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

Ebru Turanoglu Bekar

Chalmers, Industri- och materialvetenskap, Produktionssystem

Anders Skoogh

Chalmers, Industri- och materialvetenskap, Produktionssystem

Per Nyqvist

Chalmers, Industri- och materialvetenskap, Produktionssystem

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 och fabriksdesIgn Testbed (SUMMIT)

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


Produktionsteknik, arbetsvetenskap och ergonomi

Annan maskinteknik

Tillförlitlighets- och kvalitetsteknik



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