Two Birds with One Network: Unifying Failure Event Prediction and Time-to-failure Modeling

Abstract

One of the key challenges in predictive maintenance is to predict the impending downtime of an equipment with a reasonable prediction horizon so that countermeasures can be put in place. Classically, this problem has been posed in two different ways which are typically solved independently: (1) Remaining useful life (RUL) estimation as a long-term prediction task to estimate how much time is left in the useful life of the equipment and (2) Failure prediction (FP) as a short-term prediction task to assess the probability of a failure within a prespecified time window. As these two tasks are related, performing them separately is sub-optimal and might results in inconsistent predictions for the same equipment. In order to alleviate these issues, we propose two methods: Deep Weibull model (DW-RNN) and multi-task learning (MTL-RNN). DW-RNN is able to learn the underlying failure dynamics by fitting Weibull distribution parameters using a deep neural network, learned with a survival likelihood, without training directly on each task. While DW-RNN makes an explicit assumption on the data distribution, MTL-RNN exploits the implicit relationship between the longterm RUL and short-term FP tasks to learn the underlying distribution. Additionally, both our methods can leverage the non-failed equipment data for RUL estimation. We demonstrate that our methods consistently outperform baseline RUL methods that can be used for FP while producing consistent results for RUL and FP. We also show that our methods perform at par with baselines trained on the objectives optimized for either of the two tasks.

Remaining Useful Life Estimation Using Functional Data Analysis

“…In this section, we apply functional MLP ('FMLP') to conduct RUL estimation task for a widely-used benchmark data set called NASA C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) data [28]. We compare the performance of functional MLP with a variety of state-of-the-art deep learning approaches, including the Convolutional Neural Network model ('CNN') in [10], the Deep Weibull network ('DW-RNN') and the multi-task learning network ('MTL-RNN') in [6], the Long Short-Term Memory method ('LSTM') [3], and the bootstrapping based Long Short-Term Memory method ('LSTMBS') [11]. As shown by the experimental results, the proposed functional MLP approach significantly outperforms all these alternative methods.…”

Developing Deep Survival Model for Remaining Useful Life Estimation Based on Convolutional and Long Short-Term Memory Neural Networks

“…Therefore, it is better to learn temporal features from the slow inherently long-term degradation process by combing those two structures. Recent paper proposed a deep neural network structure using both LSTM and CNN which can be combined in a serial or parallel manner to improve the accuracy of the RUL prediction of the equipment [34,35].…”

“…If the variance is large, it is hard to have confidence on the predicted result. The RUL prediction problem is also similar to the survival analysis which is commonly used to model time-to-death events in the healthcare domain [34]. For example, the model predicts the failure will happen in 8 days with 80% probability is much better than predict 10 days until the failure.…”

“…Martinsson proposed the Weibull time-to-event recurrent neural network, which is a simple framework for time series prediction of the time to the next event applicable [36]. Aggarwal et al used the Weibull distribution assumption on the time-to-failure event with a linear hazard rate corresponding to the linear degradation model that most of the literature makes [34].…”

Assessment of Early Stopping through Statistical Health Prognostic Models for Empirical RUL Estimation in Wind Turbine Main Bearing Failure Monitoring

“…In contrast to Ranganath et al [19] deep exponential families model, this work will limit itself to Weibull distribution's only. Yang et al [23] and Aggarwal et al [24] developed similar Weibull based NNs and RNNs, respectively, but focus amongst others on different disciplines than wind turbine monitoring.…”