A Hybrid Dynamic Principal Component Analysis Feature Extraction Method to Identify Piston Pin Wear for Binary Classifier Modeling
The wear condition of a piston pin is a main factor in determining the operational continuity and life cycle of a diesel engine; identifying its vibration feature is of paramount importance in carrying out necessary maintenance in the early wear stage. As the dynamic vibration features are susceptib...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/13/1/68 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588048076898304 |
|---|---|
| author | Hao Yang Yubin Zhai Mengkun Zheng Tan Wang Dongliang Guo Jianhui Liang Xincheng Li Xianliang Liu Mingtao Jia Rui Zhang |
| author_facet | Hao Yang Yubin Zhai Mengkun Zheng Tan Wang Dongliang Guo Jianhui Liang Xincheng Li Xianliang Liu Mingtao Jia Rui Zhang |
| author_sort | Hao Yang |
| collection | DOAJ |
| description | The wear condition of a piston pin is a main factor in determining the operational continuity and life cycle of a diesel engine; identifying its vibration feature is of paramount importance in carrying out necessary maintenance in the early wear stage. As the dynamic vibration features are susceptible to environmental disturbance during operation, an effective signal processing method is necessary to improve the accuracy and fineness of the extracted features, which is essential to build a reliable and precise binary classifier model to identify piston pin wear based on the features. Aiming at the feature extraction requirements of anti-noise, accuracy and effectiveness, this paper proposes a piston pin wear feature extraction algorithm based on dynamic principal component analysis (DPCA) combined with variational mode decomposition (VMD) and singular value decomposition (SVD). An orthogonal sensor layout is applied to collect the vibration signal under normal and worn piston pin conditions, which proved effective in reducing environmental vibration disturbance. DPCA is utilized to extract dynamical vibration features by introducing time lag. Then, the dynamic principal component matrix is further decomposed by VMD to obtain intrinsic mode functions (IMFs) as finer features and is finally decomposed by SVD to compress the features, thus improving the classification efficiency based on the features. To validate the significance of the features extracted by the proposed method, a support vector machine (SVM) is employed to model binary classifiers to evaluate the classification performance trained by different features. A modeling dataset containing 80 samples (40 normal samples and 40 worn samples) is employed, and five-round cross-validation is adopted. For each round, two binary classifier models are trained by features extracted by the proposed method and the empirical mode decomposition (EMD)–auto regressive (AR) spectrum method, fast Fourier transform (FFT) and continuous wavelet transform (CWT), respectively; the classification precision, recall ratio, accuracy and F1 ratio are obtained on the testing set by contrasting the overall performances of the five-round cross-validation, and the proposed method is proved to be more effective in noise reduction and significant feature extraction, which is able to improve the accuracy and efficiency of binary classification for piston pin wear identification. |
| format | Article |
| id | doaj-art-3336bc34d2154d88998090e2033a68e3 |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-3336bc34d2154d88998090e2033a68e32025-01-24T13:39:21ZengMDPI AGMachines2075-17022025-01-011316810.3390/machines13010068A Hybrid Dynamic Principal Component Analysis Feature Extraction Method to Identify Piston Pin Wear for Binary Classifier ModelingHao Yang0Yubin Zhai1Mengkun Zheng2Tan Wang3Dongliang Guo4Jianhui Liang5Xincheng Li6Xianliang Liu7Mingtao Jia8Rui Zhang9College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, ChinaCollege of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, ChinaCollege of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, ChinaKey Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Hefei 230036, ChinaCollege of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, ChinaCollege of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, ChinaCollege of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, ChinaCollege of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, ChinaCollege of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, ChinaCollege of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, ChinaThe wear condition of a piston pin is a main factor in determining the operational continuity and life cycle of a diesel engine; identifying its vibration feature is of paramount importance in carrying out necessary maintenance in the early wear stage. As the dynamic vibration features are susceptible to environmental disturbance during operation, an effective signal processing method is necessary to improve the accuracy and fineness of the extracted features, which is essential to build a reliable and precise binary classifier model to identify piston pin wear based on the features. Aiming at the feature extraction requirements of anti-noise, accuracy and effectiveness, this paper proposes a piston pin wear feature extraction algorithm based on dynamic principal component analysis (DPCA) combined with variational mode decomposition (VMD) and singular value decomposition (SVD). An orthogonal sensor layout is applied to collect the vibration signal under normal and worn piston pin conditions, which proved effective in reducing environmental vibration disturbance. DPCA is utilized to extract