Abstract
This paper proposes a data-driven, condition-based maintenance framework (DCBM) for deteriorating equipment under the impact of varying environments and natural aging. The equipment’s degradation status is determined by a prognostic and health monitoring method. Generally, monitoring data and maintenance inspections are imperfect because of uncertainties in the equipment degradation process, which may prevent a reliable evaluation of a system’s deterioration. By utilizing a deep learning technique, we construct a new stacked autoencoder long short-term memory (SAE-LSTM) network-based multitask learning model to extract state features from the monitoring data, and then perform multistep forecasting to obtain performance degradation and failure probability information. The developed SAE-LSTM-based multitask learning achieves prognosis results close to the actual values, which indicates the excellent feature extraction capability of this model. As a result, we introduce this deep multitask learning model into the optimization of the maintenance process. Probabilistic forecasting is used as one of the criteria for maintenance decisions made with imperfect inspections to address the influence of the uncertainties involved in the prognoses results. The effectiveness of the proposed DCBM framework is illustrated by the application of an engine degradation dataset, and this model is more cost-effective than the baseline maintenance policies.
Highlights
In engineering environments, a system’s performance deteriorates due to aging devices and various external shocks, and this process is prone to nonstationary and stochastic features
We propose a probabilistic forecasting method, based on the SAE-long short-term memory (LSTM) classifier, that provides for the possibility of equipment failure, and we integrate it into a multitask learning model (MTL) with a regressor that is used for performance estimation
The contributions of this work are twofold: First, we constructed a novel SAE-LSTM-based MTL model, which can implement the joint training of the regressor and the classifier to make status prognostics for the deteriorating equipment, and the prognosis outputs of both the RUL and the failure probability can provide more accurate descriptions for the degradation process
Summary
A system’s performance deteriorates due to aging devices and various external shocks, and this process is prone to nonstationary and stochastic features. We propose a probabilistic forecasting method, based on the SAE-LSTM classifier, that provides for the possibility of equipment failure, and we integrate it into a multitask learning model (MTL) with a regressor that is used for performance estimation. This approach can be used as a general surrogate model to forecast the performance degradation and the failure probability of the system simultaneously. This study proposes a prognostic approach to simultaneously predict the performance degradation and failure probability of deteriorated systems using monitoring data, and applies this prognostic model to achieve a more cost-effective maintenance process.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
