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

Iris Weiß,Birgit Vogel-Heuser

doi:10.3390/app11115022

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.

Highlights

The so-called ‘Industrie 4.0’ provides high potential for data-driven methods in automated Production Systems
This paper introduces the concept of data set completeness
Data quality assessment is a crucial part of data understanding and preparation in order to ensure valid data-driven results in data mining

Summary

Introduction

The so-called ‘Industrie 4.0’ provides high potential for data-driven methods in automated Production Systems (aPSs). The concept of condition monitoring utilized these models to monitor the aPS and detect deviations from normal behavior In this way, equipment’s defects and faults are being identified early and actions can be taken to avoid unplanned shutdowns (condition-based maintenance). Equipment’s defects and faults are being identified early and actions can be taken to avoid unplanned shutdowns (condition-based maintenance) This strategy will increase the availability and the overall equipment effectiveness (OEE) of the aPS. Control valves in process plants are removed and opened for inspection in yearly revisions [1]. This procedure is time- and labor-intensive, leading to high costs. One approach to detect faults in control valves has been introduced by

Results

Discussion

Conclusion