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- W4387444969 abstract "Smart condition-based maintenance is an essence of Industry 4.0 and is important for assuring independent and long-term operation of devices driven by Variable Frequency Drives (VFD). Therefore, detection of early-stage signs of fatigue and failures in VFDs is important. This usually involves analysis of input data, extracted from VFDs and external sensors, using temperature, vibration, and acoustic signals, among others. Nevertheless, due to the complexity of operation of VFDs, there is a constant need to define more unified approaches for detecting anomalies in their operation, so that higher performance and reliability can be achieved. In this paper, we present a comparative study on machine-learning based solutions, utilizing temperature signals, fetched from both real and simulated operation of VFDs in wind turbine and virtual drive, respectively. This involved Hidden Markov Models, Deep Autoencoders, and Incremental/Kernel Principal Component Analysis. We also introduced a novel anomaly metric, so that all the approaches could be efficiently benchmarked in terms of sensitivity to anomalies of distinct types. Our study confirms the feasibility of the methods in both real and simulated data from VFDs. This proves that machine-learning-based methodologies can be successfully applied for condition monitoring, via anomaly detection, in VFDs." @default.
- W4387444969 created "2023-10-10" @default.
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- W4387444969 date "2023-08-28" @default.
- W4387444969 modified "2023-10-11" @default.
- W4387444969 title "Performance of Machine-Learning-Based Algorithms for Anomaly Detection in Variable Frequency Drives Using Temperature Signals" @default.
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- W4387444969 doi "https://doi.org/10.1109/sdemped54949.2023.10271477" @default.
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