18283334. Causal Analysis of an Anomaly Based on Simulated Symptoms simplified abstract (Siemens Aktiengesellschaft)
Contents
- 1 Causal Analysis of an Anomaly Based on Simulated Symptoms
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Causal Analysis of an Anomaly Based on Simulated Symptoms - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
Causal Analysis of an Anomaly Based on Simulated Symptoms
Organization Name
Inventor(s)
Daniel Labisch of Karlsruhe (DE)
Causal Analysis of an Anomaly Based on Simulated Symptoms - A simplified explanation of the abstract
This abstract first appeared for US patent application 18283334 titled 'Causal Analysis of an Anomaly Based on Simulated Symptoms
Simplified Explanation
The method described in the abstract involves simulating error-free operation of a technical plant, simulating individual causes of an anomaly occurring during operation, comparing simulated error-free operating states with states when the anomaly occurs, deriving qualitative symptoms to describe deviations, determining a qualitative symptom if the anomaly occurs, comparing symptoms to identify similar ones, and storing causes associated with identified symptoms.
- Simulating error-free operation of a technical plant
- Simulating individual causes of an anomaly
- Comparing simulated operating states
- Deriving qualitative symptoms
- Determining and comparing symptoms
- Storing causes associated with identified symptoms
Potential Applications
The technology can be applied in various industries where technical plants are used, such as manufacturing, energy production, and transportation, to identify and address anomalies during operation.
Problems Solved
This technology helps in quickly identifying the causes of anomalies in technical plants, allowing for timely maintenance and preventing potential breakdowns that could lead to costly downtime.
Benefits
- Improved operational efficiency - Reduced downtime - Preventive maintenance - Cost savings
Potential Commercial Applications
"Anomaly Cause Determination Method in Technical Plants" can be utilized in industries such as manufacturing, energy, and transportation for predictive maintenance and optimizing plant operations.
Possible Prior Art
One possible prior art could be traditional methods of diagnosing technical plant anomalies, which may involve manual inspection and troubleshooting without the aid of simulation and comparison techniques.
Unanswered Questions
How does this method compare to traditional anomaly detection methods in terms of accuracy and efficiency?
This article does not provide a direct comparison between this method and traditional anomaly detection methods. It would be interesting to see a study or analysis that evaluates the accuracy and efficiency of this method compared to traditional approaches.
What are the potential limitations or challenges in implementing this technology in different types of technical plants?
The article does not address the potential limitations or challenges that may arise when implementing this technology in various technical plants. It would be beneficial to explore factors such as plant size, complexity, and compatibility with existing systems.
Original Abstract Submitted
A method for determining anomaly causes during operation of a technical plant includes simulating error-free operation of the technical plant, simulating individual causes of an anomaly occurring during operation of the technical plant, comparing simulated error-free operating states for each cause of the anomaly with the simulated operating states when the cause of an anomaly occurs, deriving from the comparison a qualitative symptom that describes a qualitative deviation of the operating state from the error-free operating state; determining a qualitative symptom of the anomaly if the anomaly occurs during operation of the technical plant, comparing the symptom with each symptom previously derived during simulations of the operating states when a cause of an anomaly is present, identifying symptoms having a determined degree of similarity to the symptom; and storing causes of the anomaly associated with the identified symptoms in a data memory of the technical plant and/or displaying them.
