Dislocated time sequences – deep neural network for broken bearing diagnosis

Abstract

Abstract One of the serious components to be maintained in rotating machinery including induction motors is bearings. Broken bearing diagnosis is a vital activity in maintaining electrical machines. Researchers have explored the use of machine learning for diagnostic purposes, both shallow and deep architecture. This study experimentally explores the progress of dislocated time sequences–deep neural network (DTS–DNN) used to improve multi-class broken bearing diagnosis by using public data from Case Western Reserve University. Deep architectures can be utilized with the purpose of simplifying or avoiding any traditional feature extraction process. DNN is utilized for avoiding the pooling operation in Convolution neural network that could remove important information. The obtained results were compared with the present techniques. The examination resulted in 99.42% average accuracy which is higher than the present techniques.