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A Fleet-Wide Approach for Condition Monitoring of Similar Machines Using Time-Series Clustering

  • Kilian HendrickxEmail author
  • Wannes Meert
  • Bram Cornelis
  • Karl Janssens
  • Konstantinos Gryllias
  • Jesse Davis
Conference paper
Part of the Applied Condition Monitoring book series (ACM, volume 15)

Abstract

The application of machine learning to fault diagnosis allows automated condition monitoring of machines, leading to reduced maintenance costs and increased machine availability. Traditional approaches train a machine learning algorithm to identify specific faults or operational settings. Therefore, these approaches cannot always cope with a dynamic industrial environment. However, an industrial installation often contains multiple machines of the same type, which enables a fleet-based analysis. This type of analysis compares machines to tackle the challenges of a dynamic environment. In this paper a novel method is proposed for analyzing a fleet of machines operating under similar conditions in the same area by using inter-machine comparisons. The proposed methodology consists of two steps. First, the inter-machine difference is calculated using dynamic time warping by using the amount of warping as measure. This method allows comparing the measured signals even when fluctuations are present. Second, a clustering method uses the inter-machine similarity to identify groups of machines that operate in a similar manner. The generation of a fault usually causes a change in the raw signals and diagnostic features. As a result, the inter-machine difference between the faulty machine and the rest of the fleet will increase, leading to the creation of a separate group that contains the faulty machine. The methodology is evaluated and validated on phase current signals measured on a fleet of electrical drivetrains, where a phase unbalance fault is introduced in some of the drivetrains for a specific time duration.

Keywords

Condition monitoring Fleet monitoring Dynamic time warping Clustering Phase unbalance 

Notes

Acknowledgments

The authors acknowledge the financial support of VLAIO (Flemish Innovation & Entrepreneurship) through the Baekeland PhD mandate (nr. HBC.2017.0226) and the O&O project REFLEXION (nr. IWT. 150334). Jesse Davis is partially support by the KU Leuven research funds (C14/17/070). The drivetrain fleet data was obtained in the laboratory of the ULB Beams group (http://www.beams.ulb.ac.be) with the support of Dr. Yves Mollet and Prof. Dr. Johan Gyselinck.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Kilian Hendrickx
    • 1
    • 2
    • 3
    Email author
  • Wannes Meert
    • 2
  • Bram Cornelis
    • 1
  • Karl Janssens
    • 1
  • Konstantinos Gryllias
    • 3
    • 4
  • Jesse Davis
    • 2
  1. 1.Siemens Industry Software NVLeuvenBelgium
  2. 2.Department of Computer ScienceKU LeuvenLeuvenBelgium
  3. 3.Division PMA, Department of Mechanical EngineeringKU LeuvenLeuvenBelgium
  4. 4.Dynamics of Mechanical and Mechatronic Systems, Flanders MakeLeuvenBelgium

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