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

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.