Interactive Troubleshooting Assistant for Dynamic Repair Guidance
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Solution Overview
Problem
Existing troubleshooting systems fail to dynamically update diagnoses and instructions during real-time repairs of smart and connected components, leading to inefficiencies in diagnosing and fixing system failures, as they do not consider new information generated during the repair process.
Innovation Solution
An interactive troubleshooting assistant that analyzes multimodal inputs such as text, images, and sensor data from smart and connected components, dynamically recommending actions to repair systems by continuously updating the diagnosis and instructions based on new information after each action, utilizing artificial intelligence to model troubleshooting as a sequence-to-sequence prediction problem.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing troubleshooting systems provide static diagnosis and static solution, then the system structure is simple and easy to implement, but the system cannot dynamically update the initial diagnosis and cannot adapt to new information generated during real-time repair
Solution Approach 1:
The troubleshooting system transitions from a static state to a dynamic state by continuously updating the diagnosis and recommended actions based on new information received during the repair process. The system adapts its recommendations in real-time as technicians perform actions and new data becomes available, making the diagnostic process dynamic rather than fixed.
Solution Approach 2:
The system implements feedback loops where the diagnosis and recommended actions are continuously updated based on new information received from technicians during the repair process. Each action taken by the technician generates new information that feeds back into the system, which then updates the diagnosis and provides the next recommended action, creating a closed-loop adaptive system.
2Productivity
If field engineers follow standard procedure to fix problems, then the repair process has structured guidance, but the system does not consider new information generated during inspection and repair
Solution Approach 1:
The system captures new information generated during inspection and repair activities and feeds it back into the diagnostic process. As technicians perform actions and observe system behavior, this new information is immediately incorporated to update the diagnosis and adjust recommended actions, preventing information loss and improving repair efficiency.
Solution Approach 2:
The troubleshooting process continues dynamically throughout the repair activity rather than being a one-time static assessment. The system continuously processes new information as it becomes available during inspection and repair, maintaining an up-to-date diagnosis and recommended actions throughout the entire repair process, not just at the beginning.
3Ease of operation
If troubleshooting system provides static set of instructions, then the implementation is straightforward, but the instructions cannot be dynamically changed after completion of each action
Solution Approach 1:
The set of instructions transitions from static to dynamic, automatically updating after each action is completed. The system generates new recommendations based on the current system state and new information received, providing continuously updated guidance that adapts to the evolving repair situation without requiring manual intervention to change instructions.
Solution Approach 2:
The troubleshooting system provides continuous guidance throughout the repair process by dynamically generating new instructions based on completed actions and new information. Rather than providing a fixed set of instructions at the beginning, the system continuously updates recommendations to guide technicians through the repair process efficiently, reducing overall time to correct system failures.
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.