Condition-Based Maintenance Scheduling for Technical Installations
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Solution Overview
Problem
Current methods for monitoring the condition of technical installations rely on outdated static data, leading to inefficient maintenance scheduling and increased costs, as they fail to account for the dynamic deterioration of components over time.
Innovation Solution
A method that uses online condition monitoring with sensor data to determine the current operating condition of installations, incorporating aging effects to predict future deterioration, allowing for optimized maintenance scheduling by adjusting maintenance intervals based on real-time data and component-specific weighting factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static measurements recorded once are used for monitoring the condition of technical installations, then the data used is simple to collect and store, but the data is not up-to-date and leads to inefficient maintenance scheduling
Solution Approach 1:
The patent transitions from static measurements to dynamic online condition monitoring that continuously updates the operating condition of technical installations. The system calculates current operating condition based on real-time measured values and uses aging functions to predict future conditions, enabling dynamic maintenance scheduling that adapts to actual installation state rather than relying on fixed intervals or outdated data.
Solution Approach 2:
The system implements feedback by continuously monitoring measured values from the technical installation, comparing them against reference values, and using the deviations to update the operating condition assessment. This closed-loop feedback mechanism allows the maintenance scheduling to respond to actual installation condition changes, improving timing accuracy while avoiding unnecessary maintenance on healthy components.
2Measurement precision
If online condition monitoring is performed to quickly detect malfunctions, then the detection speed is improved, but the decision-making still relies on technician experience and empirical figures rather than objective data
Solution Approach 1:
The patent transforms subjective technician experience into objective quantitative parameters by calculating a numerical operating condition value based on measured deviations from reference values. The system uses standardized formulas to convert multiple measured parameters into a single comprehensive operating condition indicator, making the assessment objective, reproducible, and suitable for automated decision-making while reducing reliance on individual technician expertise.
Solution Approach 2:
The system segments the complex condition monitoring task into distinct processing stages: collecting measured values from sensors, comparing them against reference values, calculating deviations, applying weighting factors, and synthesizing an overall operating condition score. This segmentation makes the complex data processing manageable and systematic, allowing each stage to be handled by specific software modules while maintaining overall accuracy.
3Productivity
If maintenance is scheduled based on fixed intervals and cost considerations, then the planning is simple, but the installations cannot be used for optimum length of time and costs are increased
Solution Approach 1:
The system performs preliminary action by predicting future operating conditions using aging functions before actual deterioration occurs. By calculating the expected chronological course of the operating condition, the system can proactively schedule maintenance at the optimal moment - extending intervals when conditions are stable and predicting when deterioration will reach critical thresholds, thereby maximizing installation utilization while avoiding costly emergency repairs or premature replacements.
Data Source
AI summary
The condition of a technical installation is automatically monitored. A measured value of at least one component of the installation is sensed by at least one sensor device on the installation, and the at least one measured value is used, in a data processing arrangement specific to the installation, for determining a current operating state of the installation. By way of the data processing arrangement, including component-specific aging functions, a future chronological course of the operating condition is determined from the current operating condition, and a maintenance interval for the installation is adjusted on the basis of the future chronological course of the operating condition, in order to determine a next maintenance date of the installation. A corresponding arrangement and a computer program product for condition monitoring are also described.