Corrugated Board Plant Diagnostics for Predictive Maintenance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Corrugated board production plants face significant downtime and maintenance costs due to wear and tear of functional units, leading to production losses and increased expenses, with current methods being inefficient in predicting and preventing failures.
Innovation Solution
A predictive diagnostics method that monitors operational parameters of functional units by calculating statistical functions based on historicized data within a movable learning temporal window, allowing for real-time comparison and generation of predictive diagnostic information to anticipate potential failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional maintenance schedules are used, then equipment reliability is maintained through regular servicing, but production downtime increases due to unnecessary stoppages and maintenance costs increase
Solution Approach 1:
The system performs preliminary diagnostic actions by continuously monitoring operational parameters and calculating statistical functions (standard deviation, skewness, kurtosis) to detect early signs of equipment degradation. This allows maintenance to be scheduled based on actual equipment condition rather than fixed schedules, preventing failures before they occur while avoiding unnecessary maintenance stoppages.
Solution Approach 2:
The system establishes a feedback loop by continuously collecting operational data, comparing current statistical functions against historical baselines, and generating diagnostic information that feeds back into maintenance decision-making. This closed-loop approach enables dynamic adjustment of maintenance schedules based on real-time equipment health status.
2Loss of time
If equipment is monitored continuously with advanced diagnostics, then maintenance timing is optimized and downtime reduced, but system complexity and implementation costs increase
Solution Approach 1:
The system replaces complex mechanical monitoring equipment with computational analysis of existing operational parameters. Instead of adding numerous sensors and mechanical diagnostic devices, the invention uses software-based statistical analysis (calculating standard deviation, skewness, kurtosis) of data already available from standard equipment instrumentation, thereby reducing physical system complexity while maintaining diagnostic capability.
3Productivity
If maintenance is delayed until failures occur, then production time is maximized and operational costs are reduced, but reliability drops and emergency repair costs increase
Solution Approach 1:
The system takes preliminary action by detecting equipment degradation trends through statistical analysis of operational parameters before failures occur. By calculating and monitoring changes in standard deviation, skewness, and kurtosis over time, the system enables planned maintenance scheduling that prevents unexpected breakdowns while minimizing production interruptions.
Data Source
Figure 1(A)~1(B)
Figure 1(C)~1(D)
Figure 2(A)~2(D)
AI summary
A new method is disclosed for monitoring the operation of a corrugated board production plant, the method provides for detecting at least one operational parameter of a functional unit of the plant, for example a current absorbed by a motor. Then, the current value of a statistical function of the operational parameter is calculated in a current temporal window. The maximum value and the minimum value of the same statistical function are calculated based on historicized data of the operational parameter in question. By comparing the current value of the statistical function and the maximum and minimum values, a piece of information of predictive diagnostics is obtained.