Event-Driven Dashboarding and Feedback for Improved Event Detection in Predictive Maintenance Applications

Manufacturers can plan predictive maintenance by remotely monitoring their assets. However, to extract the necessary insights from monitoring data, they often lack sufficiently large datasets that are labeled by human experts. We suggest combining knowledge-driven and unsupervised data-driven approa...

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Autores principales:	Pieter Moens, Sander Vanden Hautte, Dieter De Paepe, Bram Steenwinckel, Stijn Verstichel, Steven Vandekerckhove, Femke Ongenae, Sofie Van Hoecke
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	anomaly detection fault detection dynamic dashboards semantic reasoning user feedback Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/6e7a4804ec4c4853a019d2cb1e0ef682
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id	oai:doaj.org-article:6e7a4804ec4c4853a019d2cb1e0ef682
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spelling	oai:doaj.org-article:6e7a4804ec4c4853a019d2cb1e0ef6822021-11-11T15:23:50ZEvent-Driven Dashboarding and Feedback for Improved Event Detection in Predictive Maintenance Applications10.3390/app1121103712076-3417https://doaj.org/article/6e7a4804ec4c4853a019d2cb1e0ef6822021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10371https://doaj.org/toc/2076-3417Manufacturers can plan predictive maintenance by remotely monitoring their assets. However, to extract the necessary insights from monitoring data, they often lack sufficiently large datasets that are labeled by human experts. We suggest combining knowledge-driven and unsupervised data-driven approaches to tackle this issue. Additionally, we present a dynamic dashboard that automatically visualizes detected events using semantic reasoning, assisting experts in the revision and correction of event labels. Captured label corrections are immediately fed back to the adaptive event detectors, improving their performance. To the best of our knowledge, we are the first to demonstrate the synergy of knowledge-driven detectors, data-driven detectors and automatic dashboards capturing feedback. This synergy allows a transition from detecting only unlabeled events, such as anomalies, at the start to detecting labeled events, such as faults, with meaningful descriptions. We demonstrate all work using a ventilation unit monitoring use case. This approach enables manufacturers to collect labeled data for refining event classification techniques with reduced human labeling effort.Pieter MoensSander Vanden HautteDieter De PaepeBram SteenwinckelStijn VerstichelSteven VandekerckhoveFemke OngenaeSofie Van HoeckeMDPI AGarticleanomaly detectionfault detectiondynamic dashboardssemantic reasoninguser feedbackTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10371, p 10371 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	anomaly detection fault detection dynamic dashboards semantic reasoning user feedback Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	anomaly detection fault detection dynamic dashboards semantic reasoning user feedback Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Pieter Moens Sander Vanden Hautte Dieter De Paepe Bram Steenwinckel Stijn Verstichel Steven Vandekerckhove Femke Ongenae Sofie Van Hoecke Event-Driven Dashboarding and Feedback for Improved Event Detection in Predictive Maintenance Applications
description	Manufacturers can plan predictive maintenance by remotely monitoring their assets. However, to extract the necessary insights from monitoring data, they often lack sufficiently large datasets that are labeled by human experts. We suggest combining knowledge-driven and unsupervised data-driven approaches to tackle this issue. Additionally, we present a dynamic dashboard that automatically visualizes detected events using semantic reasoning, assisting experts in the revision and correction of event labels. Captured label corrections are immediately fed back to the adaptive event detectors, improving their performance. To the best of our knowledge, we are the first to demonstrate the synergy of knowledge-driven detectors, data-driven detectors and automatic dashboards capturing feedback. This synergy allows a transition from detecting only unlabeled events, such as anomalies, at the start to detecting labeled events, such as faults, with meaningful descriptions. We demonstrate all work using a ventilation unit monitoring use case. This approach enables manufacturers to collect labeled data for refining event classification techniques with reduced human labeling effort.
format	article
author	Pieter Moens Sander Vanden Hautte Dieter De Paepe Bram Steenwinckel Stijn Verstichel Steven Vandekerckhove Femke Ongenae Sofie Van Hoecke
author_facet	Pieter Moens Sander Vanden Hautte Dieter De Paepe Bram Steenwinckel Stijn Verstichel Steven Vandekerckhove Femke Ongenae Sofie Van Hoecke
author_sort	Pieter Moens
title	Event-Driven Dashboarding and Feedback for Improved Event Detection in Predictive Maintenance Applications
title_short	Event-Driven Dashboarding and Feedback for Improved Event Detection in Predictive Maintenance Applications
title_full	Event-Driven Dashboarding and Feedback for Improved Event Detection in Predictive Maintenance Applications
title_fullStr	Event-Driven Dashboarding and Feedback for Improved Event Detection in Predictive Maintenance Applications
title_full_unstemmed	Event-Driven Dashboarding and Feedback for Improved Event Detection in Predictive Maintenance Applications
title_sort	event-driven dashboarding and feedback for improved event detection in predictive maintenance applications
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6e7a4804ec4c4853a019d2cb1e0ef682
work_keys_str_mv	AT pietermoens eventdrivendashboardingandfeedbackforimprovedeventdetectioninpredictivemaintenanceapplications AT sandervandenhautte eventdrivendashboardingandfeedbackforimprovedeventdetectioninpredictivemaintenanceapplications AT dieterdepaepe eventdrivendashboardingandfeedbackforimprovedeventdetectioninpredictivemaintenanceapplications AT bramsteenwinckel eventdrivendashboardingandfeedbackforimprovedeventdetectioninpredictivemaintenanceapplications AT stijnverstichel eventdrivendashboardingandfeedbackforimprovedeventdetectioninpredictivemaintenanceapplications AT stevenvandekerckhove eventdrivendashboardingandfeedbackforimprovedeventdetectioninpredictivemaintenanceapplications AT femkeongenae eventdrivendashboardingandfeedbackforimprovedeventdetectioninpredictivemaintenanceapplications AT sofievanhoecke eventdrivendashboardingandfeedbackforimprovedeventdetectioninpredictivemaintenanceapplications
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Event-Driven Dashboarding and Feedback for Improved Event Detection in Predictive Maintenance Applications

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