Time-series pattern recognition in Smart Manufacturing Systems: A literature review and ontology

被引：28

作者：

Farahani, Mojtaba A. ^{[1
]}

McCormick, M. R. ^{[1
]}

Gianinny, Robert ^{[1
]}

Hudacheck, Frank ^{[1
]}

Harik, Ramy ^{[2
]}

Liu, Zhichao ^{[1
]}

Wuest, Thorsten ^{[1
]}

机构：

[1] West Virginia Univ, Morgantown, WV 26505 USA

[2] Univ South Carolina, Columbia, SC 29208 USA

来源：

JOURNAL OF MANUFACTURING SYSTEMS | 2023年 / 69卷

基金：

美国国家科学基金会;

关键词：

Industry; 4; 0; Artificial Intelligence (AI); Machine Learning (ML); Time-series pattern recognition; Smart Manufacturing; CONVOLUTIONAL NEURAL-NETWORK; FAULT-DETECTION; DIAGNOSIS; CLASSIFICATION;

D O I：

10.1016/j.jmsy.2023.05.025

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Since the inception of Industry 4.0 in 2012, emerging technologies have enabled the acquisition of vast amounts of data from diverse sources such as machine tools, robust and affordable sensor systems with advanced information models, and other sources within Smart Manufacturing Systems (SMS). As a result, the amount of data that is available in manufacturing settings has exploded, allowing data-hungry tools such as Artificial Intelligence (AI) and Machine Learning (ML) to be leveraged. Time-series analytics has been successfully applied in a variety of industries, and that success is now being migrated to pattern recognition applications in manufacturing to support higher quality products, zero defect manufacturing, and improved customer satisfaction. However, the diverse landscape of manufacturing presents a challenge for successfully solving problems in industry using time-series pattern recognition. The resulting research gap of understanding and applying the subject matter of time-series pattern recognition in manufacturing is a major limiting factor for adoption in industry. The purpose of this paper is to provide a structured perspective of the current state of time-series pattern recognition in manufacturing with a problem-solving focus. By using an ontology to classify and define concepts, how they are structured, their properties, the relationships between them, and considerations when applying them, this paper aims to provide practical and actionable guidelines for application and recommendations for advancing time-series analytics.

引用

页码：208 / 241

页数：34

共 162 条

[1] An individualized system of skeletal data-based CNN classifiers for action recognition in manufacturing assembly [J].