Rotating machinery fault-induced vibration signal modulation effects: A review with mechanisms, extraction methods and applications for diagnosis.

• Different kinds of fault-induced vibration signal modulation effects are formulated, illustrated and summarized. • An up-to-date methodological review for signal modulation feature extraction methods is provided. • Representative works of vibration signal modulation feature extraction-based fault diagnosis are reviewed and illustrated. Rotating machinery faults can induce characteristic modulation effects in a vibration signal, and their diagnosis can thus be conducted by extracting fault-induced modulation features. To be specific, such a diagnostic strategy typically involves three main aspects, i.e., fault-induced signal modulation mechanisms, modulation feature extraction methods and applications for diagnosis. To date, the research works on these three aspects all achieved great progresses. A systematic review is thus urgently needed to summarize these achievements, and guide their future directions and developments. This paper aims to fill these gaps and address the needs. Three kinds of typical modulation effects induced by transmission elements are firstly reviewed and summarized. For each kind of modulation effects, a representative phenomenological model is given to formulate and illustrate the relationship between fault-induced modulation patterns and characteristic spectral distributions. As the primary tools to extract signal modulation features, different kinds of time-frequency analysis and signal decomposition methods are then systematically reviewed, including their up-to-date developments, classifications and application scenarios. Some representative works of vibration signal modulation feature extraction-based fault diagnosis are finally introduced along with several typical examples. Based on current research achievements, two potential topics for the future developments of vibration-based diagnostic techniques are the quantitative study of fault feature evolution patterns and the development of more robust and efficient feature extraction algorithms. [ABSTRACT FROM AUTHOR]