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

2021 ◽  
Vol 11 (21) ◽  
pp. 10371
Author(s):  
Pieter Moens ◽  
Sander Vanden Hautte ◽  
Dieter De Paepe ◽  
Bram Steenwinckel ◽  
Stijn Verstichel ◽  
...  
Keyword(s):  
Event Detection ◽  
Large Datasets ◽  
Predictive Maintenance ◽  
Data Driven ◽  
Monitoring Data ◽  
Use Case ◽  
Semantic Reasoning ◽  
Classification Techniques ◽  
Event Classification ◽  
Event Driven

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.

A Lesson on Operationalizing Machine Learning for Predictive Maintenance of Gas Turbines

2021 ◽  
Author(s):  
Paolo Pileggi ◽  
Elena Lazovik ◽  
Ron Snijders ◽  
Lars-Uno Axelsson ◽  
Sietse Drost ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gas Turbines ◽  
Service Providers ◽  
Black Box ◽  
Careful Consideration ◽  
Knowledge Systems ◽  
Predictive Maintenance ◽  
Data Driven ◽  
Use Case ◽  
Digital Twin

Abstract OEMs, service providers and end-users are moving from preventative to predictive maintenance to minimize the risk of unwanted power plant shut-downs and to maximize profitability. Digital Twin and Machine Learning (ML) are important techniques in this transformation as it complements and improves the traditional expert-based knowledge systems. There is a continued trend to use data-driven, so-called black-box, ML techniques as an improvement over traditional statistical approaches. However, these ML approaches suffer from low interpretability or explainability, making it hard to trust how or why a certain anomaly in the system is detected, limiting the trust in the prediction and making it much less likely to identify the real original cause of the problem. In this paper, we present our lesson learnt from operationalizing ML in a real-world use case that studied data from the 1.85 MWe OPRA OP16 all radial single-shaft gas turbine. We comment on the unforeseen obstacles we uncovered during our ML anomaly detection application and juxtapose them with the high potential value that our novel ML applications and explanation method can provide. ML may be enticing for the predictive maintenance of gas turbines but our lesson makes it clear that operationalizing ML goes beyond merely algorithm specifics. In line with the nature of the Digital Twin, it requires careful consideration of the specialized IT system supporting the algorithm, and the specific process it supports and in which it is deployed.

Data-driven strategies for predictive maintenance: Lesson learned from an automotive use case

2022 ◽  
Vol 134 ◽  
pp. 103554
Author(s):  
Danilo Giordano ◽  
Flavio Giobergia ◽  
Eliana Pastor ◽  
Antonio La Macchia ◽  
Tania Cerquitelli ◽  
...  
Keyword(s):  
Predictive Maintenance ◽  
Data Driven ◽  
Use Case

A Moroccan Leading Use Case for Predictive Maintenance, IoT and Industry 4.0

2020 ◽  
Author(s):  
Abdenour Jbili ◽  
Mounir Lahlimi
Keyword(s):  
Industry 4.0 ◽  
Predictive Maintenance ◽  
Use Case

Using Monitoring Data to Improve HPC Performance via Network-Data-Driven Allocation

2021 ◽  
Author(s):  
Yijia Zhang ◽  
Burak Aksar ◽  
Omar Aaziz ◽  
Benjamin Schwaller ◽  
Jim Brandt ◽  
...  
Keyword(s):  
Data Driven ◽  
Monitoring Data ◽  
Network Data

scholarly journals Analysis for full face mechanical behaviors through spatial deduction model with real-time monitoring data

2021 ◽  
pp. 147592172110448
Author(s):  
Xuyan Tan ◽  
Yuhang Wang ◽  
Bowen Du ◽  
Junchen Ye ◽  
Weizhong Chen ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Mechanical Analysis ◽  
Data Driven ◽  
Monitoring Data ◽  
Tunnel Structure ◽  
Mechanical Behaviors ◽  
Proposed Model ◽  
Full Face ◽  
Cross Test ◽  
Mechanical Constraint

Mechanical analysis for the full face of tunnel structure is crucial to maintain stability, which is a challenge in classical analytical solutions and data analysis. Along this line, this study aims to develop a spatial deduction model to obtain the full-faced mechanical behaviors through integrating mechanical properties into pure data-driven model. The spatial tunnel structure is divided into many parts and reconstructed in a form of matrix. Then, the external load applied on structure in the field was considered to study the mechanical behaviors of tunnel. Based on the limited observed monitoring data in matrix and mechanical analysis results, a double-driven model was developed to obtain the full-faced information, in which the data-driven model was the dominant one and the mechanical constraint was the secondary one. To verify the presented spatial deduction model, cross-test was conducted through assuming partial monitoring data are unknown and regarding them as testing points. The well agreement between deduction results with actual monitoring results means the proposed model is reasonable. Therefore, it was employed to deduct both the current and historical performance of tunnel full face, which is crucial to prevent structural disasters.

Data-Driven Predictive Maintenance: A Methodology Primer

2021 ◽  
pp. 39-73
Author(s):  
Tania Cerquitelli ◽  
Nikolaos Nikolakis ◽  
Lia Morra ◽  
Andrea Bellagarda ◽  
Matteo Orlando ◽  
...  
Keyword(s):  
Predictive Maintenance ◽  
Data Driven

Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case

2020 ◽  
Vol 54 ◽  
pp. 138-151 ◽  
Author(s):  
Radhya Sahal ◽  
John G. Breslin ◽  
Muhammad Intizar Ali
Keyword(s):  
Big Data ◽  
Industry 4.0 ◽  
Stream Processing ◽  
Predictive Maintenance ◽  
Use Case
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