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Fault feature selection for the identification of compound gear-bearing faults using firefly algorithm

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Abstract

The occurrence of compound faults in real-time conditions leads to the early failure of components. However, identifying compound faults in a rotor system is more complex because extracting the fault information from the vibration signals is challenging. Hence, it is inevitable to devise a reliable strategy for predicting compound faults in a rotor system to ensure its life. This work proposes a novel feature selection method using the firefly algorithm (FA) to identify compound gear-bearing faults. The statistical features are extracted from the time domain vibration signals. The firefly algorithm is employed to select the features that have the most pertinent information about the faults. The classification potential of the selected features is tested with an optimized feed forward fully connected neural network (FFFCNN) architecture. Further, the performance of the proposed approach is compared with the genetic algorithm, relief-based feature selection techniques and without the feature selection approach. The FA-based feature selection combined with the FFFCNN achieves a prediction accuracy of 94.86% in identifying the compound gear-bearing faults.

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Engineering College, Kovilpatti, 628502, Tamilnadu, India

    Andrews Athisayam & Manisekar Kondal

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  1. Andrews Athisayam
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Contributions

Algorithm development, experiment preparation, data collection, and analysis were performed by Andrews Athisayam. Manisekar Kondal provided feedback on the concept and overseas the entire process. The first draft of the manuscript was written by Andrews Athisayam and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Andrews Athisayam.

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Athisayam, A., Kondal, M. Fault feature selection for the identification of compound gear-bearing faults using firefly algorithm. Int J Adv Manuf Technol 125, 1777–1788 (2023). https://doi.org/10.1007/s00170-023-10846-y

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