Hybrid Multi-Scale Convolutional Long Short-Term Memory Network for Remaining Useful Life Prediction and Offset Analysis

Abstract

Prognostic and health management (PHM) has become increasingly popular due to the requirement of improved maintenance techniques in the industry. Remaining useful life (RUL) estimation is an important parameter through which PHM can be utilized to implement timely and cost-effective maintenance. Due to recent advancements in sensor-based and other Industry 4.0 related technologies, data-driven methods for RUL estimation have become more prevalent and effective. In this paper, a novel data-driven method for sensor-based RUL estimation using a combination of multi-scale convolutional neural network (MS-CNN) and long short-term memory (LSTM) is proposed. The proposed hybrid multi-scale convolutional LSTM (HMCL) model is capable of extracting both spatial features of various scales and temporal features from the input data to provide accurate RUL predictions. L2 regularization and dropout techniques are used to reduce overfitting. The performance of the proposed model is evaluated using the C-MAPSS dataset. It achieves excellent performance as compared to other state-of-the-art methods making it a promising approach for sensor-based RUL prediction. Additionally, to discern the cause for occurrence of offsets, i.e., deviations in the model’s predictions with the true RUL value, an offset analysis is carried out. Through the analysis, an estimate on the location and cause of offsets is established and based on the sensory input data, offsets are identified using an SVM classification model. Despite being a simple classification model, it is able to achieve a decent performance in classifying the offsets.