A New Method of Wheelset Bearing Fault Diagnosis

Abstract

During the movement of rail trains, trains are often subjected to harsh operating conditions such as variable speed and heavy loads. It is therefore vital to find a solution for the issue of rolling bearing malfunction diagnostics in such circumstances. This study proposes an adaptive technique for defect identification based on multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and Ramanujan subspace decomposition. MOMEDA optimally filters the signal and enhances the shock component corresponding to the defect, after which the signal is automatically decomposed into a sequence of signal components using Ramanujan subspace decomposition. The method’s benefit stems from the flawless integration of the two methods and the addition of the adaptable module. It addresses the issues that the conventional signal decomposition and subspace decomposition methods have with redundant parts and significant inaccuracies in fault feature extraction for the vibration signals under loud noise. Finally, it is evaluated through simulation and experimentation in comparison to the current widely used signal decomposition techniques. According to the findings of the envelope spectrum analysis, the novel technique can precisely extract the composite flaws that are present in the bearing, even when there is significant noise interference. Additionally, the signal-to-noise ratio (SNR) and fault defect index were introduced to quantitatively demonstrate the novel method’s denoising and potent fault extraction capabilities, respectively. The approach works well for identifying bearing faults in train wheelsets.

Fuzzy Entropy-Assisted Deconvolution Method and Its Application for Bearing Fault Diagnosis

“…FCC is the fault characteristic coefficient. Hence, T i can be solved by the numerical method from Equation (26).…”

“…(5) BD was combined with other methods to achieve better fault feature enhancement effects, for instance, the combination of MOMEDA with EEMD [21], VMD [25], resonance-based sparse signal decomposition (RSSD) [13] and Ramanujan subspace decomposition [26], and the combination of MED with SK [27].…”

Early faint fault diagnosis of wheelset axlebox bearings in urban rail trains based on ICiSSA-MOMEDA

“…, z m ). The of the decomposed signal was obtained using equation (11). A diagonal averaging operation was performed on the initial components to recover the length of the initial signal components to N.…”

“…The time-varying, non-linear operating characteristics of urban rail trains and the large range of transient load fluctuations lead to strong non-smoothness in the vibration and dynamic changes in the fault characteristics of the wheelset axlebox system. Wheelset axle boxes operate under dynamic excitation, track irregularities, and occasional vibration shocks, which result in very complex fault information [9][10][11]. Fault-diagnosis methods based on vibration signals have been intensively researched and have achieved good results in the field of fault diagnosis for urban rail trains.…”