Chi-Ching Hsu

Last Name

Hsu

First Name

Chi-Ching

ORCID

0000-0002-6919-8882

Organisational unit

03869 - Franck, Christian / Franck, Christian

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Publications 1 - 8 of 8

Explainable AI guided unsupervised fault diagnostics for high-voltage circuit breakers
Hsu, Chi-Ching; Frusque, Gaëtan; Forest, Florent; et al. (2025)
Reliability Engineering & System Safety
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.
Transmission and Distribution Equipment: Providing Intelligent Maintenance
Franck, Christian; Hsu, Chi-Ching; Xiao, Yu; et al. (2023)
IEEE Power and Energy Magazine
One of the cornerstones of a reliable transmission and distribution grid operation are fully functional components that can operate robustly and with a low outage rate under all specified operating conditions. Dependable maintenance strategies are thus indispensable and are applied by grid operators around the world. One of the present key challenges in most countries with a widely developed transmission and distribution (T&D) grid system are aging components that reach their anticipated end of life. Asset management faces the question if the lifetime of components could be prolonged, and the replacement could be delayed. For this, the health of the components needs to be assessed and is ideally continuously monitored. In addition to this, the currently ongoing transition of the entire energy system leads to a change and increase of the stress to the T&D equipment. The integration of new renewable energy sources on all voltage levels leads to bi‐directional power‐flows and increased variability. The higher demand for electric power not only increases the power flow levels on average, but in particular also the peak flows. The result of this changed and increased stress on the equipment is an accelerated component aging and the need for maintenance strategies to be adopted to this new situation. Recent developments in low‐cost and low‐power data acquisition technology and machine learning based algorithms, combined with rapidly increasing decentralized embedded computing power, offer the opportunity to develop improved and intelligent maintenance strategies based on continuous monitoring and real‐time health evaluation of the equipment. This paradigm changes the prospects in equipment maintenance, offering significantly reduced costs in asset management. The lifetime of properly dimensioned equipment is expected to be several decades, sometimes as much as 40 to 60 years. Most maintenance checks therefore only confirm the excellent condition of the component and would have not been necessary at this point in time. Thus, not only delaying the replacement, but also delaying a maintenance interval based on the health condition would be very welcome. Hence, the most commonly used maintenance concepts are changed from time‐based checks to a new and more intelligent approach, oriented towards a just‐in‐time service. This article aims to introduce the concepts of intelligent maintenance strategies, trends, and challenges of T&D equipment condition monitoring, as well as the automatic estimation of equipment health.
Vacuum Circuit Breaker Closing Time Key Moments Detection via Vibration Monitoring: A Run-to-Failure Study
Hsu, Chi-Ching; Frusque, Gaëtan; Muratovic, Mahir; et al. (2022)
2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
A Comparison of Residual-based Methods on Fault Detection
Hsu, Chi-Ching; Frusque, Gaetan; Fink, Olga (2023)
Proceedings of the Annual Conference of the PHM Society 2023
An important initial step in fault detection for complex indus trial systems is gaining an understanding of their health con dition. Subsequently, continuous monitoring of this health condition becomes crucial to observe its evolution, track changes over time, and isolate faults. As faults are typically rare occurrences, it is essential to perform this monitoring in an unsupervised manner. Various approaches have been proposed not only to detect faults in an unsupervised manner but also to distinguish between different potential fault types. In this study, we perform a comprehensive comparison be tween two residual-based approaches: autoencoders, and the input-output models that establish a mapping between oper ating conditions and sensor readings. We explore the sensor wise residuals and aggregated residuals for the entire sys tem in both methods. The performance evaluation focuses on three tasks: health indicator construction, fault detection, and health indicator interpretation. To perform the comparison, we utilize the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dynamical model, specifically a sub set of the turbofan engine dataset containing three different fault types. All models are trained exclusively on healthy data. Fault detection is achieved by applying a threshold that is determined based on the healthy condition. The detection results reveal that both models are capable of detecting faults with an average delay of around 20 cycles and maintain a low false positive rate. While the fault detection performance is similar for both models, the input-output model provides better interpretability regarding potential fault types and the possible faulty components.
Interpretable Detection of Partial Discharge in Power Lines with Deep Learning
Michau, Gabriel; Hsu, Chi-Ching; Fink, Olga (2021)
Sensors
