Fault Diagnosis Knowledge Map for Sensor-Based Solution Recommendation
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Solution Overview
Problem
In modern digital factories, workers face difficulties in timely solving unexpected problems due to the limitations of existing diagnostic programs, which often rely on established modes and lack the ability to diagnose faults based on specific task characteristics, and typically recommend solutions from pre-stored documents, making it challenging to detect faults requiring sensor data.
Innovation Solution
An intelligent fault diagnosis and solution recommendation system that collects data streams with device identification and time information, uses convolutional neural networks for fault analysis, and recommends solutions based on a cloud knowledge map, integrating data stream and multimedia content analysis to generate problem descriptions and provide video or document solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing diagnostic programs rely on established modes and pre-stored documents, then the system structure is simple and easy to operate, but the system cannot detect faults requiring sensor data and lacks the ability to diagnose faults based on specific task characteristics
Solution Approach 1:
The system segments fault diagnosis into multiple independent modules: data stream analysis module, multimedia content analysis module, fault analysis module, and solution recommendation module. Each module handles specific aspects of fault detection, allowing the system to process sensor data and multimedia content while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces a cloud-based knowledge map as an intermediary between fault detection and solution provision. The knowledge map stores structured fault information, diagnostic rules, and solution documents, enabling the system to handle complex diagnostic tasks without increasing operational complexity at the user level.
2Measurement precision
If the system performs complex fault analysis using convolutional neural networks and data stream analysis, then the accuracy and timeliness of fault detection is improved, but the computational complexity and processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing diagnostic rules, fault patterns, and solution documents in the cloud knowledge map before actual fault occurrence. When faults occur, the system quickly matches sensor data and multimedia content against pre-established patterns, reducing real-time processing time while maintaining high diagnostic accuracy.
Solution Approach 2:
The system applies partial analysis by focusing computational resources on the most relevant fault detection tasks. The convolutional neural networks are deployed selectively for multimedia content analysis, while data stream analysis uses rule-based approaches for routine monitoring, balancing accuracy requirements with processing efficiency.
3Productivity
If workers need to solve unexpected problems in real-time, then the response time to faults is reduced, but the complexity of operation increases for workers
Solution Approach 1:
The system implements self-service by automatically collecting sensor data, analyzing multimedia content, diagnosing faults, and generating solution recommendations without requiring worker intervention in the analysis process. Workers simply need to upload fault-related multimedia content and receive automated diagnostic results and solution recommendations, maintaining operational simplicity while achieving rapid fault resolution.
Solution Approach 2:
The system incorporates feedback mechanisms where diagnostic results and solution recommendations are continuously refined based on worker interactions and fault resolution outcomes. The cloud knowledge map is updated with new fault patterns and solutions, improving system performance over time without increasing operational complexity for users.
Data Source
AI summary
Examples of the present disclosure include methods and/or systems for fault diagnosis and solution recommendation. A method may include: obtaining original data including fault problem of a target device; analyzing the original data including fault problem to obtain problem description information; analyzing the problem description information to obtain a diagnosis report; and, according to the diagnosis report, obtaining a video and/or document solution for the fault based on a cloud knowledge map, and recommending the solution to a user. The knowledge map comprises: nodes representing the fault, video solution and/or document solution, and multiple edges representing the relationship between nodes.