Identifying fault of rotating machinery under different load conditions with high accuracy is a
remaining challenge for vibration signal based fault diagnosis. Aiming at this challenge, this …

Most intelligent fault diagnosis methods of rotating machinery generally consider that normal
samples and fault samples as equally important for pattern recognition training. It ignores …

Due to the non-stationary and nonlinear characteristics of rotating machinery fault signals, it
is difficult to identify different fault conditions using only traditional time-frequency domain …

Fault diagnosis methods based on machine learning have made great progress for rotating
machinery. The main steps of the machine learning process involve feature extraction …

Rolling bearings and gears are important components of rotating machinery. Their operating
condition affects the operation of the equipment. Fault in the accessory directly leads to …

Fault diagnosis of rotating machinery mainly includes fault feature extraction and fault
classification. Vibration signal from the operation of machinery usually could help …

Timely and accurate condition monitoring and fault diagnosis of rotating machinery are very
important to maintain a high degree of availability, reliability and operational safety. This …

To achieve better fault diagnosis of rotating machinery, this paper presents a novel
intelligent fault diagnosis model based on singular value manifold features (SVMF) …

As a crucial and widely used component in industrial fields with great complexity, the health
condition of rotating machinery is directly related to production efficiency and safety …

The vibration signals of faulty rotating machinery are typically nonstationary, nonlinear, and
mixed with abundant compounded background noise. To extract the potential excitations …