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A demonstrator example for virtual sensing approaches in predictive maintenance applications

Predictive maintenance strategies are being developed in many sectors to prevent system failures, reduce downtimes and improve resource efficiency. An essential requirement for these approaches is the availability of failure related sensor data at critical components. When monitoring vehicle fleets, acceleration measurements are easily accessible, while extensive sensor setups for each vehicle are not economically feasible. Assuming transferrable system behavior between vehicles, virtual sensing techniques can be deployed to provide estimates for sensor data of interest using predictive data driven models.

The practical validation of predictive maintenance approaches is challenging, since the lifetime of many real components is too high to obtain feedback during the development…

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Description

The practical validation of predictive maintenance approaches is challenging, since the lifetime of many real components is too high to obtain feedback during the development process. In this contribution, a fatigue test bench demonstrator example is used to create a dataset for the development and benchmarking of virtual sensing approaches for predictive maintenance. To emulate individual user behavior, simple notched steel specimens are each subjected to unique service loads, which are derived from an existing dataset of e-bike measurements [1]. Two servo hydraulic cylinders are combined to control both force and position of the specimens in order to emulate measuring conditions in vehicles.

The resulting data is subsequently used to parameterize and validate two separate virtual sensing strategies. In the first approach [2], referred to as sensor estimation, the load history of the specimen is predicted from concurrent acceleration measurements through a machine learning model. These predictions are used to compute fatigue damage sums following the nominal stress concept, in order to predict the cycles until failure for each validation specimen. In the second approach [3], the acceleration data is transformed into a reduced data representation, which is used to directly predict fatigue damage sums. Both approaches are compared and their benefits and drawbacks are discussed.

Reference: VAL5-2024-049

Title: A demonstrator example for virtual sensing approaches in predictive maintenance applications
Author(s): L. Heindel, P. Hantschke, M. Kästner
DOI: 10.48447/VAL5-2024-049
Event: VAL5 - Fifth International Conference on Material and Component Performance under Variable Amplitude Loading
Year of publication: 2024
Publication type: conference paper (PDF)
Language: English
Keywords: virtual sensing,predictive maintenance,machine learning

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Author(s): M. Česnik, J. Slavič, M. Boltežar

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Author(s): M. Hupka, M. Wächter, A. Esderts

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Author(s): M. Palmieri, G. Morettini, G. Zucca, F. Cianetti, C. Braccesi

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A fatigue damage assessment approach in vibration fatigue exploiting the static approach

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English

€25.00

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Author(s): B. Spak, M. Schlicht, P. Froitzheim, W. Flügge, M. Kästner

Application of the Local Strain Approach to estimate fatigue life of clinched joints under constant and variable amplitude loading in different loading conditions

Publication type: conference paper (PDF)

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Online only

Author(s): J. D. Mattos, M. A. Arbelo

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Comparative Study of Crack Growth Retardation Models for an Integral Stiffened Panel Subjected to Overload.

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English

€25.00

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Author(s): T. Lecleve, S. Courtin, F. Szmytka, C. Mai

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Statistical method and associated non-linear cumulative model for fatigue nuclear applications

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Author(s): M. Lauf, S. Pruy, S. Kiesner, P. Hantschke, M. Kästner

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Computational fatigue assessment for welded copper joints against vibratory loads

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English

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Author(s): P. Schwarz, P. Häfele, T. Langlois

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Fatigue behaviour of specimen and rotor lamellas of non-grain oriented electrical steel under constant and variable amplitude loading

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English

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Author(s): A. Trapp, P. Wolfsteiner

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Non-stationary vibration loading - Assessing its effects on structural dynamics and vibration fatigue

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English

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Author(s): A. Halfpenny, S. Vervoort

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Dynamic fatigue analysis of a welded joints under sequential and simultaneous multi-axial random PSD loading

Publication type: conference paper (PDF)

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English

€25.00

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Author(s): J. Kraft, M. Vormwald

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Fatigue life estimation of notched components under multiaxial non-proportional loading with variable amplitudes.

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1

English

€25.00

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Author(s): D. Fräulin, A. Trapp, M. Hinz, P. Wolfsteiner

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Data-driven load spectrum estimator for non-stationary random vibration loading

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English

€25.00

Online only