Abstract Nowadays, Deep Learning is the most attractive research trend in the area of
Machine Learning. With the ability of learning features from raw data by deep architectures …

Deep learning, which is a subfield of machine learning, has opened a new era for the
development of neural networks. The auto-encoder is a key component of deep structure …

Detecting in prior bearing faults is an essential task of machine health monitoring because
bearings are the vital components of rotary machines. The performance of traditional …

The operation conditions of the rotating machinery are always complex and variable, which
makes it difficult to automatically and effectively capture the useful fault features from the …

Effective fault diagnosis has long been a research topic in the prognosis and health
management of rotary machinery engineered systems due to the benefits such as safety …

The collected vibration data with labeled information from bearing is far insufficient in
engineering practice, which is challenging for training an intelligent diagnosis model. For …

Bearing fault diagnosis is a critical and challenging task for prognostics and health
management of motors. The ability to efficiently and accurately classify the fault categories …

Imbalanced data problems are prevalent in the real rotating machinery applications.
Traditional data-driven diagnosis methods fail to identify the fault condition effectively for …

Microseismic technology is widely used in unconventional oil and gas production.
Microseismic noise reduction is of great significance for the identification of microseismic …

Epilepsy is a chronic neurological disorder characterized by the occurrence of spontaneous
seizures, which affects about one percent of the worlds population. Most of the current …