Royal Eijkelkamp BV provides a complete suite of solutions of sensors and telemetry for soil and water data collection. They provide a solution for groundwater level monitoring that comprises the setup of the entire monitoring network, which is made up of multiple monitoring wells. The Royal Eijkelkamp BV seeked to improve its services to end-user customers, optimize the maintenance process, and improve the quality control of its measurements.
Each monitoring well includes sensors (pressure and temperature), the telemetry system (data logger), a database, and a visualization portal to manage the data. The values of groundwater level, battery state, and other parameters are logged by their data logger system and sent at pre-programmed intervals to the remote server. There is additional storage of maintenance procedures in addition to measurement data.
They also provide a corrective and periodic maintenance plan for the network. Their system relies on an automatic validation system to check for measurement errors due to many measurements. This validation system executes a univariate analysis to check for inconsistency in the collected data. The monitoring well requires maintenance if the quality check indicates a fault (i.e., malfunction or breakdown).
Due to the large amount of data collected, a manual inspection of the collected data is complex. Thus, inevitably it requires an automatic procedure to perform the quality control tests. The AI model drives the information extraction from the correlation between the measurement and the maintenance data to perform automatic quality control and failure detection system. The AI system assists the maintenance staff by reducing the maintenance effort and increasing trust in the measurement platform.
The Radboud University leads the experiment, and the Royal Eijkelkamp BV is the end-user.
This experiment aimed to provide an AI-powered quality control that will relate the measurement and maintenance data to deliver a grade (score) of the quality measurement, detect system faults, and detect their root cause. The system has used expert systems, multivariate data analysis, and deep learning models as the solution’s core.
This quality control system assigns a grade to the quality of the measurements and detect system failure. Many gains are expected from this solution, as this AI quality-control system will assist the maintenance staff in identifying the root cause of fault more quickly and reducing time spent on-site. Also, the company can understand the multiple causes that affect the measurement, allowing quality improvement of their products and solutions.
| Country: | NL |
| Address: | Geert Grooteplein Noord 9, Nijmegen 6525 |
Having insights on the accuracy of measurement in real-time is important not only for the end-user, as it can add a layer of subjectivity to the observations conclusions, but it also impacts the network management maintenance strategy.
The AI model reduces the time spent by the maintenance staff and optimize the routine of maintenance.
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Score the quality of measurement
Detect failures and their root causes