A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis

• Published in: Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing Vol. 513; pp. 407 - 415
• Main Authors: Ademujimi, Toyosi Toriola, Brundage, Michael P., Prabhu, Vittaldas V.
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Series: IFIP Advances in Information and Communication Technology
• Subjects: Artificial intelligence; Fault Detection; Industrie 4.0; Intelligent maintenance; Machine learning; Manufacturing diagnosis
• ISBN: 3319669222; 9783319669229
• ISSN: 1868-4238; 1868-422X
• DOI: 10.1007/978-3-319-66923-6_48

Artificial intelligence applications are increasing due to advances in data collection systems, algorithms, and affordability of computing power. Within the manufacturing industry, machine learning algorithms are often used for improving manufacturing system fault diagnosis. This study focuses on a review of recent fault diagnosis applications in manufacturing that are based on several prominent machine learning algorithms. Papers published from 2007 to 2017 were reviewed and keywords were used to identify 20 articles spanning the most prominent machine learning algorithms. Most articles reviewed consisted of training data obtained from sensors attached to the equipment. The training of the machine learning algorithm consisted of designed experiments to simulate different faulty and normal processing conditions. The areas of application varied from wear of cutting tool in computer numeric control (CNC) machine, surface roughness fault, to wafer etching process in semiconductor manufacturing. In all cases, high fault classification rates were obtained. As the interest in smart manufacturing increases, this review serves to address one of the cornerstones of emerging production systems.