Chi-Ching Hsu

@article{HSU2025111199,
            title = {Explainable AI guided unsupervised fault diagnostics for high-voltage circuit breakers},
            journal = {Reliability Engineering & System Safety},
            volume = {263},
            pages = {111199},
            year = {2025},
            issn = {0951-8320},
            doi = {https://doi.org/10.1016/j.ress.2025.111199},
            url = {https://www.sciencedirect.com/science/article/pii/S0951832025004004},
            author = {Chi-Ching Hsu and Gaëtan Frusque and Florent Forest and Felipe Macedo and Christian M. Franck and Olga Fink},
            keywords = {Condition monitoring, High-voltage circuit breaker, Fault detection, Fault segmentation, Fault diagnostics, Unsupervised clustering, Vibration signal, Convolutional autoencoder, Explainable artificial intelligence (XAI)},
            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.}
            }

@ARTICLE{hsu2025continuous,
            author={Hsu, Chi-Ching and Frusque, Gaetan and Fink, Olga and Franck, Christian M.},
            journal={IEEE Access}, 
            title={Continuous Health State Monitoring of High-Voltage Circuit Breakers}, 
            year={2025},
            volume={13},
            number={},
            pages={89819-89830},
            keywords={Circuit breakers;Vibrations;Monitoring;Coils;Sensors;Degradation;Temperature sensors;Feature extraction;Vibration measurement;Motors;Condition monitoring;high-voltage circuit breaker;run-to-failure experiment;vibration signals},
            doi={10.1109/ACCESS.2025.3570535}}
          

@INPROCEEDINGS{zoltan2025iot,
        author={Marcsek, Zoltán and Gfrörer, Tino and Polonelli, Tommaso and Hsu, Chi-Ching and Magno, Michele and Franck, Christian M.},
        booktitle={2025 10th International Workshop on Advances in Sensors and Interfaces (IWASI)}, 
        title={An IoT Sensor Platform for Predictive Maintenance of High Voltage Circuit Breakers}, 
        year={2025},
        volume={},
        number={},
        pages={1-6},
        keywords={Coils;Vibrations;Substations;Circuit breakers;Current measurement;High-voltage techniques;Integrated circuit reliability;Wireless fidelity;Testing;Predictive maintenance},
        doi={10.1109/IWASI66786.2025.11121959}}      
        

@inproceedings{hsu2023comparison,
        title={A Comparison of Residual-based Methods on Fault Detection},
        author={Hsu, Chi-Ching and Frusque, Ga{\"e}tan Michel and Fink, Olga},
        booktitle={Proceedings of the Annual Conference of the PHM Society 2023},
        volume={15},
        number={1},
        year={2023},
        organization={PHM Society}
      }
        

@ARTICLE{10045627,
        author={Franck, Christian Michael and Hsu, Chi-Ching and Xiao, Yu and Bleuler, Pascal and Frusque, Gaetan and Muratovic, Mahir and Polonelli, Tommaso},
        journal={IEEE Power and Energy Magazine}, 
        title={Transmission and Distribution Equipment: Providing Intelligent Maintenance}, 
        year={2023},
        volume={21},
        number={2},
        pages={18-29},
        keywords={Maintenance engineering;Voltage control;Bidirectional power flow;Asset management;Power grids;Intelligent control;Power system planning;Renewable energy sources},
        doi={10.1109/MPE.2022.3230968}}            
        

@INPROCEEDINGS{9945354,
        author={Hsu, Chi-Ching and Frusque, Gaetan and Muratovic, Mahir and Franck, Christian M. and Fink, Olga},
        booktitle={2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)}, 
        title={Vacuum Circuit Breaker Closing Time Key Moments Detection via Vibration Monitoring: A Run-to-Failure Study}, 
        year={2022},
        volume={},
        number={},
        pages={254-260},
        keywords={Vibrations;Vacuum systems;Circuit breakers;Power transmission;Life testing;Prediction algorithms;Time measurement;condition monitoring;vacuum circuit breakers;short-time energy;vibration signals;run-to-failure dataset},
        doi={10.1109/SMC53654.2022.9945354}}