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Diagnosis of faulty gears by modified AlexNet and improved grasshopper optimization algorithm (IGOA)

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Abstract

Gearbox is a significant part for the transmission of vehicles and various mechanical devices and is being utilized broadly in the industries despite of its failure prone nature. Therefore, the need arises for diagnosing the faults present in a gearbox and to rectify the faulty gear. In this paper, deep learning method is utilized for the diagnosis of faulty gears and employs the modified AlexNet for the classification of various gear signals. The hidden units present in the bidirectional LSTM (long short term memory) layer of the AlexNet is selected by proposing an improved grasshopper optimization algorithm (IGOA). After the process of classification, performance evaluation is carried out for various performance measures. It is found that proposed method achieves accuracy of 2.4 %, specificity of −0.3 %, sensitivity of 1.01 %, recall of 0.97 %, precision of 0.59 %. Based on the results obtained it is found that proposed algorithm is more efficient when compared to existing algorithm.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vijayawada, AP, 522502, India

    Rohit Ghulanavar, Kiran Kumar Dama & A. Jagadeesh

  2. Department of Mechanical Engineering, KIT’s College of Engineering, Gokul Shirgaon, Kolhapur, MS, 416234, India

    Rohit Ghulanavar

Authors
  1. Rohit Ghulanavar
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  2. Kiran Kumar Dama
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  3. A. Jagadeesh
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Corresponding author

Correspondence to Rohit Ghulanavar.

Additional information

Rohit Ghulanavar is studying Doctor of Philosophy in Mechanical Engineering Department of Koneru Lakshmaiah Education Foundation (KLEF) Green Fields, Vaddeswaram, Andhra Pradesh, INDIA, and also working as Assistant Professor in Department of Mechanical Engineering at KIT’s College of Engineering (Autonomous), Kolhapur. He has completed his M.E. in Machine Design from Dr. J. J. Magdum College of Engineering, Jaysingpur Maharashtra, INDIA having seven years of teaching experience in undergraduate engineering education. He is a life time member of Tribology Society of India (TSI) and IAENG, member of the Institutions of Engineers (India). He has published 8 papers in national and international journals and one book in Lambert Academic Publication.

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Ghulanavar, R., Dama, K.K. & Jagadeesh, A. Diagnosis of faulty gears by modified AlexNet and improved grasshopper optimization algorithm (IGOA). J Mech Sci Technol 34, 4173–4182 (2020). https://doi.org/10.1007/s12206-020-0909-6

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  • DOI: https://doi.org/10.1007/s12206-020-0909-6

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