Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder

Abstract

Many approaches for estimation of Remaining Useful Life (RUL) of a machine, using its operational sensor data, make assumptions about how a system degrades or a fault evolves, e.g., exponential degradation. However, in many domains degradation may not follow a pattern. We propose a Long Short Term Memory based Encoder-Decoder (LSTM-ED) scheme to obtain an unsupervised health index (HI) for a system using multi-sensor time-series data. LSTM-ED is trained to reconstruct the time-series corresponding to healthy state of a system. The reconstruction error is used to compute HI which is then used for RUL estimation. We evaluate our approach on publicly available Turbofan Engine and Milling Machine datasets. We also present results on a real-world industry dataset from a pulverizer mill where we find significant correlation between LSTM-ED based HI and maintenance costs.

Presented at 1st ACM SIGKDD Workshop on Machine Learning for Prognostics and Health Management, San Francisco, CA, USA, 2016. 10 pages

References in corpus (3)

• Sequence to Sequence Learning with Neural Networks
• Recurrent Neural Network Regularization
• LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection

Cited by in corpus (8)

• Deep Learning for Anomaly Detection: A Survey
• Deep Learning and Its Applications to Machine Health Monitoring: A Survey
• TimeNet: Pre-trained deep recurrent neural network for time series classification
• Semi-supervised Learning with Deep Generative Models for Asset Failure Prediction
• A Neural Network-Evolutionary Computational Framework for Remaining Useful Life Estimation of Mechanical Systems
• Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities
• Two Birds with One Network: Unifying Failure Event Prediction and Time-to-failure Modeling
• Attention Sequence to Sequence Model for Machine Remaining Useful Life Prediction