Optimal feature selection on Serial Cascaded deep learning for predictive maintenance system in automotive industry with fused optimization algorithm

Machines can make an appropriate operating and maintenance choice when defects are accurately and promptly predicted. Researchers choose data-driven predictive maintenance techniques for making the prediction more quickly and economically than alternative methods. To maintain secure and dependable manufacturing operations, the production equipment needs predictive maintenance. It is crucial to have an efficient predictive maintenance model for preventing unexpected shutdown by defects during production. The majority of related research focuses on early fault warnings but ignores how different issues differ in severity. Making the right maintenance policy is extremely important for a manufacturing organization because maintenance affects employee safety, economy, reliability, and availability. To solve these difficulties in the automotive sector, an efficient predictive maintenance strategy with deep structured architecture is offered to predict faults in the machines and increase the lifespan of the equipment. Firstly, the required data is collected from external sources for the predictive maintenance system. The garnered data is given to the pre-processing section and the transformation stage. Secondly, the transformed data is subjected to the autoencoder for selecting the optimal encoded vectors with the Henry Gas Solubility Search and Rescue Optimization (HGSSRO). The selected features are given to deep hybrid learning with Serial Cascaded Deep Learning (SCDL) to predict the occurrence of faults. Here, the autoencoder obtains the encoded vectors from the optimally selected features. Then, the optimal encoded vectors are subjected to the LSTM to acquire the features. The extracted features are given to DNN for the final prediction of failures. In this hybrid SCDL, the variables are optimized by the same HGSSRO to improve the prediction performance. The efficacy of the developed deep learning-aided predictive system is validated ed by comparing the conventional prediction models.