Review of Signal Decomposition Theory and Its Applications in Machine Fault Diagnosis

Abstract

Major equipment manufacturing industry is the pillar of national economy and is also the strategic industry related to the national security. The operation safety of major equipment has always been the focus of public attention. Due to the harsh working conditions, the key components of the mechanical equipment are prone to failure, which will result in performance degradation and even breakdown of the equipment. Condition monitoring and fault diagnosis are necessary to ensure the safe operation of the major equipment. Signal decomposition techniques can suppress the interferences of noise and other unconcerned signal components, and thus can effectively extract fault features from the vibration signal. Therefore, signal decomposition plays a key role in machine fault diagnosis. At present, researchers have carried out extensive research on signal decomposition theory and its applications in machine fault diagnosis. Firstly, the advances of research on signal decomposition theory are reviewed from the time-domain, frequency-domain and time-frequency domain methods; then, the advances of research on the applications of the signal decomposition in machine fault diagnosis are also reviewed from the bearing, gear and rotor rub-impact fault diagnosis; finally, the challenges facing the field are summarized.