Citation: Gashi, M.; Gursch, H.;
Hinterbichler, H.; Pichler, S.;
Lindstaedt, S.; Thalmann, S. MEDEP:
Maintenance Event Detection for
Multivariate Time Series Based on
PELT Approach. Sensors 2022, 22,
2837. https://doi.org/10.3390/
s22082837
Academic Editors: Hamed Badihi,
Tao Chen and Ningyun Lu
Received: 21 February 2022
Accepted: 4 April 2022
Published: 7 April 2022
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Article
MEDEP: Maintenance Event Detection for Multivariate Time
Series Based on the PELT Approach
Milot Gashi
1,
* , Heimo Gursch
2
, Hannes Hinterbichler
3
, Stefan Pichler
3
, Stefanie Lindstaedt
2
and Stefan Thalmann
4,
*
1
Pro2Future GmbH, 4040 Linz, Austria
2
Know-Center GmbH, 8010 Graz, Austria; hgursch@know-center.at (H.G.); lindstaedt@tugraz.at (S.L.)
3
Fronius International, 4643 Pettenbach, Austria; hinterbichler.hannes@fronius.com (H.H.);
pichler.stefan@fronius.com (S.P.)
4
Business Analytics and Data Science Center, University of Graz, 8010 Graz, Austria
* Correspondence: milot.gashi@pro2future.at (M.G.); stefan.thalmann@uni-graz.at (S.T.)
Abstract:
Predictive Maintenance (PdM) is one of the most important applications of advanced data
science in Industry 4.0, aiming to facilitate manufacturing processes. To build PdM models, sufficient
data, such as condition monitoring and maintenance data of the industrial application, are required.
However, collecting maintenance data is complex and challenging as it requires human involvement
and expertise. Due to time constraints, motivating workers to provide comprehensive labeled data is
very challenging, and thus maintenance data are mostly incomplete or even completely missing. In
addition to these aspects, a lot of condition monitoring data-sets exist, but only very few labeled small
maintenance data-sets can be found. Hence, our proposed solution can provide additional labels and
offer new research possibilities for these data-sets. To address this challenge, we introduce MEDEP,
a novel maintenance event detection framework based on the Pruned Exact Linear Time (PELT)
approach, promising a low false-positive (FP) rate and high accuracy results in general. MEDEP
could help to automatically detect performed maintenance events from the deviations in the condition
monitoring data. A heuristic method is proposed as an extension to the PELT approach consisting of
the following two steps: (1) mean threshold for multivariate time series and (2) distribution threshold
analysis based on the complexity-invariant metric. We validate and compare MEDEP on the Microsoft
Azure Predictive Maintenance data-set and data from a real-world use case in the welding industry.
The experimental outcomes of the proposed approach resulted in a superior performance with an FP
rate of around 10% on average and high sensitivity and accuracy results.
Keywords:
event detection; welding industry; predictive maintenance; maintenance event detection;
change point detection
1. Introduction
Predictive Maintenance (PdM) is one of the most prominent industrial applications of
data-driven technologies and key to the smart manufacturing concepts, promising many
benefits such as optimized maintenance scheduling, resource optimization, and improved
decision support [
1
]. PdM models are typically used to predict future failures due to the
wearing out of components and thus provide the opportunity to perform maintenance
proactively. The main reasons for the interest of researchers and industry alike in PdM
in recent years are the relevance and influence of maintenance on production cost and
quality [
2
], the increased information base due to the availability of cheap and powerful
sensor technology [
3
], and huge advances in artificial intelligence (AI) [
4
]. In general,
maintenance costs are an aspect that make up the majority of operating costs and can
vary between 15% and 60% depending on the type of industry [
5
]. Consequently, PdM
helps to reduce maintenance costs without increasing the risk of downtimes. For instance,
Sensors 2022, 22, 2837. https://doi.org/10.3390/s22082837 https://www.mdpi.com/journal/sensors
