Interactive Troubleshooting Assistant for Dynamic Repair Adaptation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing troubleshooting systems provide static diagnoses and solutions, failing to dynamically update based on new information generated during real-time repairs, leading to inefficiencies in diagnosing and fixing system failures, and lack dynamic updates during system installation processes.
Innovation Solution
An interactive troubleshooting assistant that analyzes data from smart and connected components, using multimodal inputs like text, images, and sensor data to provide real-time recommendations and dynamically update actions, assigning the correct workers with necessary tools and parts, and adapting to new information after each action in the repair process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static troubleshooting systems are used, then system structure is simple and ease of operation is maintained, but the ability to adapt to new information during repair is poor and time to diagnose and repair increases
Solution Approach 1:
The system continuously receives feedback from multiple sources including sensor data from the equipment, images captured by cameras, text inputs from technicians, and sensor data from tools. This feedback loop enables the system to dynamically update the digital twin and adapt troubleshooting recommendations based on the current state of the equipment and actions already taken, thereby improving adaptability while reducing diagnostic time through real-time information integration.
Solution Approach 2:
The system transitions from static troubleshooting procedures to dynamic adaptive troubleshooting by continuously updating the digital twin representation of the equipment state. The troubleshooting plan is dynamically regenerated based on current sensor readings, observed conditions, and completed actions, allowing the system to adapt to new information in real-time and reduce overall diagnostic and repair time.
2Productivity
If static troubleshooting procedures are followed, then device complexity is low and ease of operation is maintained, but productivity decreases due to inability to dynamically update recommendations
Solution Approach 1:
The system introduces a digital twin as an intermediary representation of the physical equipment state. This digital twin serves as a mediator between the physical equipment and the troubleshooting logic, allowing complex sensor data, images, and text inputs to be integrated and processed without directly complicating the troubleshooting interface. The digital twin abstracts the complexity while enabling dynamic updates to troubleshooting recommendations, thereby improving repair efficiency without proportionally increasing operational complexity.
Solution Approach 2:
The system integrates multiple data sources and functionalities into a unified troubleshooting platform. It simultaneously processes sensor data from equipment, images from cameras, text inputs from technicians, and sensor data from tools, while maintaining a single coherent troubleshooting workflow. This multi-functional integration improves productivity by consolidating multiple functions into one system rather than requiring separate tools and procedures for each data type.
3Measurement precision
If real-time data collection from multiple sources is implemented, then adaptability and measurement precision improve, but device complexity and loss of time increase due to processing requirements
Solution Approach 1:
The system creates a digital copy (digital twin) of the physical equipment and its state. This digital twin replicates the essential characteristics and state of the physical system, allowing complex processing to be performed on the digital representation rather than directly on the physical system. The digital twin serves as a simplified model that captures the necessary state information while reducing the complexity of real-time processing by working with digital data rather than raw sensor streams.
Data Source
AI summary
An interactive troubleshooting assistant and method for troubleshooting a system in real time to repair (fix) one or more problems in a system is disclosed. The interactive troubleshooting assistant and method may include receiving multimodal inputs from sensors, wearable devices, a person, etc. that may be input into a feature extractor including attention layers and pre-processing units of a cloud computing system hosted by one or more servers, such as a private cloud system. A pre-processing unit converts the raw multimodal input into a structed form so that an attention layer can give weights to features provided by the pre-processing unit according to their importance. The weighted extracted features may be provided to an actions predictor. The actions predictor generates the most suitable action based on the weighted extracted features generated by the feature extractor based on the multimodal inputs. After the most suitable action is performed, the interactive troubleshooting assistant considers new information from multimodal inputs so that the interactive troubleshooting assistant can provide the next recommended action. The interactive troubleshooting assistant may repeat these operations until the repair is completed.