Extracting fault-related features from noisy vibration signals is a pivotal prerequisite for condition monitoring of rolling element bearings. Multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is a non-iterative blind deconvolution algorithm and has been proven as an efficient tool for periodic impulses extraction of rotating machinery. However, the effectiveness of MOMEDA strongly relies on the accuracy of the prior impulse period. Additionally, the MOMEDA method suffers from serious edge effect, resulting in the filtered signal shorter than the original signal. To solve these limitations of MOMEDA, a novel deconvolution algorithm called adaptive MOMEDA (AMOMEDA) is presented in this paper. The new method adopts a periodic modulation intensity (PMI) based strategy to automatically estimate the impulse period rather than a fixed prior period. The impulse period can gradually approximate the real fault-related period with the update of the filtered signal after every iterative step. To overcome the edge effect of MOMEDA, a waveform extension strategy is designed to adaptively restore the length of the filtered signal as the original signal according to the local characteristics of the signal at the left boundary. Simulated and experimental results of railway axle-box bearing demonstrated the effectiveness and superiority of AMOMEDA by comparison with MOMEDA. Moreover, the proposed method is compared with fast kurtogram and the improved complete ensemble empirical mode decomposition with adaptive noise to show that the proposed method is more effective.

The repetitive transient impulses are typical symptoms of rolling bearing faults. The indicator of second-order cyclostationarity (ICS2) based blind deconvolution (CYCBD) maximizing the cyclostationary behavior of the excitation source proves to be an effective method for enhancing the periodic cyclostationarity component caused by bearing defect. However, the deviation of the fault characteristic frequenc–a common phenomenon easily caused by roller creep or measurement systems, etc., can lead to the collapse of this method in practical applications. Additionally, this method is prone to induce the generation of pseudo-cyclostationary components under the guidance of an inappropriate cyclic frequency. Thus, this work proposes a candidate fault frequencies (CFFs) based blind deconvolution, abbreviated as CFFsBD, for fault feature enhancement of bearings. This idea comes from the concept of CFFs–a collection of frequencies most likely to be associated with bearing fault, that can be identified by mining the local time-frequency features of the signal to be analyzed. A new indicator constructed by using CFFs to replace the cycle frequencies in ICS2 is utilized as the criterion to guide the solution of the deconvolution filter. This new indicator is a generalized version of ICS2, and thus CYCBD can be considered a special case of the proposed CFFsBD. The performance of the CFFsBD is demonstrated by the analysis of simulated and experimental signals of faulty bearings. The results highlight the robustness and stability of the proposed CFFsBD in extracting fault-induced cyclostationary symptoms.

The transient impulse features caused by rolling bearing faults are often present in the resonance frequency band which is closely related to the dynamic characteristics of the machine structure. Informative frequency band identification is a crucial prerequisite for envelope analysis and thereby accurate fault diagnosis of rolling bearings. In this paper, based on the ratio of quasi-arithmetic means and Gini index, improved Gini indices (IGIs) are proposed to quantify the transient impulse features of a signal, and their effectiveness and advantages in sparse quantification are confirmed by simulation analysis and comparisons with traditional sparsity measures. Furthermore, an IGI-based envelope analysis method named IGIgram is developed for fault diagnosis of rolling bearings. In the new method, an IGI-based indicator is constructed to evaluate the impulsiveness and cyclostationarity of the narrow-band filtered signal simultaneously, and then a frequency band with abundant fault information is adaptively determined for extracting bearing fault features. The performance of the IGIgram method is verified on the simulation signal and railway bearing experimental signals and compared with typical sparsity measures-based envelope analysis methods and log-cycligram. The results demonstrate that the proposed IGIs are efficient in quantifying bearing fault-induced transient features and the IGIgram method with appropriate power exponent can effectively achieve the diagnostics of different axle-box bearing faults.