When the rolling bearing fails, the fault features contained in bearing vibration signal are
easily submerged by fortissimo noise interference signals, and have obvious non-stationary …

Current deep-learning methods are often based on significantly large quantities of labeled
fault data for supervised training. In practice, it is difficult to obtain samples of rolling bearing …

Rotating machines are commonly used mechanical equipment in various industrial
applications. These machines are subjected to dynamic and harsh operating conditions over …

Fault diagnosis in high-speed machining centers (HSM) is critical in manufacturing systems,
since early detection saves a substantial amount of time and money. It is known that 42% of …

Gas path fault diagnosis plays a critical role in the security guarantee and maintenance of
aero-engines. In this paper, an approach based on a fusion neural network under multiple …

To satisfy the requirements of the end-to-end fault diagnosis of rolling bearings, a hybrid
model, based on optimal SWD and 1D-CNN, with the layer of multi-sensor data fusion, is …

Mechanical fault diagnosis can prevent the deterioration of mechanical equipment failures
and is important for the stable operation of mechanical equipment. Firstly, this paper reviews …

Despite significant advances in auto-encoder (AE) based intelligent fault diagnosis recently,
their assumption of high-quality sensor data is unrealistic due to noisy environment. Aiming …

In the current industrial production process, fault data of rotating machinery are often difficult
to obtain, and a small amount of fault data can lead to insufficient training of the model and …

Deep neural networks (DNNs) have shown high accuracy in fault diagnosis, but they
struggle to effectively capture changes over time in multivariate time-series data and suffer …