LearningADD: Machine learning based acoustic defect detection in factory automation. (July 2021)

• Record Type: Journal Article
• Authors: Zhang, Tao; Ding, Biyun; Zhao, Xin; Liu, Ganjun; Pang, Zhibo
• Is Part Of: Journal of manufacturing systems. Volume 60(2021)
• Page Start: 48
• Page End: 58
• Publication Date: 2021-07
• Subjects: Acoustic defect detection -- Edge computing -- Factory automation -- Feature extraction algorithm; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2021.04.005
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: Intelligent acoustic defect detection method based on machine learning. Feature Extraction based on the improved HHT and SFLA to improve detection accuracy. Deployment strategy based on parallel computing and edge computing to improve real-time performance. Effectiveness is experimentally validated with nontransparent glass bottle inspection. Abstract: Defect inspection of glass bottles in the beverage industrial is of significance to prevent unexpected losses caused by the damage of bottles during manufacturing and transporting. The commonly used manual methods suffer from inefficiency, excessive space consumption, and beverage wastes after filling. To replace the manual operations in the pre-filling detection with improved efficiency and reduced costs, this paper proposes a machine learning based Acoustic Defect Detection (LearningADD) system. Moreover, to realize scalable deployment on edge and cloud computing platforms, deployment strategies especially partitioning and allocation of functionalities need to be compared and optimized under realistic constraints such as latency, complexity, and capacity of the platforms. In particular, to distinguish the defects in glass bottles efficiently, the improved Hilbert-Huang transform (HHT) is employed to extend the extracted feature sets, and then Shuffled Frog Leaping Algorithm (SFLA) based feature selection is applied to optimize the feature sets. Five deployment strategies are quantitatively compared to optimize real-time performances based on the constraints measured from a real edge and cloud environment. The LearningADD algorithms are validated by the datasets from a real-life beverage factory, and the F-measure of the system reaches 98.48 %. The proposed deployment strategies are verified by experiments on private cloud platforms, which shows that the Distributed Heavy Edge deployment outperforms other strategies, benefited from the parallel computing and edge computing, where the Defect Detection Time for one bottle is less than 2.061 s in 99 % probability.