Predictive Maintenance Optimization for Industrial Machinery using Hybrid Deep Learning and Vibration Analysis

Abstract

This study explores predictive maintenance optimization for industrial equipment through the use of a hybrid methodology combining deep learning methods with conventional vibration analysis. The research responds to the crucial requirement to reduce downtime and maintenance expenditures in industry by creating a prediction model that clearly predicts impending equipment failures. We introduce a hybrid model that is comprised of Convolutional Neural Networks (CNNs) for extraction of features from raw vibration signals and Long Short-Term Memory (LSTM) networks for analyzing time-series data and predicting failures. The model is trained and validated with a thorough dataset of vibration signals gathered from different industrial equipment under different operating conditions. The findings indicate that the suggested hybrid strategy performs better than conventional methods according to prediction accuracy, lead time, and overall maintenance cost savings. The research shows the capability of vibration analysis with deep learning for proactive scheduling of maintenance and enhanced operation efficiency in industrial settings.