Dynamic State Base Value Adaptation for Machine Monitoring
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Solution Overview
Problem
Current monitoring systems for technical processes struggle to accurately differentiate between wear-related and external influences on measurement data, leading to potential maintenance issues and false alarms.
Innovation Solution
The method involves calculating a 'good value extent' for state values with upper and lower limits in the monitoring phase, using a predetermined limit value range and a dynamically adapted state base value, allowing for better separation of internal process changes from external influences, and incorporating statistical evaluations to adapt to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If measurement data are used with fixed limit values from a learning phase, then the system is simple to operate, but it cannot adapt to changing external influences and operating conditions
Solution Approach 1:
The patent implements dynamic adaptation by automatically recalculating the state base value during the monitoring phase using currently prevailing good values from time series. This allows the system to adapt to changing external influences and operating conditions without requiring manual intervention or complex reconfiguration, resolving the contradiction between adaptability and simplicity.
2Measurement precision
If external influences are not accounted for, then the monitoring system is simpler, but false alarms increase due to inability to differentiate wear-related changes from external factors
Solution Approach 1:
The patent extracts and separates the effect of external influences by dynamically adapting the state base value to reflect current operating conditions. This isolation technique allows the system to focus specifically on wear-related changes while filtering out external factors, improving measurement precision without requiring overly complex processing.
Solution Approach 2:
The system uses feedback from currently prevailing good values to automatically adjust the state base value during monitoring. This continuous feedback mechanism enables the system to learn and adapt to changing conditions, improving the accuracy of wear detection while maintaining automated operation that doesn't increase operational complexity.
3Reliability
If maintenance is performed based on fixed intervals, then scheduling is simple, but maintenance operations are performed unnecessarily early or fail to prevent unexpected failures
Solution Approach 1:
The monitoring system performs self-service by automatically detecting wear-related changes and triggering maintenance alerts based on actual machine state rather than fixed schedules. This eliminates the need for manual interval calculation and ensures maintenance is performed precisely when needed, improving both reliability and operational availability by preventing both premature and delayed maintenance.
Data Source
AI summary
Provided is a method for monitoring measurement data by way of measurement channels. The measurement channels are processed in a plurality of time series of state values with mutually different time bases. Limit value ranges are ascertained in a learning phase and monitoring-related steps are triggered in a monitoring phase when limit values are exceeded. A good value extent is calculated for a time series with an upper and a lower limit value, with the inclusion of a limit value range which is predetermined in the learning phase and a state base value of the time series, which is determined in the monitoring phase.