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A deep learning approach for predicting critical events using event logs
Author(s) -
Huang Congfang,
Deep Akash,
Zhou Shiyu,
Veeramani Dharmaraj
Publication year - 2021
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2853
Subject(s) - computer science , event (particle physics) , data mining , artificial intelligence , machine learning , artificial neural network , hazard , data modeling , chemistry , physics , organic chemistry , quantum mechanics , database
Event logs, comprising data on the occurrence of different types of events and associated times, are commonly collected during the operation of modern industrial machines and systems. It is widely believed that the rich information embedded in event logs can be used to predict the occurrence of critical events. In this paper, we propose a recurrent neural network model using time‐to‐event data from event logs not only to predict the time of the occurrence of a target event of interest, but also to interpret, from the trained model, significant events leading to the target event. To improve the performance of our model, sampling techniques and methods dealing with the censored data are utilized. The proposed model is tested on both simulated data and real‐world datasets. Through these comparison studies, we show that the deep learning approach can often achieve better prediction performance than the traditional statistical model, such as, the Cox proportional hazard model. The real‐world case study also shows that the model interpretation algorithm proposed in this work can reveal the underlying physical relationship among events.