Recommendation Algorithm for Equipment Fault Action Suggestions
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Solution Overview
Problem
In industrial settings, when production equipment malfunctions, workers face challenges in determining the appropriate action sequence due to individual differences in know-how and manual quality, leading to variable response times and effectiveness.
Innovation Solution
A recommendation algorithm-based system that processes user query information, converts text data into numerical data, and suggests action methods by comparing it to a database of past action histories, using similarity calculations to provide prompt and effective solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If workers rely on individual know-how and manuals to handle equipment malfunctions, then they can address problems using available resources, but the action time and quality vary significantly due to individual differences
Solution Approach 1:
The system collects action histories from workers and uses recommendation algorithms to provide feedback in the form of suggested action sequences. This feedback loop enables continuous improvement of action quality while reducing time loss by learning from past experiences and providing data-driven recommendations.
Solution Approach 2:
The patent replaces the mechanical reliance on individual worker know-how and static manuals with an intelligent information processing system. The system uses recommendation algorithms and natural language processing to automatically analyze problem descriptions and generate action suggestions, substituting human cognitive variability with algorithmic consistency.
2Measurement precision
If workers sequentially check related processes and equipment when a failure occurs, then they can identify the root cause, but the process becomes complex and time-consuming
Solution Approach 1:
The system introduces an intermediary intelligent algorithm that mediates between the worker's problem description and the complex diagnostic procedures. The recommendation algorithm acts as a bridge, translating natural language problem descriptions into structured action sequences, thereby simplifying the interaction while maintaining diagnostic accuracy.
Solution Approach 2:
The system performs preliminary analysis of the problem by processing the worker's description through natural language processing and matching it with historical action data before the worker begins the actual troubleshooting. This preliminary action prepares the optimal action sequence in advance, reducing the complexity of the subsequent diagnostic process.
3Reliability
If new workers follow detailed action manuals, then they can handle problems systematically, but the required time to accumulate technical know-how increases significantly
Solution Approach 1:
The system copies proven effective action sequences from experienced workers' historical data and presents them as recommendations to new workers. Instead of requiring new workers to accumulate know-how over time, the system directly provides copied best practices from the collective experience stored in the database, maintaining action consistency while eliminating the time penalty of learning curves.
4Measurement precision
If the system processes text data through natural language processing and recommendation algorithms, then it can provide accurate action suggestions, but the processing complexity and computational requirements increase
Solution Approach 1:
The system segments the complex text processing task into distinct modules: natural language processing module, feature extraction module, recommendation algorithm module, and output generation module. Each module handles a specific aspect of the processing pipeline, making the overall system more manageable and maintainable while achieving high recommendation accuracy through specialized processing at each stage.
Data Source
AI summary
A recommendation algorithm-based problem action suggestion system includes a query input unit configured to extract text data for each item in user's query information received from a user terminal in abnormality of a device of production equipment or a product, a database unit configured to manage a past action history of workers with respect to abnormality occurrence of the device into a database (DB) of problem action data, and a controller configured to perform a pre-processing work for converting the text data into numerical data that may be processed by the recommendation algorithm, to obtain similarity between the numerical data and the problem action data of the database unit and to suggest action methods to the user terminal in an order of higher probability of solving the problem.