A probabilistic estimation of remaining useful life from censored time-to-event data

• URL: http://arxiv.org/abs/2405.01614v1
• Date: Thu, 2 May 2024 16:17:29 GMT
• Title: A probabilistic estimation of remaining useful life from censored time-to-event data
• Authors: Christian Marius Lillelund, Fernando Pannullo, Morten Opprud Jakobsen, Manuel Morante, Christian Fischer Pedersen,
• Abstract summary: The remaining useful life (RUL) of ball bearings plays an important role in predictive maintenance. We propose a probabilistic estimation of RUL using survival analysis that supports censored data. We demonstrate our approach in the XJTU-SY dataset using cross-validation and find that Random Survival Forests consistently outperforms both non-neural networks and neural networks in terms of the mean absolute error (MAE)
• Score: 39.58317527488534
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Predicting the remaining useful life (RUL) of ball bearings plays an important role in predictive maintenance. A common definition of the RUL is the time until a bearing is no longer functional, which we denote as an event, and many data-driven methods have been proposed to predict the RUL. However, few studies have addressed the problem of censored data, where this event of interest is not observed, and simply ignoring these observations can lead to an overestimation of the failure risk. In this paper, we propose a probabilistic estimation of RUL using survival analysis that supports censored data. First, we analyze sensor readings from ball bearings in the frequency domain and annotate when a bearing starts to deteriorate by calculating the Kullback-Leibler (KL) divergence between the probability density function (PDF) of the current process and a reference PDF. Second, we train several survival models on the annotated bearing dataset, capable of predicting the RUL over a finite time horizon using the survival function. This function is guaranteed to be strictly monotonically decreasing and is an intuitive estimation of the remaining lifetime. We demonstrate our approach in the XJTU-SY dataset using cross-validation and find that Random Survival Forests consistently outperforms both non-neural networks and neural networks in terms of the mean absolute error (MAE). Our work encourages the inclusion of censored data in predictive maintenance models and highlights the unique advantages that survival analysis offers when it comes to probabilistic RUL estimation and early fault detection.

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