Predictive maintenance demonstrator dataset with individual load histories
Documentation of the data | A thorough documentation of this dataset, including experimental setup, creation of the signals, postprocessing and data access is provided in the textfile dataset_description.txt in the dataset. | |
Countries to which the data refer | GERMANY | |
References to related material | http://dx.doi.org/10.25532/OPARA-189 | |
Description of the data | The research field of predictive maintenance aims to develop methods that are capable of predicting component failure before it occurs. This way, maintenance can be conducted with the component is still intact, while at the same time increasing resource efficiency by exploiting the fatigue strength of the component. In order to enable predictive maintenance, sensor data of the component is required, from which the predictions can be generated. Furthermore, the individual loads acting on each component of a system depend on the operating conditions of the systems and are therefore different between different systems. Failure therefore occurs as a result of individual load histories. Virtual sensing methods aim to predict unmeasured physical quantities from available measurement data. These methods offer significant benefits to predictive maintenance, since virtual sensors can be used to estimate quantities that are difficult to measure. In fleet situations, where many identical systems with different usage histories are monitored, virtual sensors can also be deployed as a replacement for some sensors of the system. This can be done in a data-driven manner by installing sensors for all quantities of interest at one reference system, whose measurement data is used to parameterize the virtual sensor model, so that it can then be deployed for the remaining standard systems. In many real applications, the time to failure is in the range of years, complicating the development and validation of predictive maintenance and virtual sensing approaches. The aim of this dataset is therefore to provide a demonstrator example where failure occurs based on individual load histories. Its sensor setup is designed to provide a virtual sensor use case with independent training and testing data, so that the dataset can be used for algorithm development and benchmarking purposes. | |
Type of the data | Dataset | |
Total size of the dataset | 2239844090 | |
Author | Heindel, Leonhard | |
Author | Hantschke, Peter | |
Author | Kästner, Markus | |
Upload date | 2025-04-14T15:27:54Z | |
Publication date | 2025-04-14T15:27:54Z | |
Data of data creation | 2025-04-04 | |
Publication date | 2025-04-14 | |
Abstract of the dataset | Predictive maintenance aims to develop methods that are capable of predicting component failure before it occurs. Virtual sensing methods predict unmeasured physical quantities from available measurement data. These methods offer significant benefits to predictive maintenance, since virtual sensors can be used to estimate quantities that are difficult to measure. In many real applications, the time to failure is in the range of years, complicating the development and validation of predictive maintenance and virtual sensing approaches. This dataset provides a demonstrator example where failure occurs based on individual load histories. The sensor setup consists of simple notched steel specimens, which are clamped between two servo-hydraulic cylinders of a fatigue test bench. It is designed to provide a virtual sensor use case with independent training and testing data, so that the dataset can be used for algorithm development and benchmarking purposes. | |
Public reference to this page | https://opara.zih.tu-dresden.de/handle/123456789/1427 | |
Public reference to this page | https://doi.org/10.25532/OPARA-812 | |
Publisher | Technische Universität Dresden | |
Licence | Attribution 4.0 International | en |
URI of the licence text | http://creativecommons.org/licenses/by/4.0/ | |
Specification of the discipline(s) | 4 | |
Title of the dataset | Predictive maintenance demonstrator dataset with individual load histories | |
Research instruments | servo-hydraulic fatigue test bench | |
Research instruments | acceleration sensors | |
Research instruments | force sensors | |
Research instruments | displacement sensors | |
opara.descriptionObject | virtual sensing | |
opara.descriptionObject | fatigue | |
Underlying research object | predictive maintenance | |
Software | Python | |
Project abstract | The aim of the project is to increase the availability of a tramcar fleet by evaluating standard sensors that are available in every tramcar. Insights into the condition of tramcar and track enable target-oriented fleet usage and reduce follow-up maintenance costs. The increase in rail reliability increases the acceptance of public transport among the population. Financial advantages arise for operators and users. | |
Public project website(s) | https://tu-dresden.de/ing/maschinenwesen/ifkm/nefm/forschung/projekte/LRVTwin | |
Project title | LRVTwin - A digital twin for light rail vehicles |
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