The key idea behind demodulation analysis for bearing diagnosis is to determine the fault-
induced frequency band and directly detect the potential bearing fault characteristic …

Today, instrumentation plays an essential role within Industry 4.0, integrating elements and
technologies at the forefront of the industrial world such as the Internet of Things (IoT), Cloud …

Although the deep learning diagnosis model has been widely used in the fault diagnosis of
rotating machinery. However, these methods lack the interpretability of the diagnostic …

Fault diagnosis is of significant importance for intelligent manufacturing as it can increase
production efficiency and decrease the uncertain breakdown risk of machines. Previous …

Mechanical fault diagnosis under nonstationary conditions is one of the hotspots in the
condition monitoring research field, presenting still several difficulties. Vibration signals of …

The bearing fault signal can be seen as convolution of periodical impulses and interference
components. The minimum entropy deconvolution (MED) is effective approach for the …

Linear discriminant analysis (LDA) is a supervised machine learning algorithm for
dimensionality reduction and pattern recognition, which aims to simultaneously maximize a …

Rotating machinery (RM) such as bearings and gears often operates under time-varying
operating conditions (TVOC), which makes the vibration signals non-stationary. In this case …

Although deep neural networks (DNNs) have been widely applied in machinery fault
diagnosis, the key problems of impulsive component extraction and noise filtering in the …

With the development of industrial manufacturing intelligence, the role of rotating machinery
in industrial production and life is more and more important. Aiming at the problems of the …