On Designing Features for Condition Monitoring of Rotating Machines
<|reference_start|>On Designing Features for Condition Monitoring of Rotating Machines: Various methods for designing input features have been proposed for fault recognition in rotating machines using one-dimensional raw sensor data. The available methods are complex, rely on empirical approaches, and may differ depending on the condition monitoring data used. Therefore, this article proposes a novel algorithm to design input features that unifies the feature extraction process for different time-series sensor data. This new insight for designing/extracting input features is obtained through the lens of histogram theory. The proposed algorithm extracts discriminative input features, which are suitable for a simple classifier to deep neural network-based classifiers. The designed input features are given as input to the classifier with end-to-end training in a single framework for machine conditions recognition. The proposed scheme has been validated through three real-time datasets: a) acoustic dataset, b) CWRU vibration dataset, and c) IMS vibration dataset. The real-time results and comparative study show the effectiveness of the proposed scheme for the prediction of the machine's health states.<|reference_end|>
@article{maurya2024on,
    title={On Designing Features for Condition Monitoring of Rotating Machines},
    author={Seetaram Maurya and Nishchal K. Verma},
    journal={arXiv preprint arXiv:2402.09957},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.09957},
    primaryClass={cs.LG eess.SP}
}