A Comprehensive Approach to Asset Degradation Modeling via Sensory Data Fusion for Remaining Useful Life Prediction

Abstract

For effective management of assets, accurate forecasting for system failures is necessary. Sensory data fusion is a viable option to predict Remaining Useful Life (RUL) in assets by combining multiple data sources for improved prediction capabilities. This research paper aims at predicting RUL integrating various sensory data streams. Using Artificial Neural Networks (ANN), this research aims at synthesizing, learning from, and fusing information emanating from different sensors leading to accurate RUL estimations required for proactive maintenance strategies. The methodology in this study involves the use of ANN architectures for processing multivariate time-series data collected from sensors. By iterative training, the ANN captures complex relationships within the data allowing the integration of different information sources thus aiding in RUL predictions. Through the synthesis of sensory data by the ANN model, promising results have been achieved in predicting RUL. The model effectively learns from multiple sources demonstrating enhanced accuracy in estimating remaining operational cycles before asset failure.