Application of improved MCKD method based on QGA in planetary gear compound fault diagnosis

Lyu, Xuan; Hu, Zhanqi; Zhou, Haili; Wang, Qiang

doi:10.1016/j.measurement.2019.02.071

Cited by 107 publications

(47 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wang et al [42] proposed a MCKD-CEEMD-ApEn method to denoise and classify the combined failure of slewing bearings. Lyu et al [43] proposed an improved maximum correlated kurtosis deconvolution method based on quantum genetic algorithm, named QGA-MCKD for gear and bearing compound fault diagnosis. Cheng et al [44] proposed the optimal minimum entropy deconvolution adjusted and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) for enhancing the impulse-like component in the fault signal.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction Based on EWT With Scale Space Threshold and Improved MCKD for Fault Diagnosis

Guo²,

Chen

2021

IEEE Access

View full text Add to dashboard Cite

Aiming at the problem of feature extraction of non-stationary, non-linear and weak fault signals, a new feature extraction method based on empirical wavelet transform (EWT) with scale space threshold (STEWT) and improved maximum correlation kurtosis deconvolution (MCKD) with power spectral entropy and grid search (PGMCKD), namely STEWT-PGMCKD is proposed for rolling bearing faults in this paper. In the proposed STEWT-PGMCKD method, the scale space threshold method is designed to solve the problems of falling into local extremum and mode over decomposition caused by the local-max-min band decomposition method of EWT, which is used to decompose the frequency band of signal, and the correlation analysis is carried out between the decomposed modal components and the original signal to retain the modal components with high correlation. Then an adaptive MCKD based on power spectral entropy is proposed to solve the problem that the signal processing effect of MCKD is affected by filter size L and deconvolution period T . Nextly, the parameters of the MCKD are optimized by grid search method. Finally, the power spectrum analysis of the enhanced signal is carried out to realize the feature extraction and fault diagnosis. The experiment results show that the proposed STEWT-PGMCKD method can effectively extract the weak fault information and accurately realize the fault diagnosis for rolling bearings.

show abstract

Section: Introductionmentioning

confidence: 99%

Feature Extraction Based on EWT With Scale Space Threshold and Improved MCKD for Fault Diagnosis

Guo²,

Chen

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Li et al studied an improved tunable Q-factor wavelet transform (TQWT) based on the characteristic frequency ratio to detect bearing transient signals [13]. Lyu et al developed an improved maximum correlated kurtosis deconvolution (MCKD) to extract the impulse features of the planetary gearbox vibration signal and successfully verified its suitability for fault detection with heavy background noise [14]. Although the above approaches have been extensively studied and exploited for planetary gearbox fault detection, they focus on the denoising effects while ignoring the geometric characteristics of the signal [15].…”

Section: Introductionmentioning

confidence: 99%

Fault detection for planetary gearbox based on an enhanced average filter and modulation signal bispectrum analysis

Guo

Zhen

et al. 2020

ISA Transactions

View full text Add to dashboard Cite

Transient impulses are important information for machinery fault diagnosis. However, the transient features contained in the vibration signals generated by planetary gearboxes are usually immersed by a large amount of background noise and harmonic components. Even mathematical morphology (MM) is an excellent anti-noise signal processing method that can directly extract the geometry of impulse features in the time domain, but the four basic operators of MM can only extract one-way impulses while cannot extract the bidirectional impulses effectively at the same time. To accurately extract the impulse feature information, a novel method for fault detection of planetary gearbox based on an enhanced average (EAVG) filter and modulated signal bispectrum (MSB) is proposed. Firstly, the properties of the extracted impulses based on the four basic operators of MM will be divided into two categories of enhanced average operators. The four EAVG filters consist of the average weighted combination of enhanced average operators, and then the best EAVG filter is selected based on correlation coefficient to implement on the original vibration signal. It allows EAVG filter to extract positive and negative impulses of vibration signal, thereby improving the accuracy of planetary gearbox fault detection.Subsequently, the performance of the EAVG filter is influenced by the length of its structural element (SE), which is adaptively determined using an indicator based kurtosis. Then, the EAVG filter selects the optimal SE length to eliminate the interference of background noise and harmonic components to enhance the impulse components of the vibration signal. However, the nonlinear modulation components that are related to the fault types and severities are not extracted exactly and still remained in the filtered signal by EAVG. Finally, the MSB is utilized to the EAVG filtered signal to decompose the modulated components and extract the fault features. The advantages of EAVG over average (AVG) filter are clarified in the simulation study. In addition, the EAVG-MSB is validated by analyzing the vibration signals of planetary gearboxes with sun gear chipped tooth, sun gear misalignment and bearing inner race fault. The results indicate that the EAVG-MSB is effective and accurate in feature extraction compared with the combination morphological filter-hat transform (CMFH) and average combination difference morphological filter (ACDIF), and the feasibility of the EAVG-MSB are proved for planetary gearbox condition monitoring and fault diagnosis.

show abstract

“…Teng et al [ 15 ] established a novel vibration model and used empirical wavelet transform to find multiple fault feature. Lyu et al [ 16 ] proposed an improved maximum correlated kurtosis deconvolution method based on quantum genetic algorithm to diagnose compound fault. Miao et al [ 17 ] developed an improved parameter-adaptive variational mode decomposition for identification of compound fault.…”

Section: Introductionmentioning

confidence: 99%

Compound Fault Diagnosis of Rolling Bearing Based on Singular Negentropy Difference Spectrum and Integrated Fast Spectral Correlation

Tang

Tian

2020

Entropy

View full text Add to dashboard Cite

Compound fault diagnosis is challenging due to the complexity, diversity and non-stationary characteristics of mechanical complex faults. In this paper, a novel compound fault separation method based on singular negentropy difference spectrum (SNDS) and integrated fast spectral correlation (IFSC) is proposed. Firstly, the original signal was de-noised by SNDS which improved the noise reduction effect of singular difference spectrum by introducing negative entropy. Secondly, the de-noised signal was analyzed by fast spectral correlation. Finally, IFSC took the fourth-order energy as the index to determine the resonance band and separate the fault features of different single fault. The proposed method is applied to analyze the simulated compound signals and the experimental vibration signals, the results show that the proposed method has excellent performance in the separation of rolling bearing composite faults.

show abstract

Application of improved MCKD method based on QGA in planetary gear compound fault diagnosis

Cited by 107 publications

References 29 publications

Feature Extraction Based on EWT With Scale Space Threshold and Improved MCKD for Fault Diagnosis

Feature Extraction Based on EWT With Scale Space Threshold and Improved MCKD for Fault Diagnosis

Fault detection for planetary gearbox based on an enhanced average filter and modulation signal bispectrum analysis

Compound Fault Diagnosis of Rolling Bearing Based on Singular Negentropy Difference Spectrum and Integrated Fast Spectral Correlation

Contact Info

Product

Resources

About