A Machine Learning Based Health Indicator Construction in Implementing Predictive Maintenance: A Real World Industrial Application from Manufacturing

Autor:	Ebru Turanouglu Bekar, Harshad Kurrewar, Per Nyqvist, Anders Skoogh
Rok vydání:	2021
Předmět:	Computer science business.industry Business value USable Machine learning computer.software_genre Health indicator Predictive maintenance Task (project management) Manufacturing Sustainability Artificial intelligence Business case business computer
Zdroj:	Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems ISBN: 9783030859053 APMS (3)
DOI:	10.1007/978-3-030-85906-0_65
Popis:	Predictive maintenance (PdM) using Machine learning (ML) is a top-rated business case with respect to the availability of data and potential business value for future sustainability and competitiveness in the manufacturing industry. However, applying ML within actual industrial practice of PdM is a complex and challenging task due to high dimensionality and lack of labeled data. To cope with this challenge, this paper presents a systematic framework based on an unsupervised ML approach by aiming to construct health indicators, which has a crucial impact on making the data meaningful and usable for monitoring machine performance (health) in PdM applications. The results are presented by using real-world industrial data coming from a manufacturing company. In conclusion, the designed health indicators can be used to monitor machine performance over time and further be used in a supervised setting for the purpose of prognostic like remaining useful life estimation in implementing PdM in the industry.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::ead131c72972e81d68a28020e8aff6cc https://doi.org/10.1007/978-3-030-85906-0_65 Zobrazit plný text záznamu