Methods for Rolling Element Bearing Fault Diagnosis under Constant and Time-varying Rotational Speed Conditions

Abstract

Bearings are among the most commonly used components in rotating machines and bearing fault diagnosis has been widely investigated, especially with vibration signal based techniques. Generally, bearing faults can be diagnosed in the frequency domain since each type of fault has a specific Fault Characteristic Frequency (FCF) proportional to the rotational frequency. However, this approach is complicated by the facts that: (1) bearing vibration signals are often contaminated by random noise and interference signals transmitted from other sources, (2) bearings often operate under time-varying speed conditions which make the FCF also time-varying. To address the problems for bearing fault signature extraction, new methods are proposed in this thesis. For the constant speed case, bearing fault signature extraction using the method of Oscillatory Behavior-based Signal Decomposition (OBSD) is investigated, which includes: (1) effects of parameter selection and (2) automatic parameter selection of OBSD for bearing fault signature extraction. For bearing fault diagnosis under time-varying speed conditions, research based on the time-frequency technique is conducted, including (1) a multiple time-frequency curve extraction algorithm and (2) a resampling-free and tachometer-free method for the time-varying speed case with the presence of interferences, and (3) proposing a short-time kurtogram for bearing fault signature extraction under time-varying speed conditions. The effectiveness of the proposed methods in this thesis has been validated by simulated signals and experimental data.