About: Anomaly detection methods in turning based on motor data analysis

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Anomaly detection methods in turning based on motor data analysis
Creator	Onozuka, Hideaki Kono, Ippei Watanabe, Tsubasa
source	Elsevier
abstract	Abstract A cutting anomaly can be detected by using machine learning and pattern recognition in addition to the conventional method of using cutting knowledge to determine the criteria of detection. However, it is difficult to guarantee that all cutting anomalies can be detected during mass production due to various events that can occur. Moreover, machine learning and pattern recognition are not systemized for use in mass production. In this work, we investigated the detection of turning anomalies during mass production under optimized cutting conditions. We applied a method that utilizes the motor current of each operating axis to monitor the machining state without affecting the machining process and determines the correlation between turning anomalies and motor data. Our target was the unsteady anomalies that appear in mass production, such as chip biting and tool vibration. On the basis of the obtained correlation, we developed a formalized anomaly detection method using traditional statistics and a systemized anomaly detection method using discretization and pattern recognition based on the Mahalanobis Taguchi method with auto parameter tuning to eliminate the need for detailed analysis based on knowledge of cutting phenomena. Both methods achieved a detection accuracy of over 98%.
has issue date	2020-12-31(xsd:dateTime)
bibo:doi	10.1016/j.promfg.2020.05.126
has license	els-covid
sha1sum (hex)	e54f7660f9f086558c4b53ad25f39769e03e8b94
schema:url	https://doi.org/10.1016/j.promfg.2020.05.126
resource representing a document's title	Anomaly detection methods in turning based on motor data analysis
schema:publication	Procedia Manufacturing
resource representing a document's body	covid:e54f7660f9f086558c4b53ad25f39769e03e8b94#body_text
is schema:about of	named entity 'addition' named entity 'anomalies' named entity 'investigated' named entity 'detected' named entity 'tool' »more»

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