Automatic Transmission Fault Evaluation Using Learning Progress
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Solution Overview
Problem
Existing fault evaluation devices for automatic transmissions struggle to accurately determine the presence of faults due to variations in shifting shock, which can be influenced by factors other than transmission faults, such as the status of the learning process for oil pressure correction.
Innovation Solution
A fault evaluation device that includes a processor and memory with mapping data, using acceleration and learning progress variables to output an evaluation value indicating the presence or absence of a fault, considering both the acceleration during shifting and the status of the learning process for oil pressure correction, allowing for accurate evaluation irrespective of the learning process's progress.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fault evaluation is based solely on acceleration during shifting, then the evaluation process is simple, but the accuracy of fault detection deteriorates due to false positives from learning phase variations
Solution Approach 1:
The patent transitions from one-dimensional evaluation (acceleration only) to two-dimensional evaluation by adding the learning progress variable as a new dimension. This allows the system to distinguish between acceleration variations caused by learning phase adjustments versus those caused by actual transmission faults, thereby improving detection accuracy without significantly increasing system complexity.
Solution Approach 2:
The patent changes the evaluation parameters from solely acceleration-based to a combined parameter set including acceleration and learning progress. By monitoring how the learning progress variable changes over time and correlating it with acceleration patterns, the system can filter out false positives during the learning phase while maintaining simple evaluation logic.
2Productivity
If the evaluation considers only acceleration variations, then the evaluation is quick and simple, but it cannot distinguish between learning-induced variations and fault-induced variations
Solution Approach 1:
The learning progress variable serves as an intermediary parameter that mediates between the acceleration signal and the final fault evaluation. By introducing this intermediate indicator that tracks the learning phase status, the system can quickly assess whether acceleration variations are due to learning adjustments or actual faults, maintaining evaluation speed while improving reliability.
3Device complexity
If fault evaluation ignores the learning process status, then the evaluation system remains simple, but it produces false positives during the learning phase
Solution Approach 1:
The system performs preliminary tracking of the learning progress variable before conducting fault evaluation. By pre-establishing the learning status and comparing it with acceleration patterns, the system can proactively identify whether observed variations are expected during learning or indicate actual faults, improving precision without adding significant complexity to the evaluation architecture.
Data Source
AI summary
A fault evaluation device for an automatic transmission evaluates a fault of the automatic transmission. The fault evaluation device is used for a vehicle having the automatic transmission and a control device configured to execute a learning process for correcting a target pressure for oil to be supplied to the automatic transmission such that variations in acceleration of the vehicle during shifting of the automatic transmission are small. The fault evaluation device includes a processor and a memory. The memory stores mapping data that prescribe mapping. The processor is configured to output an output variable, which is an evaluation value that indicates the presence or absence of the fault of the automatic transmission, when an input variable is input.