Autonomous Deviation Analysis Using Simulated State Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current root cause analysis methods in automation technology and robotics are time-consuming, error-prone, and require expert knowledge, making them inefficient and costly for identifying deviations in systems like industrial robots.
Innovation Solution
An autonomous unit analyzes current and past system data using similarity metrics and simulations to independently determine the cause of deviations, allowing for autonomous error cause analysis and potential countermeasures, reducing reliance on human experts and improving reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual root cause analysis is performed using conventional approaches (logging and remote monitoring), then experts can identify the cause of deviations, but the analysis becomes time-consuming and complex
Solution Approach 1:
The autonomous unit performs root cause analysis independently using its own processing capabilities, comparing current state data with historical data and simulations without requiring external expert intervention. The system serves itself by automatically identifying deviations, determining their causes, and implementing countermeasures
Solution Approach 2:
Manual expert analysis is replaced with an automated information processing system that uses algorithms to compare state data, evaluate similarities, and determine causes. The mechanical process of expert review is substituted with computational methods including data comparison, similarity metrics, and simulation techniques
2Reliability
If manual expert analysis is used for root cause identification, then accurate causes can be determined, but expert knowledge is required and costs increase
Solution Approach 1:
The autonomous unit independently performs the analytical function that previously required external experts. It autonomously compares state data, identifies deviations, determines causes through simulation and comparison, and implements countermeasures without human intervention
Solution Approach 2:
The system creates virtual copies of system states through simulations that replicate actual operating conditions. By comparing current state data with simulated scenarios, the system identifies causes without requiring physical presence or interpretation by experts
3Loss of information
If conventional logging and remote monitoring are used to record system states, then data is available for analysis, but the approaches are insufficient for increasing automation and Industry 4.0 requirements
Solution Approach 1:
The autonomous unit automatically processes recorded data through sophisticated comparison algorithms and simulations, transforming passive data storage into active autonomous analysis. The system self-determines causes and implements countermeasures based on the recorded state data
Solution Approach 2:
The system combines multiple data sources (state data, sensor data, simulation data) and multiple analytical methods (comparison algorithms, similarity metrics, simulations) into an integrated autonomous analysis capability that exceeds the sum of its individual components
Data Source
Figure 1
Figure 2
AI summary
The invention relates to a method for analyzing a cause of at least one deviation, comprising the steps of: receiving a state data set (10) to be analyzed, comprising the at least one deviation (S1); determining at least one previous state data set (20, S2); determining at least one alternative previous state data set (30) based on the at least one previous state data set (20, S3); determining at least one simulated data set (40) by simulating the at least one alternative previous state data set (30, S4); comparing the at least one simulated data set (40) with the state data set to be analyzed (10, S5); determining a similarity value between the at least one simulated data set (40) and the state data set to be analyzed (10, S6);Output of at least one simulated data set (40), at least one alternative preceding state data set (30) as the cause of at least one deviation or at least one error message depending on the similarity value (S7). The invention further relates to a corresponding autonomous unit and a corresponding computer program product.