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A Novel De-noising Method Based on Discrete Cosine Transform and Its Application in the fault feature extraction of Hydraulic Pump

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Journal of Shanghai Jiaotong university(Science) 2016年第3期21卷 297-306页

作者：王余奎黄之杰赵徐成朱毅魏东涛 Air Force Logistics College of PLA Xuzhou 221000JiangsuChina Ordnance Engineering College of PLA Shijiazhuang 050003China

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.

关键词： discrete cosine transform(DCT) de-noising method cosine neighboring coefficients(CNC) de-noising method hydraulic pump fault feature extraction

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Thruster fault feature extraction method for underwater vehicle

Thruster fault feature extraction method for underwater vehi...

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第32届中国控制与决策会议

作者： Dacheng Yu Ke Deng Wei Gong Mingjun Zhang Zhenzhong Chu College of Mechanical and Electrical Engineering Harbin Engineering University College of Information Engineering Shanghai Maritime University

A novel thruster fault feature extraction method for autonomous underwater vehicle（AUV） is presented in this article.When using fractal dimension method to extract the thrust fault feature from state quantity,the noise feature value may be greater than the fault feature value,which lead to failure of fault feature extraction.To solve this problem,this paper proposes an improved method based on empirical mode decomposition,fractal dimension and the short-time higher frequency component positioning algorithm to extract fault features from state quantitiy.In this paper,the empirical mode decomposition（EMD） is used to replace the filtering method of the fractal dimension.The rolling time windows are introduced into the high frequency part of EMD,and the fractal dimension mutation at the time of fault occurrence is captured by calculating the fractal dimension of small samples in each time window.fault feature extraction is completed by extracting the maximum value of the fractal dimension mutation.When using the fractal dimension method to extract the thruster fault feature from the control quantity,the calculation time is too long.To reduce the calculation time,this paper proposes a fault feature extraction and identification method based on fractal dimension and the support vector regression algorithm（SVR）.In this paper,the ideal SVR model between control quantity and fractal dimension for different thruster fault is established,and the fault feature is extracted from the original data by SVR model.The effectiveness of the improved method is verified by the pool experimental data of an underwater vehicle.

关键词： SVR model fractal dimension fault feature extraction fault identification

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fault diagnosis of rolling bearings with noise signal based on modified kernel principal component analysis and DC‐ResNet

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 1014-1028页

作者： Yunji Zhao Menglin Zhou Xiaozhuo Xu Nannan Zhang School of Electrical Engineering and Automation Henan Polytechnic UniversityJiaozuoChina Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment Henan Polytechnic UniversityJiaozuoChina

In view of the influence of aliasing noise on the effectiveness and accuracy of bearing fault diagnosis,a bearing fault diagnosis algorithm based on the spatial decoupling method of modified kernel principal component analysis(MKPCA)and the residual network with deformable convolution(DC‐ResNet)is innovatively proposed.Firstly,the Gaussian noise with different signal‐to‐noise ratios(SNRs)is added to the data to simulate the different degrees of noise in the actual data acquisition process.The MKPCA is used to project the fault signal with different SNRs in the kernel space to reduce the data dimension and eliminate some noise effects.Finally,the DC‐ResNet model is used to further filter the noise effects and fully extract the fault features through the training of the preprocessed data.The proposed algorithm is tested on the Case Western Reserve University(CWRU)and Xi'an Jiaotong University and Changxing Sumyoung Technology Co.,Ltd.(XJTU‐SY)bearing data sets with different SNR noise.The fault diagnosis accuracy can reach 100%within 30 min,which has better performance than most of the existing methods.The experimental results show that the algorithm has an excellent effect on accuracy and computation complexity under different noise levels.

关键词： artificial intelligence classification deep learning fault feature extraction

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