OPC-Based Automation Monitoring for Predictive Task Failure
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Solution Overview
Problem
Current equipment monitoring systems in industrial automation facilities face inefficiencies in data collection, as they often gather excessive data without change and are either proactive or reactive, lacking comprehensive performance data for predictive maintenance.
Innovation Solution
An automation management system that includes a PLC and a PC with an OPC server to collect and store data based on task-specific start and end times, using performance graphs and threshold calculations to determine normal, cautionary, or warning indicators for predictive maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If polling data from computational devices at preset time intervals, then data collection is systematic and continuous, but a large amount of data is collected and stored even when there is no activity or change
Solution Approach 1:
The system uses event-triggered periodic action where data collection occurs only when changes are detected in equipment state, rather than continuous polling at fixed intervals. This maintains systematic monitoring while significantly reducing unnecessary data collection during steady-state operations.
Solution Approach 2:
The invention extracts only the relevant data points that indicate actual equipment state changes or anomalies. By filtering and selecting only meaningful data for collection and storage, the system reduces overall data volume while maintaining monitoring reliability.
2Quantity of substance
If computational devices transmit data only upon detection of a problem using CIP protocol, then data transmission volume is reduced, but the system becomes reactive and complete performance data is not available for playback
Solution Approach 1:
The system performs preliminary data collection and storage of complete performance data before problems occur. By proactively capturing and storing comprehensive equipment performance information in advance, the system ensures data availability for later playback and analysis without requiring continuous high-volume transmission.
Solution Approach 2:
The invention introduces an intermediary data storage layer that buffers complete performance data between the computational devices and the analysis system. This intermediary storage mechanism allows selective data transmission while maintaining complete performance records for playback, resolving the conflict between data volume reduction and information completeness.
3Measurement precision
If OPC server is configured based on computational device configuration, then data collection is initially accurate, but software modifications on computational devices prevent accurate data collection
Solution Approach 1:
The system implements dynamic configuration where the OPC server automatically adapts to computational device software changes. By making the data collection configuration dynamic rather than static, the system maintains measurement precision even when computational device software is modified, as it can recalibrate and adjust to new configurations.
Solution Approach 2:
The invention incorporates feedback mechanisms that monitor data collection accuracy and automatically adjust OPC server configuration in response to computational device changes. This feedback loop ensures continuous accurate data collection despite software modifications on the computational devices.
Data Source
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AI summary
A method for monitoring performance of at least one task in controlled equipment is disclosed herein. The method includes collecting a series of signals associated with the at least one task, at least some of the signals in the series define timing values for the at least one task. comparing each of at least some of the timing values to a reference value, generating an accumulated variance value based on the comparisons and selectively generating a predictive failure indication based on the generated accumulated variance value.