Peer-Machine Configuration Analysis for Industrial Misconfiguration Detection
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Solution Overview
Problem
Industrial machines often experience configuration errors that are difficult to detect and troubleshoot, leading to costly downtime and sub-optimal production processes, as existing methods rely on manual comparisons with recommended configurations that may not be suitable for varied use cases or lifecycle changes.
Innovation Solution
A method that identifies similar industrial machines based on predetermined features, retrieves and compares configuration values to determine deviation values, and automatically detects misconfigurations by comparing these values with threshold values, allowing for targeted analysis and optimization of machine configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual comparison with recommended configuration is used, then configuration errors can be detected, but it is time-consuming and costly requiring service personnel visits
Solution Approach 1:
The system enables machines to automatically detect and report their own configuration status by comparing with peer machines, eliminating the need for manual service personnel intervention. The automated anomaly detection system continuously monitors configuration parameters and self-identifies deviations without external assistance.
Solution Approach 2:
The system establishes a feedback loop where configuration data from multiple machines is continuously collected, analyzed, and used to generate anomaly detections. The comparison results feed back into the system to trigger automated alerts and enable continuous improvement of configuration standards based on aggregated data.
2Reliability
If manual configuration comparison is performed, then configuration errors can be identified, but production losses occur due to downtime
Solution Approach 1:
The system performs preliminary configuration validation by continuously comparing machine configurations against learned optimal configurations from peer machines. Anomalies are detected and flagged before they cause production issues, allowing preventive maintenance scheduling that minimizes production disruption.
Solution Approach 2:
The patent replaces manual mechanical inspection processes with automated digital configuration analysis. Software-based anomaly detection algorithms continuously monitor configuration parameters remotely, substituting physical service personnel visits with automated computational analysis that occurs without interrupting production.
3Ease of manufacture
If recommended configuration from OEM is used, then standardization is achieved, but it may not be optimal for different use cases and lifecycle stages
Solution Approach 1:
The system transitions from static OEM-recommended configurations to dynamic, adaptive configuration management. Configuration standards evolve automatically based on aggregated data from multiple machines across different use cases and operational stages, allowing the system to adapt to varying requirements while maintaining core standardization benefits.
Solution Approach 2:
The patent enables configuration parameters to change and optimize based on empirical data collected from peer machines. Instead of fixed OEM recommendations, the system continuously adjusts optimal parameter values based on real-world performance data, allowing configuration optimization for specific use cases while maintaining standardized monitoring and analysis frameworks.
4Reliability
If every industrial machine is checked by service personnel, then configuration errors are detected, but it is cost-intensive
Solution Approach 1:
The system creates a universal configuration monitoring platform that serves multiple machines simultaneously through peer-to-peer comparison. A single automated system can analyze configurations across an entire fleet of machines, providing multi-functional anomaly detection that replaces multiple individual service personnel efforts with one centralized intelligent system.
Data Source
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AI summary
This invention describes a method for detecting a misconfiguration of a machine function of a first industrial machine (M1). A set (N) of second industrial machines (MS) from said several other industrial machines (MO) is created by identifying a predetermined feature of the first industrial machine (M1) in several other industrial machines (MO). A first configuration value is created, which relates to the machine function of the first industrial machine (M1), and a second configuration value is created which relates to the machine function of the second industrial machines (MS). Depending on a relation of the first configuration value to the second configuration value a deviation value is determined by a configuration anomaly detection module (CADM). For detecting the misconfiguration of the first industrial machine (M1) the deviation value is compared with a predetermined threshold value.