AI – Driven Predictive Maintenance For Industrial Rotating Machinery Using Vibration Analysis

Authors: Dr. Biswajit Datta, Rahila Begum Gadi

Abstract: Industrial rotary machines experiencing unplanned outages owing to bearing and gear defects cost economies and safety greatly. In this paper, we introduce a hybrid deep learning algorithm for predictive maintenance through the combination of 1-Dimensional Convolutional Neural Net-works (1D-CNN) and Bi-directional Long Short-Term Memory (BiLSTM) networks in order to perform diagnosis as well as Remaining Useful Life (RUL) estimation using vibration data. The introduced algorithm uses multi-scale feature learning and temporal dependency modeling in order to detect degradation trends without the need for much feature engineering. Evaluation experiments conducted on the CWRU bearing dataset illustrate high classification accuracy even when the vibration data contains varying amounts of noise and low RUL prediction error. Com-parative experiments show the effectiveness of the introduced hybrid model when compared to other models such as SVM, Random Forest, CNN, and LSTM.

DOI: http://doi.org/10.5281/zenodo.23156475