An adaptive synchroextracting transform for the analysis of noise contaminated multi-component nonstationary signals. (January 2023) - Li, Jiaxin; Mba, David; Li, Xiaochuan; Shang, Yajun; He, Shuai; Lin, Tian Ran
Highlights: An adaptive signal analysis algorithm is developed for varying speed bearings and gearbox fault diagnosis. The algorithm can effectively extract the defect signal components which energies are above the noise floor for an accurate fault diagnosis. Abstract: The Synchro-Extracting Transform technique (SET) can capture the changing dynamic in a non-stationary signal which can be applied for fault diagnosis of rotating machinery operating under varying speed or/and load conditions. However, the time frequency representation (TFR) of a signal produced by SET can be affected by noise contained in the signal, which can largely reduce the accuracy of fault diagnosis. This paper addresses this drawback and presents a new extraction operator to improve the energy concentration of the TFR of a noise contaminated multi-component signal by using an adaptive ridge curve identification process together with SET. The adaptive ridge curve extraction is deployed to extract the signal components of a multi-component signal via an iterative approach. The effectiveness of the algorithm is verified using one set of simulated noise-added signals and two sets of experimental bearing and gearbox defect signals. The result shows that the proposed technique can accurately identify the fault components from noise contaminated multi-component non-stationary machine defect signals.