Pre-Trained Rule Engine for Equipment Abnormal Event Guidance
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Solution Overview
Problem
In industrial environments, operators face challenges in quickly and efficiently correcting abnormal events due to the lack of clear guidance on the right steps and operations needed, which can lead to unwanted actions and equipment failures.
Innovation Solution
A pre-trained rule engine system that receives real-time information about equipment abnormalities, selects relevant data from historic, expert, and equipment standard data, analyzes this information, and provides assistance to operators through a user interface to correct the issues effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If operators rely on their own knowledge to handle abnormal events, then they can perform operations without external assistance, but the time required to correct abnormal events increases and the risk of unwanted actions increases
Solution Approach 1:
An intelligent assistant system acts as an intermediary between operators and abnormal events. The system receives real-time information about abnormal events, analyzes them using pre-trained rule engines, and provides recommended actions to operators. This mediator reduces the time required to correct abnormal events while guiding operators through the process, thereby reducing complexity and preventing unwanted actions.
Solution Approach 2:
The system performs preliminary analysis and preparation by pre-training rule engines with historical data and expert knowledge before abnormal events occur. When an abnormal event happens, the system has already prepared potential solutions and can quickly retrieve and present them to operators, significantly reducing the response time without increasing operational complexity.
2Ease of operation
If standard faceplates with fixed objects are used for monitoring and control, then the system structure remains simple, but operators cannot efficiently address abnormal events without extensive knowledge
Solution Approach 1:
The intelligent assistant system enables operators to handle abnormal events independently by providing them with context-aware recommendations and guidance. Operators no longer need extensive external knowledge or support to correct abnormal events, as the system serves them with the necessary information and recommended actions based on the specific situation.
Solution Approach 2:
The system pre-trains rule engines with historical data, expert opinions, and equipment standard data before deployment. This preliminary preparation allows the system to quickly analyze abnormal events and provide accurate recommendations without requiring complex real-time processing or extensive operator knowledge, thus improving efficiency without proportionally increasing system complexity.
3Reliability
If operators follow multiple possible approaches to restore normalcy, then flexibility in handling different scenarios is maintained, but operators may take unwanted steps or lose time wandering through incorrect options
Solution Approach 1:
The system provides feedback to operators by analyzing the current abnormal event state and recommending the most appropriate corrective actions. The pre-trained rule engine evaluates multiple possible approaches and prioritizes them based on historical success rates and current conditions, guiding operators toward the most reliable solution while reducing the time spent exploring incorrect options.
Solution Approach 2:
The system performs preliminary analysis of the abnormal event using pre-trained models that have already evaluated multiple possible approaches during training. This preliminary work identifies the most likely correct actions before the operator needs to make decisions, thereby improving reliability while reducing the time required to identify correct corrective steps.
Data Source
AI summary
The present disclosure relates to a method and pre-trained rule engine for providing an assistance to correct abnormal event encountered for equipment. Real-time information related to the equipment is received, upon identification of the abnormal event. At least one data is selected from historic data, expert opinion data and equipment standard data associated with the equipment based on real-time state information related to the equipment. The received real-time information is analyzed using the selected at least one data. An assistance is generated for correcting the abnormal event based on the analysis. The assistance is provided to an operator for correcting the abnormal event.