A syntactic pattern recognition based approach to online anomaly detection and identification on electric motors - TÜMER, MUSTAFA BORAHAN (My University, 2022)
Online anomaly detection and identification is a major task of many Industry 4.0 applications. Electric motors, being one of the most crucial parts of many products, are subjected to end-of-line tests to pick up faulty ones before being mounted to other devices. With this study, we propose a Syntactic Pattern Recognition based approach to online anomaly detection and identification on electric motors. Utilizing Variable Order Markov Models and Probabilistic Suffix Trees, we apply both unsupervised and supervised approaches to cluster motor conditions and diagnose them. Besides being explainable, the diagnosis method we propose is completely online and suitable for parallel computing, which makes it a favorable method to use synchronously with a physical test system. We evaluate the proposed method on a benchmark dataset and on a case study, which is being worked on within the scope of a European Union funded research project on reliability.
Keywords: Tıp; Bilgisayar Bilimleri; Algoritmalar; Sağlık Bilimleri; Mühendislik ve Teknoloji; Medicine; Computer Sciences; algorithms; Health Sciences; Engineering and Technology; BİLGİSAYAR BİLİMİ, YAPAY ZEKA; Bilgisayar Bilimi; Mühendislik, Bilişim ve Teknoloji (ENG); GÖRÜNTÜLEME BİLİMİ VE FOTOĞRAF TEKNOLOJİSİ; Klinik Tıp; Klinik Tıp (MED); COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; Engineering, Computing & Technology (ENG); IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY; CLINICAL MEDICINE; Clinical Medicine (MED); Bilgisayarla Görme ve Örüntü Tanıma; Bilgisayar Bilimi Uygulamaları; Yapay Zeka; Bilgisayar Bilimi (çeşitli); Genel Bilgisayar Bilimi; Fizik Bilimleri; Computer Vision and Pattern Recognition; Computer Science Applications; Artificial Intelligence; Computer Science (miscellaneous); General Computer Science; Physical Sciences; Syntactic Pattern Recognition; Variable Order Markov Models; Probabilistic Suffix Trees; Anomaly detection; Diagnosis; INTELLIGENT FAULT-DIAGNOSIS; INDUCTION-MOTORS; NETWORK; METHODOLOGY; VIBRATION; TREE