Background An intelligent predictive health management paradigm for industrial machinery
is inevitable in Industry 4.0. The machinery health failure/degradation data acquired as time …

Remaining useful life (RUL) prediction is a challenging task for prognostics and health
management (PHM). Due to the complexity physics involved for precisely modeling the …

As is known to all, rolling bearing fault will induce periodic impulses. Although the existing
fault diagnosis methods, such as wavelet transform (WT) and ensemble empirical mode …

Aiming at the problem of poor prediction performance of rolling bearing remaining useful life
(RUL) with single performance degradation indicator, a novel based-performance …

There are many signal decomposition methods in gear fault diagnosis at present, such as
ensemble empirical mode decomposition (EEMD), wavelet transform (WT), singular spectral …

Remaining useful life (RUL) refers to the remaining service life of a mechanical system after
it runs for a period. Predicting the remaining service life of the system accurately can greatly …

Remaining useful life (RUL) prediction is fundamental to prognostics and health
management (PHM). Considering the advantages of both model-based and data-driven …

Predicting the remaining useful life (RUL) of rolling bearings can effectively prevent the
breakdown of rotating machinery systems and catastrophic accidents. Most existing RUL …

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) …

Accurate bearing degradation assessment and remaining useful life (RUL) prediction may
effectively avoid major disasters in manufacturing. With the rapid development of the …