The determination of an index to balance the impulsiveness and cyclostationarity of an
expected component is an interesting research topic in mechanical health monitoring. Many …

Intelligent fault diagnosis methods are promising in dealing with mechanical big data owing
to its efficiency in extracting discriminative features automatically. Sparse filtering (SF) is a …

Using transfer learning (TL) for fault detection and diagnosis has been a hot topic in
prognostic and health management (PHM) field. In this article, a systematic framework is …

In this study, a novel fault diagnosis system for rotating machinery using thermal imaging is
proposed. This system consists of bi-dimensional empirical mode decomposition (BEMD) for …

Since the vibration signal of mechanical equipment with early faults is highly similar to that of
mechanical equipment under the normal state, it is still a great challenge to extract sensitive …

Sensitive feature extraction from the raw vibration signal is still a great challenge for
intelligent fault diagnosis of rolling bearing. Current fault classification framework generally …

Feature-based classification techniques consist of data acquisition, preprocessing, feature
representation, feature calculation, feature selection, and classifiers. They are useful for …

Raw statistical features can imitate the amplitude, average, energy and time, and frequency
series distribution of a raw vibration signal. However, these raw statistical features are either …

The safety, reliability, efficiency and performance of rotating machinery are major concerns
in industry. The task of condition monitoring and fault diagnosis of rotating machinery faults …

The rolling bearing is a key element of rotating machine and its fault diagnosis is a research
focus. When a single fault of a rolling bearing fails to be addressed in time, it will cause …