Diagnosis of faulty gears by modified AlexNet and improved grasshopper optimization algorithm (IGOA)
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DOI 10.1007/s12206-020-0909-6
Journal of Mechanical Science and Technology 34 (10) 2020 Original Article DOI 10.1007/s12206-020-0909-6 Keywords: · AlexNet · Bidirectional LSTM layer · Deep learning · Faulty gear · Gearbox · Improved grasshopper optimization algorithm (IGOA)
Correspondence to: Rohit Ghulanavar [email protected]
Citation: Ghulanavar, R., Dama, K. K., Jagadeesh, A. (2020). Diagnosis of faulty gears by modified AlexNet and improved grasshopper optimization algorithm (IGOA). Journal of Mechanical Science and Technology 34 (10) (2020) ?~?. http://doi.org/10.1007/s12206-020-0909-6
Received April 22nd, 2020 Revised
Diagnosis of faulty gears by modified AlexNet and improved grasshopper optimization algorithm (IGOA) Rohit Ghulanavar1,2, Kiran Kumar Dama1 and A. Jagadeesh1 1
Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vijayawada 522502, 2 AP, India, Department of Mechanical Engineering, KIT’s College of Engineering, Gokul Shirgaon, Kolhapur 416234 MS., India
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.
June 25th, 2020
Accepted July 29th, 2020 † Recommended by Editor Chongdu Cho
© The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2020
1. Introduction The gear acts as a significant part in a machine or a mechanical system. A simple gear assembly consists of at least two gears which rotates and mesh with each other in order to transmit power or torque [1]. Every gear will have tooth like structures termed as gear teeth and the process in which the teeth present in two gears are made contact with each other by rotation is known as gear meshing. Gears are not only utilized in the transmission of power and but also helpful in fluid propulsion (i.e.) in case of the gear pump. Normally a gear is made of a component known as pinion which is the smaller sized wheel. Depending on the number of teeth present in a gear, the ratio of the angular velocities known as gear ratio is determined among the input and output of a gear. If more than two gears are made to function in a sequ
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