AI-Personalized Maintenance Messaging for Electromechanical Equipment
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Solution Overview
Problem
Conventional maintenance techniques for electromechanical devices face challenges in providing interpretable and personalized feedback to users, leading to potential component damage and inefficient resource usage due to unprocessed diagnostic data and user-specific system settings.
Innovation Solution
A personalized messaging system utilizing generative artificial intelligence, such as large language models, generates messages tailored to the electromechanical device and user characteristics, providing feedback on maintenance, repairs, and system settings adjustments based on sensor data and diagnostic codes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional diagnostics tools and systems are used to perform maintenance on electromechanical devices, then diagnostic information can be accessed and interpreted, but the information becomes difficult for users to interpret, leading to potential damage and increased computational resource usage
Solution Approach 1:
The patent introduces a natural language processing intermediary that translates complex diagnostic codes and sensor data into plain English messages. This intermediary layer converts the raw diagnostic information from the electromechanical device into user-friendly explanations, maintaining information accuracy while eliminating interpretation difficulties for end users.
Solution Approach 2:
The system changes the parameter of information presentation from technical diagnostic codes to natural language descriptions. By transforming the format and complexity level of the information output, the system makes diagnostic data accessible to non-technical users without losing the underlying technical accuracy.
2Reliability
If conventional diagnostics tools are used, then maintenance information can be obtained, but computational resources are increased due to complex processing requirements
Solution Approach 1:
The patent extracts only the essential diagnostic information needed for user understanding, separating critical maintenance data from unnecessary complex processing. The system identifies and processes only the most relevant sensor readings and diagnostic codes, eliminating redundant computational steps while maintaining reliability of the maintenance information provided.
3Loss of information
If complex diagnostics systems are implemented, then comprehensive maintenance data can be collected, but the system complexity increases making it harder to operate
Solution Approach 1:
The patent segments the complex diagnostic information into distinct, manageable components such as sensor readings, diagnostic codes, and actionable recommendations. Each segment is processed and presented separately in natural language, allowing users to understand and act on specific issues without being overwhelmed by the entire system's complexity.
Data Source
AI summary
Message personalization for an electromechanical device is described. Data is received, the system information data associated with a change in state of one or more components of an electromechanical device. A message is generated by inputting the system information data as a prompt to generative artificial intelligence representative of one or more characteristics of the electromechanical device and trained to analyze the system information data. The message indicates feedback responsive to the change in state of the one or more components. The message is output, where one or more language characteristics of the message are associated with the one or more characteristics of the electromechanical device.