To integrate the complete degradation information of machinery, deep learning-based prognostics approaches usually use monitoring data acquired by different sensors as the inputs of networks. These approaches, however, lack an explicit learning mechanism to effectively identify the distinctions of different sensor data and highlight the important degradation information, thereby affecting the accuracy of deep prognostics networks and limiting their generalization. To overcome the aforementioned weaknesses, a new deep prognostics framework named multi-scale convolutional attention network (MSCAN) is proposed in this paper for predicting remaining useful life (RUL) of machinery. In the proposed MSCAN, self-attention modules are first constructed to effectively fuse the input multi-sensor data. Then, a multi-scale learning strategy is developed to automatically learn representations from different temporal scales. Finally, the learned high-level representations are fed into dynamic dense layers to perform regression analysis and RUL estimation. The proposed MSCAN is evaluated using multi-sensor monitoring data from life testing of milling cutters, and also compared with some state-of-the-art prognostics approaches. Experimental results demonstrate the effectiveness and superiority of the proposed MSCAN in fusing multi-sensor information and improving RUL prediction accuracy.

Semi-observable systems are referred to as a kind of widely used industrial equipment whose physical degradation state is only observable via shutdown inspection. To monitor the degradation process of semi-observable systems online, different types of sensors are generally employed to collect monitoring signals. Lots of studies have been conducted to fuse multi-sensor signals to predict remaining useful life (RUL). Majority of them, however, ignored the partially available state observations which can be viewed as ground truth measurements of physical degradation. To deal with this problem, this paper proposes a multi-sensor data-driven RUL prediction method for semi-observable systems, which leverages degradation information from online multi-sensor signals as well as offline state observations. This method is developed based on a generalizable state-space model combined with particle filtering (PF) framework. In the framework, a state transition function is used to describe the degradation process of system states. A multi-dimensional measurement function is constructed to describe the mapping between states and multi-sensor signals. To enhance the performance of prediction, an algorithm named prioritized sensor group selection (PSGS) is also proposed to select the optimal sensor group for RUL prediction. The effectiveness of the proposed method is demonstrated using an experiment of cutting tool wear.

Intelligent fault diagnosis of train bogie bearings based on edge computing is a promising technology to ensure the safety and reliability of train operation, which can give fault diagnosis systems better real-time performance and lower communication costs. Lightweight diagnosis networks are the core of this technology. However, existing lightweight diagnosis networks have the following limitations. 1) They lack a lightweight learning mechanism for edge computing scenarios to deal with time-scale changes of bearing fault features when trains operate under variational conditions. 2) An adaptive neural network pruning technique synchronized with training is expected to avoid the repetitive and cumbersome processes of traditional pruning techniques. To overcome the above limitations, this paper proposes a new multi-scale lightweight network with adaptive pruning for the intelligent diagnosis fault of train bogie bearings in edge computing scenarios. First, weight-sharing multi-scale convolutions (WSMSCs) are developed to capture multi-time scale features from vibration signals with extremely low computing costs. Then, inverse separable convolution (ISC) blocks are built to further capture high-level features, and an adaptive pruning technique that does not require artificial jobs is proposed to eliminate useless network structures concurrently with training. Finally, diagnostic results are obtained through a dense layer activated by Softmax. Experimental results demonstrate that the accuracy and complexity of the proposed network are superior to other state-of-the-art lightweight bearing fault diagnosis networks under varying operating conditions. Furthermore, the proposed networks can be deployed on more general edge devices.