our solution on a physical testbed, where we achieve 100% detection accuracy in both classes of attacks while preventing 100% of FIAs.
  arXiv  Detail & Related papers  (2025-05-14T13:37:07Z)
• Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly Detections [50.343419243749054]
  Anomaly Detection (AD) involves identifying deviations from normal data distributions.<n>We propose a novel approach that conditions the prompts of the text encoder based on image context extracted from the vision encoder.<n>Our method achieves state-of-the-art performance, improving performance by 2% to 29% across different metrics on 14 datasets.
  arXiv  Detail & Related papers  (2025-04-15T10:42:25Z)
• FedLED: Label-Free Equipment Fault Diagnosis with Vertical Federated Transfer Learning [16.520970191947935]
  FedLED is the first unsupervised vertical FTL equipment fault diagnosis method. knowledge of the unlabeled target domain is further exploited for effective unsupervised model transfer.
  arXiv  Detail & Related papers  (2023-12-29T03:31:28Z)
• Causal Disentanglement Hidden Markov Model for Fault Diagnosis [55.90917958154425]
  We propose a Causal Disentanglement Hidden Markov model (CDHM) to learn the causality in the bearing fault mechanism. Specifically, we make full use of the time-series data and progressively disentangle the vibration signal into fault-relevant and fault-irrelevant factors. To expand the scope of the application, we adopt unsupervised domain adaptation to transfer the learned disentangled representations to other working environments.
  arXiv  Detail & Related papers  (2023-08-06T05:58:45Z)
• Zero-Shot Motor Health Monitoring by Blind Domain Transition [17.664784126708742]
  We propose a zero-shot bearing fault detection method that can detect any fault on a new (target) machine regardless of the working conditions, sensor parameters, or fault characteristics. Experimental results demonstrate that this novel approach can accurately detect any bearing fault achieving an average recall rate of around 89% and 95% on two target machines regardless of its type, severity, and location.
  arXiv  Detail & Related papers  (2022-12-12T18:36:02Z)
• Data-driven design of fault diagnosis for three-phase PWM rectifier using random forests technique with transient synthetic features [2.382536770336505]
  A three-phase pulse-width modulation (PWM) can usually maintain operation when opencircuit faults occur in insulated-gate bipolar transistors (IGBTs) Data-driven online fault diagnosis method is proposed to locate the open-circuit faults of IGBTs timely effectively.
  arXiv  Detail & Related papers  (2022-11-02T05:48:30Z)
• Fault diagnosis for open-circuit faults in NPC inverter based on knowledge-driven and data-driven approaches [2.22076315914821]
  The open-circuit faults diagnosis and location issue of the neutral-point-clamped (NPC) inverters are analysed. A novel fault diagnosis approach based on knowledge driven and data driven was presented for the open-circuit faults in insulated-gate bipolar transistors (IGBTs) of NPC inverter.
  arXiv  Detail & Related papers  (2022-10-31T04:33:53Z)
• Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection [122.4894940892536]
  We present a novel self-supervised masked convolutional transformer block (SSMCTB) that comprises the reconstruction-based functionality at a core architectural level. In this work, we extend our previous self-supervised predictive convolutional attentive block (SSPCAB) with a 3D masked convolutional layer, a transformer for channel-wise attention, as well as a novel self-supervised objective based on Huber loss.
  arXiv  Detail & Related papers  (2022-09-25T04:56:10Z)
• On-board Fault Diagnosis of a Laboratory Mini SR-30 Gas Turbine Engine [54.650189434544146]
  A data-driven fault diagnosis and isolation scheme is explicitly developed for failure in the fuel supply system and sensor measurements. A model is trained using machine learning classifiers to detect a given set of fault scenarios in real-time on which it is trained. Several simulation studies were carried out to demonstrate and illustrate the proposed fault diagnosis scheme's advantages, capabilities, and performance.
  arXiv  Detail & Related papers  (2021-10-17T13:42:37Z)
• No Need to Know Physics: Resilience of Process-based Model-free Anomaly Detection for Industrial Control Systems [95.54151664013011]
  We present a novel framework to generate adversarial spoofing signals that violate physical properties of the system. We analyze four anomaly detectors published at top security conferences.
  arXiv  Detail & Related papers  (2020-12-07T11:02:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.