The theoretical developments of data-driven fault diagnosis methods have yielded fruitful
achievements and significantly benefited industry practices. However, most methods are …

The current data-level and algorithm-level based imbalanced fault diagnosis methods have
respective limitations such as uneven data generation quality and excessive reliance on …

Abstract Internet of Things (IoT) integration in healthcare improves patient care while also
making healthcare delivery systems more effective and economical. To fully realize the …

In an actual industrial scenario, machines typically operate normally for the majority of the
time, with malfunctions occurring only occasionally. As a result, there is very little recorded …

Deep learning based visual cognition has greatly improved the accuracy of defect detection,
reducing processing times and increasing product throughput across a variety of …

With the development of machine learning, data-driven mechanical fault diagnosis methods
have been widely used in the field of PHM. Due to the limitation of the amount of fault data, it …

Most of the health data contained unbalanced data that affected the performance of the
classification method. Unbalanced data causes the classification method to classify the …

Abstract Wrong-Way Driving (WWD) crashes are relatively rare but more likely to produce
fatalities and severe injuries than other crashes. WWD crash segment prediction task is …

Software defect prediction (SDP) plays a vital role in enhancing the quality of software
projects and reducing maintenance-based risks through the ability to detect defective …

Machine learning (ML) based bearing fault detection is an emerging application of Artificial
Intelligence (AI) that has proven its utility in effectively classifying various faults for timely …