Smart Machine Maintenance Enabled by a Condition Monitoring Living Lab

Ooijevaar, T.H.; Pichler, Kurt; Di, Yuan; Devos, Steven; Volckaert, Bruno; Hoecke, Sofie Van; Hesch, Clemens

doi:10.1016/j.ifacol.2019.11.704

Cited by 8 publications

(17 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Smart Maintenance Living Lab (SMLL) is an open test research platform that aims to support the adoption of condition monitoring technologies [5]. The platform consists of a fleet of seven identical drivetrain setups that perform accelerated lifetime tests on bearings.…”

Section: Smart Maintenance Living Lab Datasetmentioning

confidence: 99%

“…The fleet offers two advantages: first, it allows faster data collection; second, identical drivetrain systems can have variability. Therefore, it offers the opportunity for training and evaluating robust models [5,27].…”

Section: Smart Maintenance Living Lab Datasetmentioning

confidence: 99%

“…This paper uses the bearing benchmarks from two test setups, namely the Pronostia setup by the FEMTO-ST institute [4] and the Smart Maintenance Living Lab by Flanders Make and imec [5]. Both test setups emerge from the necessity of having platforms to conduct bearing experiments, generate degradation data under different conditions, and validate prognostic models.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Semi-Supervised Approach with Monotonic Constraints for Improved Remaining Useful Life Estimation

Avendano

Vandermoortele

Soete

et al. 2022

Sensors

View full text Add to dashboard Cite

Remaining useful life is of great value in the industry and is a key component of Prognostics and Health Management (PHM) in the context of the Predictive Maintenance (PdM) strategy. Accurate estimation of the remaining useful life (RUL) is helpful for optimizing maintenance schedules, obtaining insights into the component degradation, and avoiding unexpected breakdowns. This paper presents a methodology for creating health index models with monotonicity in a semi-supervised approach. The health indexes are then used for enhancing remaining useful life estimation models. The methodology is evaluated on two bearing datasets. Results demonstrate the advantage of using the monotonic health index for obtaining insights into the bearing degradation and for remaining useful life estimation.

show abstract

Section: Smart Maintenance Living Lab Datasetmentioning

confidence: 99%

Section: Smart Maintenance Living Lab Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Semi-Supervised Approach with Monotonic Constraints for Improved Remaining Useful Life Estimation

Avendano

Vandermoortele

Soete

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…It was already apparent from the challenge that different operating conditions resulted in varying machine learning models. However, early results from the collected data also indicate a variability between different drivetrain subsystems [13]. Therefore, the data collected by the Smart Maintenance Living Lab on a fleet of seven machines can result in a more reliable data set that enables the generalization of algorithms created for smart maintenance use cases.…”

Section: Related Workmentioning

confidence: 99%

“…As mentioned in Section 2, the edge tier consists of seven identical drivetrain systems [13]. A single setup comprises of a shaft with the test bearing, lubricated by an internal oil bath, and one support bearing on each side.…”

Section: Drivetrain Systemsmentioning

confidence: 99%

Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications

Moens

Bracke

Soete

et al. 2020

Sensors

View full text Add to dashboard Cite

The wide adoption of smart machine maintenance in manufacturing is blocked by open challenges in the Industrial Internet of Things (IIoT) with regard to robustness, scalability and security. Solving these challenges is of uttermost importance to mission-critical industrial operations. Furthermore, effective application of predictive maintenance requires well-trained machine learning algorithms which on their turn require high volumes of reliable data. This paper addresses both challenges and presents the Smart Maintenance Living Lab, an open test and research platform that consists of a fleet of drivetrain systems for accelerated lifetime tests of rolling-element bearings, a scalable IoT middleware cloud platform for reliable data ingestion and persistence, and a dynamic dashboard application for fleet monitoring and visualization. Each individual component within the presented system is discussed and validated, demonstrating the feasibility of IIoT applications for smart machine maintenance. The resulting platform provides benchmark data for the improvement of machine learning algorithms, gives insights into the design, implementation and validation of a complete architecture for IIoT applications with specific requirements concerning robustness, scalability and security and therefore reduces the reticence in the industry to widely adopt these technologies.

show abstract

Systematic Methodology for Generating Natural Spall Faults on Rolling Element Bearings

Ompusunggu

2023

Lecture Notes in Mechanical Engineering

View full text Add to dashboard Cite

Smart Machine Maintenance Enabled by a Condition Monitoring Living Lab

Cited by 8 publications

References 7 publications

A Semi-Supervised Approach with Monotonic Constraints for Improved Remaining Useful Life Estimation

A Semi-Supervised Approach with Monotonic Constraints for Improved Remaining Useful Life Estimation

Scalable Fleet Monitoring and Visualization for Smart Machine Maintenance and Industrial IoT Applications

Systematic Methodology for Generating Natural Spall Faults on Rolling Element Bearings

Contact Info

Product

Resources

About