Fault detection in mixture production process based on wavelet packet and support vector machine

• Published in: Journal of intelligent & fuzzy systems Vol. 40; no. 5; pp. 10235 - 10249
• Main Authors: Chen, Yan, song, Huan-sheng, yang, Yan-ni, wang, Gang-feng
• Format: Journal Article
• Language: English
• Published: Amsterdam IOS Press BV 01.01.2021
• Subjects: Algorithms; Asphalt; Basis functions; Chi-square test; Classification; Decomposition; Energy value; Evolutionary algorithms; Evolutionary computation; Fault detection; Feature extraction; Feature selection; Highway construction; Mathematical analysis; Optimization; Real time; Road construction; Statistical tests; Support vector machines
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-201803

Mixture production equipment is widely employed in road construction, and the quality of the produced mixture is the essential factor to ensure the quality of road construction. To detect the quality of the real-time produced mixture and solve the shortcomings of laboratory detection lag, a new fault detection method in the mixture production process is proposed, which is based on wavelet packet decomposition (WPD) and support vector machine (SVM). The proposed scheme includes feature extraction, feature selection, SVM classification, and optimization algorithm. During feature extraction, wavelet basis function is utilized to 4-layer decompose the aggregate and asphalt data mixed in real-time. The energy value calculated by wavelet packet coefficient is the extracted feature. During feature selection, a method combining the chi-square test and wrapper (CSW) is conducted to select the optimal feature subset from WPD features. Eventually, by adopting the optimal feature subset, SVM has been developed to classify various faults. Its parameters are optimized by differential evolution (DE) algorithm. In the test stage, multiple faults of different specifications of aggregates and asphalt are detected in the mixture production process. The results demonstrate that (1) accuracy produced by the CSW method with WPD features is 4.33% higher than the PCA method with statistical features; (2) SVM classification method optimized by DE algorithm brings an increase in recognition accuracy of identifying different types of mixture production faults produced by different equipment. Compared to other available methods, the proposed algorithm has a very outstanding detection performance.