dynamical vibration features by introducing time lag. Then, the dynamic principal component matrix is further decomposed by VMD to obtain intrinsic mode functions (IMFs) as finer features and is finally decomposed by SVD to compress the features, thus improving the classification efficiency based on the features. To validate the significance of the features extracted by the proposed method, a support vector machine (SVM) is employed to model binary classifiers to evaluate the classification performance trained by different features. A modeling dataset containing 80 samples (40 normal samples and 40 worn samples) is employed, and five-round cross-validation is adopted. For each round, two binary classifier models are trained by features extracted by the proposed method and the empirical mode decomposition (EMD)–auto regressive (AR) spectrum method, fast Fourier transform (FFT) and continuous wavelet transform (CWT), respectively; the classification precision, recall ratio, accuracy and F1 ratio are obtained on the testing set by contrasting the overall performances of the five-round cross-validation, and the proposed method is proved to be more effective in noise reduction and significant feature extraction, which is able to improve the accuracy and efficiency of binary classification for piston pin wear identification.https://www.mdpi.com/2075-1702/13/1/68piston pin wearvibration feature extractiondynamic principal component analysis (DPCA)variational mode decomposition (VMD)singular value decomposition (SVD) |
| spellingShingle | Hao Yang Yubin Zhai Mengkun Zheng Tan Wang Dongliang Guo Jianhui Liang Xincheng Li Xianliang Liu Mingtao Jia Rui Zhang A Hybrid Dynamic Principal Component Analysis Feature Extraction Method to Identify Piston Pin Wear for Binary Classifier Modeling Machines piston pin wear vibration feature extraction dynamic principal component analysis (DPCA) variational mode decomposition (VMD) singular value decomposition (SVD) |
| title | A Hybrid Dynamic Principal Component Analysis Feature Extraction Method to Identify Piston Pin Wear for Binary Classifier Modeling |
| title_full | A Hybrid Dynamic Principal Component Analysis Feature Extraction Method to Identify Piston Pin Wear for Binary Classifier Modeling |
| title_fullStr | A Hybrid Dynamic Principal Component Analysis Feature Extraction Method to Identify Piston Pin Wear for Binary Classifier Modeling |
| title_full_unstemmed | A Hybrid Dynamic Principal Component Analysis Feature Extraction Method to Identify Piston Pin Wear for Binary Classifier Modeling |
| title_short | A Hybrid Dynamic Principal Component Analysis Feature Extraction Method to Identify Piston Pin Wear for Binary Classifier Modeling |
| title_sort | hybrid dynamic principal component analysis feature extraction method to identify piston pin wear for binary classifier modeling |
| topic | piston pin wear vibration feature extraction dynamic principal component analysis (DPCA) variational mode decomposition (VMD) singular value decomposition (SVD) |
| url | https://www.mdpi.com/2075-1702/13/1/68 |
| work_keys_str_mv | AT haoyang ahybriddynamicprincipalcomponentanalysisfeatureextractionmethodtoidentifypistonpinwearforbinaryclassifiermodeling AT yubinzhai ahybriddynamicprincipalcomponentanalysisfeatureextractionmethodtoidentifypistonpinwearforbinaryclassifiermodeling AT mengkunzheng ahybriddynamicprincipalcomponentanalysisfeatureextractionmethodtoidentifypistonpinwearforbinaryclassifiermodeling AT tanwang ahybriddynamicprincipalcomponentanalysisfeatureextractionmethodtoidentifypistonpinwearforbinaryclassifiermodeling AT dongliangguo ahybriddynamicprincipalcomponentanalysisfeatureextractionmethodtoidentifypistonpinwearforbinaryclassifiermodeling AT jianhuiliang ahybriddynamicprincipalcomponentanalysisfeatureextractionmethodtoidentifypistonpinwearforbinaryclassifiermodeling AT xinchengli ahybriddynamicprincipalcomponentanalysisfeatureextractionmethodtoidentifypistonpinwearforbinaryclassifiermodeling AT xianliangliu ahybriddynamicprincipalcomponentanalysisfeatureextractionmethodtoidentifypistonpinwearforbinaryclassifiermodeling AT mingtaojia ahybriddynamicprincipalcomponentanalysisfeatureextractionmethodtoidentifypistonpinwearforbinaryclassifiermodeling AT ruizhang ahybriddynamicprincipalcomponentanalysisfeatureextractionmethodtoidentifypistonpinwearforbinaryclassifiermodeling AT haoyang hybriddynamicprincipalcomponentanalysisfeatureextractionmethodtoidentifypistonpinwearforbinaryclassifiermodeling AT yubinzhai hybriddynamicprincipalcomponentanalysisfeatureextractionmethodtoidentifypistonpinwearforbinaryclassifiermodeling AT mengkunzheng hybriddynamicprincipalcomponentanalysisfeatureextractionmethodtoidentifypistonpinwearforbinaryclassifiermodeling AT tanwang hybriddynamicprincipalcomponentanalysisfeatureextractionmethodtoidentifypistonpinwearforbinaryclassifiermodeling AT dongliangguo hybriddynamicprincipalcomponentanalysisfeatureextractionmethodtoidentifypistonpinwearforbinaryclassifiermodeling AT jianhuiliang hybriddynamicprincipalcomponentanalysisfeatureextractionmethodtoidentifypistonpinwearforbinaryclassifiermodeling AT xinchengli hybriddynamicprincipalcomponentanalysisfeatureextractionmethodtoidentifypistonpinwearforbinaryclassifiermodeling AT xianliangliu hybriddynamicprincipalcomponentanalysisfeatureextractionmethodtoidentifypistonpinwearforbinaryclassifiermodeling AT mingtaojia hybriddynamicprincipalcomponentanalysisfeatureextractionmethodtoidentifypistonpinwearforbinaryclassifiermodeling AT ruizhang hybriddynamicprincipalcomponentanalysisfeatureextractionmethodtoidentifypistonpinwearforbinaryclassifiermodeling |