This paper proposes a new signal feature extraction and fault diagnosis method for fault
diagnosis of low-speed machinery. Statistic filter (SF) and wavelet package transform (WPT) …

The working conditions of rolling bearings usually is very complex, which makes it difficult to
diagnose rolling bearing faults. In this paper, a novel method called the adaptive deep …

Wireless sensor networks (WSNs) which consist of miscellaneous sensors are used
frequently in monitoring vital equipment. Benefiting from the development of data mining …

Sensing is one of the most important components in manufacturing systems to ensure the
high quality of products. However, the deployment of a large number of sensors increases …

A dual-experience pool deep reinforcement learning (DEPDRL) model is proposed for
rolling bearing fault diagnosis with unbalanced data. In this method, a dual-experience pool …

Featured Application The proposed method in this paper is widely applied to the field of fault
diagnosis of rotating machinery. Abstract Aiming at the problem of poor robustness of the …

For industrial processes, new scarce faults are usually judged by experts. The lack of
instances for these faults causes a severe data imbalance problem for a diagnosis model …

Diagnosis of motor bearing faults under variable speed is a problem. In this study, a new
computer-vision-based order tracking method is proposed to address this problem. First, a …

The traditional approaches for condition monitoring of roller bearings are almost always
achieved under Shannon sampling theorem conditions, leading to a big-data problem. The …

We consider the compressive sensing reconstruction for vibration signals, which are
complex due to the harsh working environment. The recent fast iterative shrinkage …