Failure Log Analytics for Reducing Electrical Machine Downtime using Deep Learning

Abstract

Electrical machine downtime reduces productivity across various operation times that are addressed using stored data logs in the controller. Analyzing such logs is useful in preventing/ reducing machine downtime through precise controller options. This article proposes a Downtime Reduction-focused Log Analytical Model (DR-LAM) for improving the machine operation time by reducing operation failures. In this model, deep learning is employed for differentiating the production-less electrical cycles in correlation with the previous output. This differentiation is conditional using run-time failures and failed operation cycles. Therefore the logs are analyzed based on the above differentiations for precise problem identification. The training for the deep learning network is provided using previous differentiated cycle logs improving the detection ratio.