This paper proposes a novel hybrid method aiming at the fault prognosis of bearings. A
nonlinear health indicator (HI) is first constructed using Complete Ensemble Empirical Mode …

This paper presents a data-driven intelligent fault diagnosis approach for rotating machinery
(RM) based on a novel continuous wavelet transform-local binary convolutional neural …

Health prognosis of rolling bearing is of great significance to improve its safety and
reliability. This paper presents a novel health prognosis method for the rolling bearing based …

Remaining useful life (RUL) prognosis is of great significance to improve the reliability,
availability, and maintenance cost of an industrial equipment. Traditional machine learning …

Deep learning (DL) method are extensively used for bearing fault diagnosis (BFD). Due to
severe data distribution difference under variable working conditions, they have …

Mechanical system fault diagnosis is essential to save costs and ensure safety. Generally,
rotating machinery operates in nonstationary cases, which makes fault features complex and …

This paper proposes a novel hybrid scheme through read-first-LSTM (RLSTM) encoder-
decoder and broad learning system (BLS) for bearings degradation monitoring and …

In this paper, a hybrid convolutional neural network (CNN)-bidirectional gated recurrent unit
(BiGRU) model is integrated with the bootstrap method to endow the deep learning (DL) …

Supervised learning-based methods have been widely used for fault diagnosis of rotating
machinery. The performance of these methods usually relies on the labeled fault samples …

Deep-learning-based methods have been successfully applied to fault diagnosis of rotating
machinery. However, the domain mismatch among different operating conditions …