AI Operation Log Diagnosis for Autonomous Malfunction Detection
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Solution Overview
Problem
Existing AI devices face challenges in autonomously diagnosing malfunctions without expert intervention, particularly in varying environments, as they lack the ability to self-assess and adapt their operation logs effectively.
Innovation Solution
An AI device equipped with a sensing unit to collect operation logs, a memory to store data, and a processor to provide this data to an AI model for classification, allowing the device to determine whether it operates within a normal or malfunction symptom range, and perform appropriate control actions, such as deleting or storing logs accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If an AI device autonomously diagnoses malfunctions using operation logs and AI models, then the device can self-diagnose without expert assistance, but the device complexity increases due to additional sensing units, memory, and processing requirements
Solution Approach 1:
The AI device performs self-diagnosis by autonomously collecting operation logs through sensing units, storing them in memory, processing them through a processor with an AI model, and generating malfunction diagnoses without requiring external expert intervention. The system serves itself by implementing the entire diagnosis workflow internally.
Solution Approach 2:
An AI model acts as an intermediary between the raw operation log data and the malfunction diagnosis output. The sensing units collect operational data, which is then processed through the AI model to interpret patterns and determine malfunction states, bridging the gap between data collection and diagnostic conclusions.
2Measurement precision
If the AI device collects and stores operation logs for diagnosis, then the diagnosis accuracy improves, but the loss of time increases due to data collection and processing requirements
Solution Approach 1:
The sensing units continuously collect and store operation logs in memory during normal device operation, preparing diagnostic data in advance before any malfunction occurs. This preliminary data collection ensures that when a malfunction is suspected, the AI model can immediately process pre-collected logs without waiting for data gathering, reducing diagnostic time while maintaining accuracy.
3Reliability
If the AI device processes operation logs through an AI model to determine malfunction symptoms, then the reliability of malfunction diagnosis improves, but the device complexity increases due to additional processing requirements
Solution Approach 1:
The AI model serves as an intermediary processing layer that takes raw operation log data from the sensing units and transforms it into reliable malfunction diagnoses. This specialized component handles the complex pattern recognition and interpretation tasks, allowing the rest of the system to remain relatively simple while achieving high diagnostic reliability.
Data Source
AI summary
An artificial intelligence (AI) device includes a sensing unit configured to collect operation log including information on an external environment factor and an operation state of an AI device, a memory configured to store data corresponding to the operation log, and a processor configured to provide the data corresponding to the operation log to an AI model, to acquire information about whether the AI device corresponds to a normal range or a malfunction symptom range, and to perform control based on the acquired information.