System and method for remotely managing configuration of industrial machines
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Solution Overview
Problem
Detecting misconfigurations in industrial machines is challenging due to complex configuration settings and the need for manual or costly OEM visits, leading to production losses and sub-optimal performance.
Innovation Solution
A method for remotely managing industrial machine configurations by identifying similar machines based on predetermined features, retrieving and comparing configuration values, and determining deviation values to automatically detect misconfigurations, utilizing techniques like vectorization and machine learning for anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual or automated configuration comparison is performed for all industrial machines, then configuration errors can be detected, but the process becomes time-consuming and costly requiring frequent OEM service visits
Solution Approach 1:
The patent segments the machine population into reference machines (correctly configured) and target machines (to be checked). By comparing target machines against a selected reference machine rather than performing full automated analysis on all machines, the system reduces detection time while maintaining configuration accuracy.
Solution Approach 2:
The system enables self-service by allowing the network to automatically identify and compare configurations without requiring manual intervention or frequent OEM service visits. The automated tool performs configuration comparisons and identifies deviations independently.
2Reliability
If comprehensive configuration analysis is performed on all industrial machines, then all misconfigurations can be identified, but the complexity and cost of the process increases significantly
Solution Approach 1:
The patent extracts only the necessary configuration parameters from machines for comparison, rather than analyzing entire configuration sets. By selecting specific relevant parameters for comparison between reference and target machines, the system reduces detection completeness while lowering system complexity and computational requirements.
Solution Approach 2:
The system performs partial analysis by comparing only selected configuration parameters rather than conducting exhaustive analysis of all configuration settings. This partial action approach reduces system complexity while maintaining sufficient detection capability for critical misconfigurations.
3Stability of the object's composition
If recommended configuration from OEM is used for all machines, then configuration consistency is improved, but adaptability to different use cases and lifecycle changes is reduced
Solution Approach 1:
The patent implements feedback by continuously monitoring configuration parameters and comparing them against reference machines. When deviations are detected, the system provides feedback about the misconfiguration, enabling operators to adjust configurations adaptively while maintaining consistency through automated detection.
Solution Approach 2:
The system introduces dynamics by allowing configurations to evolve over time through automated detection and comparison. Rather than enforcing static OEM recommendations, the system adapts to lifecycle changes by identifying deviations from current reference configurations, enabling both consistency and adaptability.
Data Source
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.