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...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-11-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/21/10371 |
| _version_ | 1797512782225080320 |
|---|---|
| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T06:05:34Z |
| format | Article |
| id | doaj.art-6e7a4804ec4c4853a019d2cb1e0ef682 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T06:05:34Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-6e7a4804ec4c4853a019d2cb1e0ef6822023-11-22T20:31:49ZengMDPI AGApplied Sciences2076-34172021-11-0111211037110.3390/app112110371Event-Driven Dashboarding and Feedback for Improved Event Detection in Predictive Maintenance ApplicationsPieter Moens0Sander Vanden Hautte1Dieter De Paepe2Bram Steenwinckel3Stijn Verstichel4Steven Vandekerckhove5Femke Ongenae6Sofie Van Hoecke7IDLab, Ghent University—imec, 9052 Ghent, BelgiumIDLab, Ghent University—imec, 9052 Ghent, BelgiumIDLab, Ghent University—imec, 9052 Ghent, BelgiumIDLab, Ghent University—imec, 9052 Ghent, BelgiumIDLab, Ghent University—imec, 9052 Ghent, BelgiumRenson Ventilation, 8790 Waregem, BelgiumIDLab, Ghent University—imec, 9052 Ghent, BelgiumIDLab, Ghent University—imec, 9052 Ghent, BelgiumManufacturers 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.https://www.mdpi.com/2076-3417/11/21/10371anomaly detectionfault detectiondynamic dashboardssemantic reasoninguser feedback |
| spellingShingle | 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 Applied Sciences anomaly detection fault detection dynamic dashboards semantic reasoning user feedback |
| title | 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_short | 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 |
| topic | anomaly detection fault detection dynamic dashboards semantic reasoning user feedback |
| url | https://www.mdpi.com/2076-3417/11/21/10371 |
| 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 |