Adaptive Diagnosis Model for Vehicle Device Reliability
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Solution Overview
Problem
Existing device diagnostic systems struggle to maintain high reliability when the driving environment of a vehicle changes, as they rely on learning data that becomes outdated or insufficient, leading to inaccurate diagnoses of device abnormalities.
Innovation Solution
A device diagnostic apparatus that utilizes a combination of statistical and physical models, where the statistical model generates spheres representing normal operation data in different driving environments and the physical model provides real-time diagnosis based on environmental parameters, prioritizing human judgment and experience for accurate device assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a statistical model is updated using accumulated learning data, then the reliability of the diagnosis model is improved over time, but when learning data is not obtained easily or device configuration changes, the reliability decreases and takes a lot of time to recover
Solution Approach 1:
The patent applies dynamics by making the diagnosis model adaptable to changing conditions. The system dynamically adjusts the diagnosis model when device configuration changes are detected, rather than relying solely on static accumulated learning data. This allows the model to remain reliable without requiring extensive relearning time, directly resolving the contradiction between maintaining reliability and avoiding time loss during recovery.
Solution Approach 2:
The patent implements preliminary action by detecting device configuration changes in advance and proactively adjusting the diagnosis model before reliability degradation occurs. Instead of waiting for reliability to decrease and then spending time recovering, the system anticipates changes and prepares the model accordingly, eliminating the time loss associated with reliability recovery.
2Reliability
If a diagnosis model is generated by combining multiple diagnosis models for fixed facilities, then the overall reliability is improved, but there is no assumption that a driving environment of a device changes, making it unsuitable for mobile devices
Solution Approach 1:
The patent makes the diagnosis model dynamic by incorporating environmental condition detection and adaptation capabilities. The system continuously monitors driving environment parameters and adjusts the diagnosis model accordingly, enabling it to adapt to changing conditions in mobile devices while maintaining the reliability benefits of combined multiple diagnosis models.
Solution Approach 2:
The patent applies local quality by tailoring the diagnosis model to specific local conditions or driving environments. Instead of using a single universal model, the system adjusts model parameters and characteristics based on local environmental factors, allowing the diagnosis system to maintain high reliability across diverse and changing conditions in mobile applications.
3Measurement precision
If learning data is accumulated before entering a tunnel, then the diagnosis model is trained, but after leaving the tunnel the learning data is of little use, causing the diagnosis model to output incorrect diagnosis results
Solution Approach 1:
The patent implements dynamics by making the diagnosis model responsive to real-time environmental changes. When the device exits the tunnel and environmental conditions change, the system dynamically adjusts the diagnosis model based on new sensor data and updated environmental parameters, preventing the model from producing incorrect results based on outdated tunnel-specific learning data.
Solution Approach 2:
The patent applies feedback by continuously monitoring environmental conditions and using this information to adjust the diagnosis model in real-time. The system incorporates feedback loops that detect changes in driving environment and automatically recalibrate the diagnosis model, ensuring that learning data remains relevant and diagnosis accuracy is maintained despite environmental transitions.
Data Source
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AI summary
A device diagnostic apparatus 1 according to the present invention includes: a diagnosis model selecting unit 24 that selects a diagnosis model 34 for diagnosing a moving device in accordance with a driving environment of the device; and a diagnosis unit 25 that inputs operation data of the device to the selected diagnosis model 34 , receives an output of a diagnosis result for the device from the diagnosis model 34, and diagnoses the device.