Fault diagnosis of rotating machinery is of great importance to the high quality products and long-term safe operation.However,the useful weak features are usually corrupted by strong background noise,thus increasing ...Fault diagnosis of rotating machinery is of great importance to the high quality products and long-term safe operation.However,the useful weak features are usually corrupted by strong background noise,thus increasing the difficulty of the feature extraction.Thereby,a novel denoising method based on the tunable Q-factor wavelet transform(TQWT)using neighboring coefficients is proposed in this article.The emerging TQWT possesses excellent properties compared with the conventional constant-Q wavelet transforms,which can tune Q-factor according to the oscillatory behavior of the signal.Meanwhile,neighboring coefficients denoising is adopted to avoid the overkill of conventional term-by-term thresholding techniques.Because of having the combined advantages of the two methods,the presented denoising method is more practical and effective than other methods.The proposed method is applied to a simulated signal,a rolling element bearing with an outer race defect from antenna transmission chain and a gearbox fault detection case.The processing results demonstrate that the proposed method can successfully identify the fault features,showing that this method is more effective than the conventional wavelet thresholding denoising methods,term-by-term TQWT denoising schemes and spectral kurtosis.展开更多
Aiming at the existing problems of discrete cosine transform(DCT) de-noising method, we introduce the idea of wavelet neighboring coefficients(WNC) de-noising method, and propose the cosine neighboring coefficients(CN...Aiming at the existing problems of discrete cosine transform(DCT) de-noising method, we introduce the idea of wavelet neighboring coefficients(WNC) de-noising method, and propose the cosine neighboring coefficients(CNC) de-noising method. Based on DCT, a novel method for the fault feature extraction of hydraulic pump is analyzed. The vibration signal of pump is de-noised with CNC de-noising method, and the fault feature is extracted by performing Hilbert-Huang transform(HHT) to the output signal. The analysis results of the simulation signal and the actual one demonstrate that the proposed CNC de-noising method and the fault feature extraction method have more superior ability than the traditional ones.展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 51275384)the Key Project of National Natural Science Foundation of China (Grant No. 51035007)+1 种基金the Important National Science and Technology Specific Projects (Grant No. 2010ZX04014-016)the National Basic Research Program of China ("973" Program) (Grant No. 2009CB724405)
文摘Fault diagnosis of rotating machinery is of great importance to the high quality products and long-term safe operation.However,the useful weak features are usually corrupted by strong background noise,thus increasing the difficulty of the feature extraction.Thereby,a novel denoising method based on the tunable Q-factor wavelet transform(TQWT)using neighboring coefficients is proposed in this article.The emerging TQWT possesses excellent properties compared with the conventional constant-Q wavelet transforms,which can tune Q-factor according to the oscillatory behavior of the signal.Meanwhile,neighboring coefficients denoising is adopted to avoid the overkill of conventional term-by-term thresholding techniques.Because of having the combined advantages of the two methods,the presented denoising method is more practical and effective than other methods.The proposed method is applied to a simulated signal,a rolling element bearing with an outer race defect from antenna transmission chain and a gearbox fault detection case.The processing results demonstrate that the proposed method can successfully identify the fault features,showing that this method is more effective than the conventional wavelet thresholding denoising methods,term-by-term TQWT denoising schemes and spectral kurtosis.
基金the National Natural Science Foundation of China(No.51275524)the General Armaments Department Equipment Support Research Project
文摘Aiming at the existing problems of discrete cosine transform(DCT) de-noising method, we introduce the idea of wavelet neighboring coefficients(WNC) de-noising method, and propose the cosine neighboring coefficients(CNC) de-noising method. Based on DCT, a novel method for the fault feature extraction of hydraulic pump is analyzed. The vibration signal of pump is de-noised with CNC de-noising method, and the fault feature is extracted by performing Hilbert-Huang transform(HHT) to the output signal. The analysis results of the simulation signal and the actual one demonstrate that the proposed CNC de-noising method and the fault feature extraction method have more superior ability than the traditional ones.
