Fault Diagnosis of Rolling Element Bearings with a Spectrum Searching Method
This addresses fault detection in rotating machinery for maintenance and safety, but appears incremental as it builds on existing spectrum analysis techniques.
The paper tackles fault diagnosis in rolling element bearings by proposing a novel spectrum searching method that constructs structural information of spectrum (SIOS) to identify harmonics of impulses buried in noise, with effectiveness verified through simulation and benchmark studies.
Rolling element bearing faults in rotating systems are observed as impulses in the vibration signals, which are usually buried in noises. In order to effectively detect the fault of bearings, a novel spectrum searching method is proposed. The structural information of spectrum (SIOS) on a predefined basis is constructed through a searching algorithm, such that the harmonics of impulses generated by faults can be clearly identified and analyzed. Local peaks of the spectrum are located on a certain bin of the basis, and then the SIOS can interpret the spectrum via the number and energy of harmonics related to frequency bins of the basis. Finally bearings can be diagnosed based on the SIOS by identifying its dominant components. Mathematical formulation is developed to guarantee the correct construction of the SISO through searching. The effectiveness of the proposed method is verified with a simulation signal and a benchmark study of bearings.