Remaining Useful Life Estimation from Event Data

Abstract—Prognostics aims to predict the degradation of equipment by estimating their remaining useful life (RUL) and/or the failure probability within a specific time horizon. The high demand of equipment prognostics in the industry have propelled researchers to develop robust and efficient prognostics techniques. Among data driven techniques for prognostics, machine learning and deep learning (DL) based techniques, particularly Recurrent Neural Networks (RNNs) have gained significant attention due to their ability of effectively representing the degradation progress by employing dynamic temporal behaviors. RNNs are well known for handling sequential data, especially continuous time series sequential data where the data follows certain pattern. Such data is usually obtained from sensors attached to the equipment. However, in many scenarios sensor data is not readily available and often very tedious to acquire. Conversely, event data is more common and can easily be obtained from the error logs saved by the equipment and transmitted to a backend for further processing. Nevertheless, performing prognostics using event data is substantially more difficult than that of the sensor data due to the unique nature of event data. Though event data is sequential, it differs from other seminal sequential data such as time series and natural language in the following manner, i) unlike time series data, events may appear at any time, i.e., the appearance of events lacks periodicity; ii) unlike natural languages, event data do not follow any specific linguistic rule. Additionally, there may be a significant variability in the event types appearing within the same sequence. Therefore, this paper proposes an RUL estimation framework to effectively handle the intricate and novel event data. The proposed framework takes discrete events generated by an equipment (e.g., type, time, etc.) as input, and generates for each new event an estimate of the remaining operating cycles in the life of a given component. To evaluate the efficacy of our proposed method, we conduct extensive experiments using benchmark datasets such as the CMAPSS data after converting the time-series data in these datasets to sequential event data. The event data conversion is carried out by careful exploration and application of appropriate transformation techniques to the time series. To the best of our knowledge this is the first time such event-based RUL estimation problem is introduced to the community. Furthermore, we propose several deep learning and machine learning based solution for the event-based RUL estimation problem. Our results suggest that the deep learning models, 1D-CNN, LSTM, and multi-head attention show similar RMSE, MAE and Score performance. Foreseeably, the XGBoost model achieve lower performance compared to the deep learning models since the XGBoost model fails to capture ordering information from the sequence of events.