Improved sliding average target detection algorithm based on statistical information

Da-wei, 李大维 Li; Hai-jiang, 孙海江 Sun; Wei-ning, 刘伟宁 Liu; Pei-xun, 刘培勋 Liu

doi:10.3788/yjyxs20183306.0497

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“…The sample length is L, the sliding window width is M, and the sample mean value of smooth filter output is y L . According to the definition of sliding average filtering [11], y L can be obtained as:…”

Section: Algorithm Derivation Of Pca Extraction Model Of Processing Roller's Performance Degradation Featurementioning

confidence: 99%

Research on Feature Extraction of Performance Degradation for Flexible Material R2R Processing Roller Based on PCA

Deng

Zhou

Yao

et al. 2020

International Journal of Rotating Machinery

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Performance feature extraction is the primary problem in equipment performance degradation assessment. To handle the problem of high-dimensional performance characterization and complexity of calculating the performance indicators in flexible material roll-to-roll processing, this paper proposes a PCA method for extracting the degradation characteristic of roll shaft. Based on the analysis of the performance influencing factors of flexible material roll-to-roll processing roller, a principal component analysis extraction model was constructed. The original feature parameter matrix composed of 10-dimensional feature parameters such as time domain, frequency domain, and time-frequency domain vibration signal of the roll shaft was established; then, we obtained a new feature parameter matrix Z org ∗ by normalizing the original feature parameter matrix. The correlation measure between every two parameters in the matrix Z org ∗ was used as the eigenvalue to establish the covariance matrix of the performance degradation feature parameters. The Jacobi iteration method was introduced to derive the algorithm for solving eigenvalue and eigenvector of the covariance matrix. Finally, using the eigenvalue cumulative contribution rate as the screening rule, we linearly weighted and fused the eigenvectors and derived the feature principal component matrix F of the processing roller vibration signal. Experiments showed that the initially obtained, 10-dimensional features of the processing rollers’ vibration signals, such as average, root mean square, kurtosis index, centroid frequency, root mean square of frequency, standard deviation of frequency, and energy of the intrinsic mode function component, can be expressed by 3-dimensional principal components F 1 , F 2 , and F 3 . The vibration signal features reduction dimension was realized, and F 1 , F 2 , and F 3 contain 98.9% of the original vibration signal data, further illustrating that the method has high precision in feature parameters’ extraction and the advantage of eliminating the correlation between feature parameters and reducing the workload selecting feature parameters.

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Section: Algorithm Derivation Of Pca Extraction Model Of Processing Roller's Performance Degradation Featurementioning

confidence: 99%

Research on Feature Extraction of Performance Degradation for Flexible Material R2R Processing Roller Based on PCA

Deng

Zhou

Yao

et al. 2020

International Journal of Rotating Machinery

View full text Add to dashboard Cite

show abstract

Improved sliding average target detection algorithm based on statistical information

Cited by 1 publication

References 0 publications

Research on Feature Extraction of Performance Degradation for Flexible Material R2R Processing Roller Based on PCA

Research on Feature Extraction of Performance Degradation for Flexible Material R2R Processing Roller Based on PCA

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