Application of a Novel Improved Adaptive CYCBD Method in Gearbox Compound Fault Diagnosis

Abstract

Recently, cyclostationary blind deconvolution (CYCBD) is often used in gearbox fault feature extraction, and it is more effective in recovering single periodic pulse. However, in the engineering application of CYCBD, the filter length (L) and cycle frequency (α)need to be verified and set through a large number of experiments, and the efficiency is very low; Moreover, the effect is not good when it is used to extract composite fault features under the background of strong noise. In order to overcome the above limitations, empirical mode decomposition (EEMD) is used to preprocess the composite fault. EEMD can remove the high-frequency noise and weak correlation components in the sampled signal. The strong correlation component is reconstructed to obtain the modal function closer to the fault frequency. The chimpanzee intelligent algorithm is applied to the determination of L and α of CYCBD by optimization to form an adaptive CYCBD. Adaptive CYCBD takes the dispersion entropy of envelope spectrum as the fitness function of chimpanzee optimization algorithm (CHOA), and finds the optimal L and α through iteration.The optimal parameter value is applied to CYCBD, and the reconstructed modal function is deconvoluted to obtain the optimal inverse filter, so as to accurately separate the fault characteristic components. Simulation and experimental results show that this method is effective for gearbox composite fault diagnosis and extraction under strong noise background.

Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities

“…More details can be found in [40][41][42][43][44][45][46][47][48], which are not enumerated here. and Cyclostationary Blind Deconvolution (CYCBD) [63], can enhance weak periodic features and suppress signal noise by constructing a comb filter, thus, have been proven to be an effective tool for separating compound fault with weak components. proposed an improved MCKD method for compound fault diagnosis of planetary gear by combining a quantum genetic algorithm (QGA), in which the single fault-related feature is extracted by the proposed method [58].…”

“…CYCBD is a blind deconvolution algorithm that has been proven to be an effective tool for compound fault diagnosis because it can reconstruct periodic impulses from coupled fault signals. Applications can be found in [63], in which an improved adaptive CYCBD method was proposed by Sun et al and applied for gearbox compound fault diagnosis under strong noise background. The CYCBD-based method provided an alternative way to extract weak shock faults from the compound fault signals.…”

State of the art on vibration signal processing towards data‐driven gear fault diagnosis

“…Ou et al proposed a method based on EEMD, cloud model, and PSO-SVM to identify minor gear faults [15]. For compound fault diagnosis of gearboxes, Sun et al proposed a method combining a novel improved adaptive cyclostationary blind deconvolution (CYCBD) with EEMD [16]. EEMD is applied to remove the high-frequency noise and weak correlation components in the sampled signal.…”

Application of IPSO-MCKD-IVMD-CAF in the compound fault diagnosis of rolling bearing

“…The successful application of CAF theory in signal processing is presented in [25]. In a word, bearing compound fault diagnosis has numerous research achievements in the aspects of cyclostationarity theory [26,27], blind source separation [28,29], deep learning [30,31] and acoustic emission technology [32,33] The overall framework of the paper is as follows: the algorithm theory of MCKD, VMD and CAF are simply explained in section 2. The process of optimizing MCKD with IPSO algorithm is mainly described in section 3.1.…”