Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks

International Journal of Prognostics and Health Management

卷:9

Narendhar Gugulothu¹  Vishnu TV¹  Puneet Agarwal¹  Gautam Shroff¹  Pankaj Malhotra¹  Lovekesh Vig¹ 

[1] TCS Research, New Delhi, India;

关键词: condition monitoring;    prognostics;    deep learning;    time series embeddings;    recurrent neural networks;   

DOI  :  doi:10.36001/ijphm.2018.v9i1.2689

来源: DOAJ

【 摘 要 】

We consider the problem of estimating the remaining useful life (RUL) of a system or a machine from sensor data. Many approaches for RUL estimation based on sensor data make assumptions about how machines degrade. Additionally, sensor data from machines is noisy and often suffers from missing values in many practical settings. We propose Embed-RUL: a novel approach for RUL estimation from sensor data that does not rely on any degradation-trend assumptions, is robust to noise, and handles missing values. Embed-RUL utilizes a sequence-to-sequence model based on Recurrent Neural Networks (RNNs) to generate embeddings for multivariate time series subsequences. The embeddings for normal and degraded machines tend to be different, and are therefore found to be useful for RUL estimation. We show that the embeddings capture the overall pattern in the time series while filtering out the noise, so that the embeddings of two machines with similar operational behavior are close to each other, even when their sensor readings have significant and varying levels of noise content. We perform experiments on publicly available turbofan engine dataset and a proprietary real-world dataset, and demonstrate that Embed-RUL outperforms the previously reported state-of-the-art (Malhotra, TV, et al., 2016) on several metrics.

【 授权许可】

Unknown