Battery degradation modeling in the presence of uncertainty is a key but challenging issue in the application of battery predictive maintenance. This article develops a capacity prediction model with uncertainty quantification for lithium-ion batteries and proposes a dynamic maintenance strategy that can help to make an optimized decision at each battery cycle stage. To be specific, after using the 1-D convolution neural network (1dCNN), deep representative features hidden in original measured signals are extracted. Then, the bidirectional long short-term memory (Bi-LSTM) is applied to estimate the battery capacities, while the quantile regression (QR) layer is embedded into the construction of the Bi-LSTM network to obtain the capacities for different quantiles. Next, the kernel density estimation (KDE) is utilized to derive the probability density of the predicted points at each battery cycle stage. Thus, the combination of 1dCNN, Bi-LSTM, QR, and KDE, named 1dCNN-BiLSTMQR-KDE, forms an efficacious capacity prediction model with reliable uncertainty management. Finally, the costs of different decisions at each battery cycle stage are evaluated, and the decision with the lower cost will be chosen. The whole proposition is verified on battery degradation datasets from NASA, and the comparison with other methods show that the proposed method is competitive.

Modern engineering systems are usually equipped with a variety of sensors to measure real-time operating conditions. Based on the condition monitoring data from multiple sensor sources, this paper deals with a dynamic predictive maintenance scheduling using deep learning ensemble for system health prognostics. The deep learning ensemble model is composed of deep autoencoder and bidirectional long short-term memory in series, and aims to accurately estimate the system health state and remaining useful life. The deep autoencoder is used to extract the deep representative features hidden in condition monitoring data, whereas the inclusion of the bidirectional long short-term memory allows learning the temporal correlation information of features in both forward and backward time directions. Thus, the combination of the two models forms an effective model. With obtained prognostic information, optimal maintenance decisions are determined by two designed rules. The first rule presets a reliable remaining useful life threshold to dynamically judge whether maintenance is carried out at current inspection time, while the second one selects a degradation state to dynamically determine whether to place a spare part order. The performance of the proposed dynamic predictive maintenance strategy is measured by the maintenance cost per unit operating time (cost rate) using the aero-engine dataset from NASA. Its effectiveness is demonstrated by comparing with recent publications.

Failure prediction and maintenance decision-making are two core activities in a prognostics and health management (PHM) system. However, they are often studied independently and hierarchically. The main goal of this paper is to combine system failure prediction with maintenance decision-making to develop a predictive maintenance strategy. System failure prediction is achieved by constructing an ensemble model of deep autoencoder (DAE), long short-term memory (LSTM), quantile regression (QR) and kernel density estimation (KDE), namely DAE-LSTMQR-KDE. Then, on the basis of the probability density of system failure time obtained from the ensemble model, replacement cost function (RCF) and ordering cost function (OCF) are proposed to support maintenance and inventory decisions. Finally, optimal decisions are determined by minimizing the two cost functions. A score equal to 246.59 and a coverage width-based criterion (CWC) index equal to 0.35 were obtained when the DAE-LSTMQR-KDE ensemble model was applied to C-MAPSS dataset, while the average maintenance cost rate (MCR) of the proposed maintenance strategy was 0.74. The results demonstrated that the proposed prediction and maintenance method outperforms several state-of-the-art methods. In addition, different cost structure scenarios are also investigated to illustrate the flexibility of maintenance decisions based on failure prediction information.

This article studies the adaptive fault tolerant control (AFTC) and collision avoidance problem for wheel mobile robots (WMRs) under event-triggered mechanism and communication constraints. The dynamics of WMRs and their transformation are considered first. To handle the constraint that only part of the robots can access the leader, the distributed state estimators are designed. Simultaneously the actuator faults and external disturbances are approximated by cerebellar model neural network. Then, an event-triggered-based AFTC and collision avoidance scheme is developed by utilizing a backstepping control technique and artificial potential field. The stability of the closed-loop system is proved and Zeno behaviors are excluded. Finally, the simulation evaluation and experimentation of three robots is implemented to illustrate the superiority of the presented scheme.