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

Abstract

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.

MEDEP: Maintenance Event Detection for Multivariate Time Series Based on the PELT Approach

“…Based on this observation, several researchers proposed technical approaches for event detection to overcome the missing human feedback as a major barrier of introducing PdM. The collected sensor data can serve the purpose of anomaly detection in general, defect detection [9,10,19], failure detection [11,12], or maintenance events detection [7] in particular. Event detection for specific components in large machines is challenging due to the high degree of complexity inherent to the large number of components and environmental factors influencing the health state of the machines and their components [1].…”

“…The knowledge acquired in fault and defect event detection models is mostly used as input for maintenance decision making. Nevertheless, anomaly detection for maintenance event detection has been receiving more attention recently [7,8,29]. In this context, research mostly focuses on the detection of abnormal patterns using sensor data complemented by a human-in-the-loop setup to validate the detection results.…”

“…In this context, research mostly focuses on the detection of abnormal patterns using sensor data complemented by a human-in-the-loop setup to validate the detection results. For instance, Moens et al [7] introduce an interactive dashboard for event detection in sensor data. This approach is based on a matrix profile as its motif discovery technique and requires human feedback or intervention to label correct maintenance events.…”

“…This is often neglected due to time constraints. Consequently, maintenance data are often incomplete or even completely missing [7,8]. One way to tackle the challenge of missing or incomplete maintenance documentation is to automatically detect the maintenance events based on their manifestation in monitoring data like time series collected from sensors in the machines.…”

“…One way to tackle the challenge of missing or incomplete maintenance documentation is to automatically detect the maintenance events based on their manifestation in monitoring data like time series collected from sensors in the machines. Anomaly detection approaches are suitable for these event detection tasks and can be found in many topics within manufacturing including defect detection [9,10], fault detection [11,12], or maintenance event detection [7]. This research aims to automatically detect performed maintenance actions in monitoring data to create comprehensive data-sets.…”