Bearing Error Diagnosis Using Deep Learning and Convolution Neural Network

Abstract

Fault diagnostics and prognostics are essential issues. Industrial plants will be under a huge amount of pressure that maintains unpredictable interruption, system failures, and safety issues to a minimum, that necessitates identifying and eliminating potential issues as quickly as possible. Intelligent problem diagnosis is a promising technique because of its capacity to handle gathered signals quickly and effectively while also offering reliable diagnosis findings. Numerous authors have validated deep learning and machine learning approaches for identifying bearings failures, the findings have mostly been confined to tiny train and test datasets, with the input data modified to achieve high accuracy. In this article, original data of accelerometer sensor was loaded into unique periodic sequencing prediction algorithm that develops an edge fault diagnosis technique. We utilize identical frequency patterns as inputs to an innovative deep neural Long-Short-Term-Memory, Recurrent Neural Network to diagnosis bearings insufficiency at excellent accuracy inside the least time (CRNN). Without the use of database adjustment, the technique would acquire the maximum level of competence in the industry. The fault diagnostic method’s efficacy and applicability are demonstrated by comparing the findings to those of other intelligent fault detection systems using two widely known benchmark real vibration datasets.