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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): E. Schäfer, V. Chmelko, M. Kučerková

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Author(s): A. Trapp, K. Schneider, K. Rother, P. Wolfsteiner

A critical plane criterion for non-stationary random vibration loading applicable in a statistical fatigue assessment

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VAL5 Fifth International Conference on Material and Component Performance under Variable Amplitude Loading

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

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

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

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Author(s): L. Solazzi, A. Mazzoni, N. Danzi, M. Cima

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Author(s): K. F. Walker, J. C. Newman

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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

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Author(s): J. Riebe, P. Hantschke, A. Griesing, M. Kästner

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Automatic operating state identification from load time series through unsupervised learning and fatigue damage regression

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Author(s): P.M. Kyaw, M. Braun

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

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Author(s): C. Fällgren, M. Vormwald

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Author(s): G. Curti, T. Dengler, M. Palmieri, A. Trapp, P. Wolfsteiner, F. Cianetti, K. Schneider

Modal driven load tailoring for efficient vibration fatigue assessment on non-stationary processes: A versatile approach exploiting the Fatigue Damage Spectrum

Publication type: conference paper (PDF)

English

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