Considering various health conditions under varying operational conditions, the mining
sensitive feature from the measured signals is still a great challenge for intelligent fault …

Effective intelligent fault diagnosis has long been a research focus on the condition
monitoring of rotary machinery systems. Traditionally, time-domain vibration-based fault …

In order to enhance the performance of bearing defect classification, feature extraction and
dimensionality reduction have become important. In order to extract the effective features …

As an outstanding discriminant analysis technique, Fisher discriminant analysis (FDA)
gained extensive attention in supervised dimensionality reduction and fault diagnosis fields …

Recently, precise and deterministic feature extraction is one of the current research topics for
bearing fault diagnosis. For this aim, an experimental bearing test setup was created in this …

Detecting defects, which is a branch of target detection in the field of computer vision, is
widely used in factory production. To solve the problems in existing detection algorithms that …

In bearings defect diagnosis applications, information fusion has been widely used to
improve identification accuracy for different types of faults, which may lead to high …

Condition monitoring schemes are essential for increasing the reliability and ensuring the
equipment efficiency in industrial processes. The feature extraction and dimensionality …

Extraction of the weak fault features under strong background noise is crucial to early fault
diagnosis in bearings. A new method called local mean decomposition (LMD)-based …

The structural health monitoring relies on the continuous observation of a dynamic system
over time to identify its actual condition, detect abnormal behaviors, and predict future states …