A Metric and Visualization of Completeness in Multi-Dimensional Data Sets of Sensor and Actuator Data Applied to a Condition Monitoring Use Case

Clicks: 180
ID: 273725
2021
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Article Quality & Performance Metrics
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Abstract
The so-called ‘Industrie 4.0’ provides high potential for data-driven methods in automated production systems. However, sensor and actuator data gathered during normal operation of the system is often limited to a narrow range of single, specific operating points. This limitation also restricts the significance of condition-based maintenance models, which are trained to the narrow data. In order to reveal the structure of such multi-dimensional data sets and detect deficiencies, this paper derives a data quality metric and visualization. The metric observes the feature space and evaluates the completeness of data. In the best case, the observations utilize the whole feature space, meaning all different combinations of the variables are present in the data. Low metric values indicate missing combinations, reducing the representativeness of the data. In this way, appropriate countermeasures can be taken if relevant data is missing. For evaluation, a data set of an industrial test bed for condition monitoring of control valves is used. It is shown that the state-of-the-art metrics and visualizations cannot detect deficiencies of completeness in multi-dimensional data sets. In contrast, the proposed heat map enables the expert to locate limitations in multi-dimensional data sets.
Reference Key
weiß2021applieda Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Iris Weiß;Birgit Vogel-Heuser;Weiß, Iris;Vogel-Heuser, Birgit;
Journal applied sciences
Year 2021
DOI
10.3390/app11115022
URL
https://www.mdpi.com/2076-3417/11/11/5022/htm
Keywords
condition monitoring data quality assessment completeness data quality metric sensor and actuator data automated production systems control valves industrie 4.0

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