A Machine Learning Based Health Indicator Construction in Implementing Predictive Maintenance: A Real World Industrial Application from Manufacturing
Paper in proceeding, 2021

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.

Feature selection and fusion

Smart maintenance

Machine learning

Predictive maintenance

Real world industrial data

Health assessment


Harshad Kurrewar

Student at Chalmers

Ebru Turanoglu Bekar

Chalmers, Industrial and Materials Science, Production Systems

Anders Skoogh

Chalmers, Industrial and Materials Science, Production Systems

Per Nyqvist

Chalmers, Industrial and Materials Science, Production Systems

IFIP Advances in Information and Communication Technology

1868-4238 (ISSN) 1868-422X (eISSN)

Vol. 632 IFIP 599-608
9783030859053 (ISBN)

IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2021
Nantes, France,

SUstainability, sMart Maintenance and factory design Testbed (SUMMIT)

VINNOVA (2017-04773), 2017-11-01 -- 2021-04-30.

Subject Categories

Production Engineering, Human Work Science and Ergonomics

Other Mechanical Engineering

Reliability and Maintenance



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