On Designing Features for Condition Monitoring of Rotating Machines

• URL: http://arxiv.org/abs/2402.09957v1
• Date: Thu, 15 Feb 2024 14:08:08 GMT
• Title: On Designing Features for Condition Monitoring of Rotating Machines
• Authors: Seetaram Maurya and Nishchal K. Verma
• Abstract summary: Various methods for designing input features have been proposed for fault recognition in rotating machines. This article proposes a novel algorithm to design input features that unifies the feature extraction process for different time-series sensor data.
• Score: 7.830376406370754
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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