Edge computing is an emerging computing paradigm where computation is executed on the edge of networks rather than on cloud servers, thereby reducing the system response time, transmission bandwidth occupation, and storage and computation resources on the cloud. In this study, an edge computing-based method for real-time fault diagnosis and dynamic control of rotating machines is proposed, and an edge computing node (ECN) is designed. A vibration signal and three motor-phase current signals are acquired synchronously by the ECN. Subsequently, 40 features are extracted from the signals and fused into an indicator that can effectively distinguish multiple motor conditions. Experimental results show that the designed ECN can diagnose electrical and mechanical faults with 100% accuracy and take control of the motor when an emergency fault is detected. The algorithm procedures, including signal acquisition, feature extraction and fusion, and fault identification, can be accomplished within 0.25 s on the ECN. The proposed edge computing framework processes sensor data in real time and thus shows potential applications in the rotating machines where fault diagnosis and dynamic control are highly time sensitive.

Remaining useful life (RUL) prediction of bearings has extraordinary significance for prognostics and health management (PHM) of rotating machinery. RUL prediction approaches based on deep learning have been dedicated to finding a nonlinear mapping relationship between non-stationary monitoring data and RUL. However, most existing approaches pay little attention to the degradation trend of diverse health stages of bearing and lack the discriminative power of crucial degradation features, resulting in the loss of some important information associated with RUL. To address this challenge, this article proposes a novel RUL prediction framework based on degradation-trend-aware deep neural network with attention mechanism (DTADAN). Firstly, the multi-direction features with evident degradation trend are extracted via the analysis of bearing vibration signal from both time domain and time-frequency domain. Next, the deep neural network architecture with attention mechanism is utilized to adaptively learn the critical degradation features beneficial for RUL prediction. Distinct from the existing approaches, the proposed framework is able to dynamically extract key degradation features of the bearing including degradation trend information and effectively fuse multi-direction information to improve RUL prediction accuracy. The performance of the proposed approach is evaluated via case studies on XJTU-SY bearing dataset and PRONOSTIA bearing dataset. Compared with other state-of-the-art approaches, the proposed framework has better predictive accuracy and robustness. Additionally, interpretable analysis is provided to reveal the process of model learning and data characteristics, and the analysis results are helpful in guiding model learning.

In many cases, a network may be dispersedly recorded by different participants and each participant is one part of the original network, and no one is willing to share its data due to commercial competition. Therefore, each participant forms an independent private network, and they form a `` multiple private networks'' regarding the original network. Existing methods only use the structure of private network itself to predict missing links, leading to underutilized information and deteriorated prediction accuracy. One natural question arises: how to integrate the information of multiple private networks by formulating a security protocol, so as to help each private network to better predict missing links in its own network without disclosing its structure to others. To this end, we propose an SMPC-LP method based on secure multiparty computation (SMPC) to solve this problem. The method fuses the information of each private network without disclosing their inputs, and then, the similarity score of each node pair is jointly calculated, achieving enhanced link prediction performance in each private network. The experimental results show that the SMPC-LP method can better predict the missing links than the methods only using the information of the one private network, without violating data privacy agreement.

Due to the continuous technological innovation in industrial processes, many deep learning based methods have shown powerful capability in handing equipment status monitoring, but most of them ignore the temporal features and the dynamic changes of diverse spatial structure of the raw data. Meanwhile, these methods usually focus on handling a single downstream task, but rarely consider different tasks simultaneously. To solve these issues, this paper proposes a more flexible monitoring framework based on dynamic-multilayer graph convolution network, which can be adapted to different downstream tasks simultaneously by agile combinations of the modules according to the different industrial scenarios. First, the time series of fault samples are segmented and constructed into a feature matrix to extract the temporal information by the temporal module. At the same time, in order to fully characterize the dynamic change of different spatial structures among samples, the dynamic graph of each moment is expanded into a multilayer graph through various composition indexes. Then, the spatial information is extracted by the intra-layer and inter-layer convolution operations in the spatial module, and the fused features are applied to different downstream tasks. Finally, the experiments are performed in two different downstream tasks, namely three datasets for fault diagnosis and one dataset for remaining useful life prediction. The results of both diagnosis and prediction are better than those of the comparison algorithms, so the effectiveness and flexibility of the proposed framework are proved.