Partial discharge (PD) is a common indication of faults in power systems, such as generators and cables. These PDs can eventually result in costly repairs and substantial power outages. PD detection traditionally relies on hand-crafted features and domain expertise to identify very specific pulses in the electrical current, and the performance declines in the presence of noise or of superposed pulses. In this paper, we propose a novel end-to-end framework based on convolutional neural networks. The framework has two contributions: First, it does not require any feature extraction and enables robust PD detection. Second, we devise the pulse activation map. It provides interpretability of the results for the domain experts with the identification of the pulses that led to the detection of the PDs. The performance is evaluated on a public dataset for the detection of damaged power lines. An ablation study demonstrates the benefits of each part of the proposed framework.
An Edge-Sensing Platform for Predictive Maintenance Application
Xiao, Yu; Gfrörer, Tino; Bleuler, Pascal; et al. (2024)
Fully functional components are the foundation of any technical system, such as the electric power transmission and distribution grid. To transition from today’s time-based maintenance of these components to an intelligent predictive maintenance strategy, the health condition of all components needs to be known continuously at any instant in time. For this, sensors and monitoring systems have to be pervasively deployed, and IoT-based solutions can offer cost- and energy-effective, but still multi-functional, solutions. The present contribution compares a cost effective sensor and IoT-based data acquisition system to a high-performance sensor and an oscilloscope on the example of measuring the vibration signal on a low-voltage molded case circuit breaker. It is shown that the sensor bandwidth and acquisition system sampling rate and resolution are sufficient, for example, to detect deviations on the closing time if an undervoltage is applied to the actuation system, based on the vibration signal alone. This measurement system is versatile enough to also measure with other sensors and to derive further system parameters, also health indicators, from the recordings.
An IoT Sensor Platform for Predictive Maintenance of High Voltage Circuit Breakers
Marcsek, Zoltán; Gfrörer, Tino; Polonelli, Tommaso; et al. (2025)
2025 10th International Workshop on Advances in Sensors and Interfaces (IWASI)
This paper presents the design, implementation, and validation of an IoT-based sensor platform for predictive maintenance of high-voltage circuit breakers (HVCBs), essential components in power transmission systems. Mechanical failures account for more than half of major HVCB failures, emphasizing the need for reliable, long-term condition monitoring. The proposed platform is specifically developed for real-world deployment and enables non-intrusive, synchronized measurement of vibration and coil current signals during switching events, sampled at 100 ksps and 10 ksps, respectively. Triggering of the measurement is achieved via hardware detection of coil current with a response time below 115 µs. Data is recorded directly to a microSD card, with optional USB streaming for testing and Wi-Fi transfer to a gateway connected to cloud storage. The system supports virtually unlimited recording duration and rearms for subsequent events in under one second, facilitating continuous operation. The platform was validated through extensive laboratory testing and real-world deployment at a Swiss Federal Railways (SBB) substation with a HVCB operating at 132 kV. Among the two, more than 500 circuit breaker operations were recorded without failure, demonstrating long-term reliability and robustness in high-voltage environments. By enabling long-term monitoring in operational substations, it addresses a critical gap in both research and industrial practice, and contributes a practical solution for improving grid reliability and reducing maintenance costs.
Continuous Health State Monitoring of High-Voltage Circuit Breakers
Hsu, Chi-Ching; Frusque, Gaëtan; Fink, Olga; et al. (2025)
IEEE Access
Circuit breakers (CBs) are renowned for their high reliability and long lifespan. As a result, many CBs installed decades ago are now approaching their predefined end of service life. However, this predefined service life does not always reflect the actual condition, as some may still be far from reaching their true end-of-life, depending on their operating conditions and history. To assess their true lifetime, continuous condition monitoring is essential. While previous studies have effectively demonstrated the ability to distinguish between different CB fault types, the evolution of CB degradation remains unclear when faults are artificially introduced. This paper investigates the health condition of two high-voltage CBs continuously through run-to-failure experiments. A comprehensive dataset was collected for all opening and closing operations with various sensors such as vibration, coil current, and travel curve and has been made publicly available for further analysis. Furthermore, features were derived from the sensor data, revealing distinct degradation trajectories over time that can be used to monitor the condition of the CBs. This paper highlights the degradation patterns of these features, some of which are well-suited for continuous condition monitoring due to their gradual changing trend over time that likely correlates with the true degradation condition, while others are less useful as they show abrupt changes only before or at failure. By leveraging these features, we can progress beyond the focus of previous research using only fault diagnosis towards fault prognosis. This shift opens the possibility for accurate prediction of the CB condition over time, enabling more effective maintenance strategies.

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Chi-Ching Hsu

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