A Data-Driven Maintenance Framework Under Imperfect Inspections for Deteriorating Systems Using Multitask Learning-Based Status Prognostics

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.