Automation Status Message Causality for Root Cause Timing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for root cause analysis in automation systems require manual time measurements or derivation from digital specifications, which are time-consuming and prone to systematic errors, especially when the system deviates from its planned state.
Innovation Solution
A method for automatically extracting time parameters from data recorded during normal operation of automation systems, allowing for the processing of status reports and identification of causal states and propagation times between components, which can be used in root cause analysis without the need for manual measurements or deviations from the original system specification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual time measurements are used to determine time parameters for root cause analysis, then the accuracy of time parameter determination is improved, but the time consumption and operational disruption increase significantly
Solution Approach 1:
The system performs preliminary action by automatically recording and storing time stamps of all status messages and component states during normal operation. This pre-captured temporal data is then readily available for root cause analysis without requiring manual measurement at the time of analysis, thus maintaining accuracy while eliminating time consumption.
Solution Approach 2:
The system performs self-service by automatically extracting and analyzing time parameters from its own operational data. The automation system monitors itself, recording status changes and component states with precise time stamps, and then uses this self-collected data to perform root cause analysis without external manual intervention.
2Ease of operation
If time parameters are derived from digital system specification, then the measurement process is simplified, but systematic errors increase when the system deviates from planned configuration
Solution Approach 1:
The system implements feedback by continuously monitoring actual component states and status messages during operation, comparing them against the digital specification, and using the actual measured time parameters from operational data to perform root cause analysis. This feedback mechanism ensures that the analysis reflects the real system behavior rather than theoretical specifications.
Solution Approach 2:
The system applies parameter changes by dynamically extracting time parameters from actual operational data rather than using fixed values from digital specification. The time parameters are adapted to reflect the actual system configuration and behavior, allowing accurate analysis even when the system deviates from its planned state.
3Productivity
If manual time measurements are performed during normal system operation, then the system can identify propagation effects in real-time, but the system operation is disrupted and time measurements become inaccurate
Solution Approach 1:
The system ensures continuity of useful action by automatically and continuously recording status messages and component states with time stamps during normal operation without interruption. This continuous automated data collection maintains both system operation and measurement reliability, allowing real-time root cause analysis without the disruptions inherent in manual measurements.
Data Source
Figure 1
Figure 2
AI summary
The invention relates to a method for the computer-aided processing of status messages (AL) in an automation system (AS), wherein the status messages (AL) are generated by a plurality of components (M-1, M, M+1) during the execution of an automated process in the automation system (AS) and are recorded at their generation times. For a plurality of status messages (AL) of a respective component (M), causal states (O, S, T, IE) are determined for the current state (O, S, T, IE) in the generated status message (AL), which exist in other components (M-1, M+1) at or before the generation time of the status message (AL), wherein the propagation time (Δt) between the occurrence of the respective causal state (O, S, T, IE) and the generation time of the status message (AL) is calculated for each causal state (O, S, T, IE).From the causal states (O, S, T, IE), groups (PO1, PO2) are formed, whereby in each group all causal states (O, S, T, IE) share at least the common characteristic that they were determined for the same current state (O, S, T, IE) in the respective component. Finally, from the propagation times (Δt) belonging to the causal states (O, S, T, IE) of the same group (PO1, PO2), one or more statistical parameters (P1, P2, ..., P5) are determined and stored.