Simulator Anomaly Diagnosis via Event List Comparison
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Solution Overview
Problem
Complex simulators with multiple interacting components pose challenges in identifying the source of anomalies, as existing methods are time-consuming and inefficient due to their complexity and non-linear behavior.
Innovation Solution
A diagnostic system comprising a communication unit, memory with a database of event lists associated with anomalies, and a processing unit that receives and compares actual component states to identify the source of anomalies by matching them with predefined lists of events, facilitating rapid anomaly detection and prevention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual troubleshooting methods are used to identify anomaly sources in complex simulators, then diagnostic accuracy can be achieved through thorough inspection, but the time required increases significantly to several hours or days
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing normal operation data for all simulator components in a database before anomalies occur. When an anomaly is detected, the system compares current data against this pre-established baseline, enabling rapid identification without time-consuming manual inspection. This preliminary data collection and storage resolves the contradiction by preparing diagnostic references in advance.
Solution Approach 2:
The system creates a digital copy of the simulator's normal operational state by collecting and storing data from all components during normal operation. This digital replica serves as a reference model that can be quickly compared against actual operational data when anomalies occur, replacing slow manual troubleshooting with fast automated data comparison while maintaining diagnostic accuracy.
2Reliability
If comprehensive monitoring of all simulator components is implemented to improve anomaly detection capability, then diagnostic accuracy improves, but system complexity and data processing requirements increase
Solution Approach 1:
The system extracts only the essential diagnostic information from comprehensive component monitoring data by comparing actual operational data against stored normal state data. Instead of processing and analyzing all raw monitoring data, the system extracts only the deviations from normal operation, significantly reducing processing complexity while maintaining reliable anomaly detection capability.
Solution Approach 2:
The system introduces a database as an intermediary layer between component monitoring and anomaly detection. Normal operational data is stored in the database as a reference, and the comparison between current and stored data serves as an intermediate processing step. This intermediary approach simplifies the diagnostic system by providing a clear baseline for comparison rather than requiring complex real-time analysis of all monitoring data.
3Measurement precision
If detailed event lists and multiple data comparison methods are used to improve diagnosis precision, then anomaly source identification accuracy increases, but information processing requirements and system resource consumption increase
Solution Approach 1:
The system applies partial action by focusing diagnostic efforts only on components showing deviations from normal operation. Instead of analyzing all simulator components equally, the system identifies and concentrates processing resources on the specific components where actual data differs from stored normal data, achieving precise anomaly source identification with reduced overall resource consumption.
Data Source
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AI summary
There is described an apparatus for diagnosing a problem in a simulator comprising a plurality of components, comprising: a communication unit; a memory having stored thereon a database containing a plurality of lists of events each associated with a respective anomaly of the simulator and a respective source of anomaly; a processing unit configured for: receiving a detected anomaly of the simulator via the communication unit; retrieving from the database at least a given one of the plurality of lists of events that correspond to the detected anomaly; receiving an actual state of operation for at least some of the plurality of components; identifying a source of the detected anomaly by comparing the received actual state of operation and at least a given one of the plurality of lists of events; and outputting the source of the detected anomaly via the communication unit.