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Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach
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| Zeitschriftentitel: | Algorithms |
|---|---|
| Personen und Körperschaften: | , |
| In: | Algorithms, 11, 2018, 11, S. 184 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
MDPI AG
|
| Schlagwörter: |
| author_facet |
Li, Qing Liang, Steven Li, Qing Liang, Steven |
|---|---|
| author |
Li, Qing Liang, Steven |
| spellingShingle |
Li, Qing Liang, Steven Algorithms Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach Computational Mathematics Computational Theory and Mathematics Numerical Analysis Theoretical Computer Science |
| author_sort |
li, qing |
| spelling |
Li, Qing Liang, Steven 1999-4893 MDPI AG Computational Mathematics Computational Theory and Mathematics Numerical Analysis Theoretical Computer Science http://dx.doi.org/10.3390/a11110184 <jats:p>Aimed at the issue of estimating the fault component from a noisy observation, a novel detection approach based on augmented Huber non-convex penalty regularization (AHNPR) is proposed. The core objectives of the proposed method are that (1) it estimates non-zero singular values (i.e., fault component) accurately and (2) it maintains the convexity of the proposed objective cost function (OCF) by restricting the parameters of the non-convex regularization. Specifically, the AHNPR model is expressed as the L1-norm minus a generalized Huber function, which avoids the underestimation weakness of the L1-norm regularization. Furthermore, the convexity of the proposed OCF is proved via the non-diagonal characteristic of the matrix BTB, meanwhile, the non-zero singular values of the OCF is solved by the forward–backward splitting (FBS) algorithm. Last, the proposed method is validated by the simulated signal and vibration signals of tapered bearing. The results demonstrate that the proposed approach can identify weak fault information from the raw vibration signal under severe background noise, that the non-convex penalty regularization can induce sparsity of the singular values more effectively than the typical convex penalty (e.g., L1-norm fused lasso optimization (LFLO) method), and that the issue of underestimating sparse coefficients can be improved.</jats:p> Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach Algorithms |
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| title |
Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach |
| title_unstemmed |
Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach |
| title_full |
Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach |
| title_fullStr |
Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach |
| title_full_unstemmed |
Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach |
| title_short |
Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach |
| title_sort |
weak fault detection of tapered rolling bearing based on penalty regularization approach |
| topic |
Computational Mathematics Computational Theory and Mathematics Numerical Analysis Theoretical Computer Science |
| url |
http://dx.doi.org/10.3390/a11110184 |
| publishDate |
2018 |
| physical |
184 |
| description |
<jats:p>Aimed at the issue of estimating the fault component from a noisy observation, a novel detection approach based on augmented Huber non-convex penalty regularization (AHNPR) is proposed. The core objectives of the proposed method are that (1) it estimates non-zero singular values (i.e., fault component) accurately and (2) it maintains the convexity of the proposed objective cost function (OCF) by restricting the parameters of the non-convex regularization. Specifically, the AHNPR model is expressed as the L1-norm minus a generalized Huber function, which avoids the underestimation weakness of the L1-norm regularization. Furthermore, the convexity of the proposed OCF is proved via the non-diagonal characteristic of the matrix BTB, meanwhile, the non-zero singular values of the OCF is solved by the forward–backward splitting (FBS) algorithm. Last, the proposed method is validated by the simulated signal and vibration signals of tapered bearing. The results demonstrate that the proposed approach can identify weak fault information from the raw vibration signal under severe background noise, that the non-convex penalty regularization can induce sparsity of the singular values more effectively than the typical convex penalty (e.g., L1-norm fused lasso optimization (LFLO) method), and that the issue of underestimating sparse coefficients can be improved.</jats:p> |
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| description | <jats:p>Aimed at the issue of estimating the fault component from a noisy observation, a novel detection approach based on augmented Huber non-convex penalty regularization (AHNPR) is proposed. The core objectives of the proposed method are that (1) it estimates non-zero singular values (i.e., fault component) accurately and (2) it maintains the convexity of the proposed objective cost function (OCF) by restricting the parameters of the non-convex regularization. Specifically, the AHNPR model is expressed as the L1-norm minus a generalized Huber function, which avoids the underestimation weakness of the L1-norm regularization. Furthermore, the convexity of the proposed OCF is proved via the non-diagonal characteristic of the matrix BTB, meanwhile, the non-zero singular values of the OCF is solved by the forward–backward splitting (FBS) algorithm. Last, the proposed method is validated by the simulated signal and vibration signals of tapered bearing. The results demonstrate that the proposed approach can identify weak fault information from the raw vibration signal under severe background noise, that the non-convex penalty regularization can induce sparsity of the singular values more effectively than the typical convex penalty (e.g., L1-norm fused lasso optimization (LFLO) method), and that the issue of underestimating sparse coefficients can be improved.</jats:p> |
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| spelling | Li, Qing Liang, Steven 1999-4893 MDPI AG Computational Mathematics Computational Theory and Mathematics Numerical Analysis Theoretical Computer Science http://dx.doi.org/10.3390/a11110184 <jats:p>Aimed at the issue of estimating the fault component from a noisy observation, a novel detection approach based on augmented Huber non-convex penalty regularization (AHNPR) is proposed. The core objectives of the proposed method are that (1) it estimates non-zero singular values (i.e., fault component) accurately and (2) it maintains the convexity of the proposed objective cost function (OCF) by restricting the parameters of the non-convex regularization. Specifically, the AHNPR model is expressed as the L1-norm minus a generalized Huber function, which avoids the underestimation weakness of the L1-norm regularization. Furthermore, the convexity of the proposed OCF is proved via the non-diagonal characteristic of the matrix BTB, meanwhile, the non-zero singular values of the OCF is solved by the forward–backward splitting (FBS) algorithm. Last, the proposed method is validated by the simulated signal and vibration signals of tapered bearing. The results demonstrate that the proposed approach can identify weak fault information from the raw vibration signal under severe background noise, that the non-convex penalty regularization can induce sparsity of the singular values more effectively than the typical convex penalty (e.g., L1-norm fused lasso optimization (LFLO) method), and that the issue of underestimating sparse coefficients can be improved.</jats:p> Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach Algorithms |
| spellingShingle | Li, Qing, Liang, Steven, Algorithms, Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach, Computational Mathematics, Computational Theory and Mathematics, Numerical Analysis, Theoretical Computer Science |
| title | Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach |
| title_full | Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach |
| title_fullStr | Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach |
| title_full_unstemmed | Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach |
| title_short | Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach |
| title_sort | weak fault detection of tapered rolling bearing based on penalty regularization approach |
| title_unstemmed | Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach |
| topic | Computational Mathematics, Computational Theory and Mathematics, Numerical Analysis, Theoretical Computer Science |
| url | http://dx.doi.org/10.3390/a11110184 |