Abnormal Sound Diagnosis Flow for Faster Cause Isolation
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Solution Overview
Problem
Existing systems struggle to efficiently narrow down the causes of vehicle abnormal sounds, leading to inefficient diagnosis processes and increased man-hours due to a lack of effective data input strategies.
Innovation Solution
An abnormal sound diagnosis system that dynamically adjusts the input of data items based on received information, using a server and inquiry terminal to narrow down candidates for the cause of the sound by selecting the most effective data to collect next, such as sound data or driving conditions, through a process that includes scoring or AI-based recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all candidates for the cause of abnormal sound are investigated, then the accuracy of diagnosis is improved, but the time and man-hours required for diagnosis increase
Solution Approach 1:
The diagnosis process is segmented into multiple stages: initial data collection, candidate cause generation, candidate evaluation, and final diagnosis. By dividing the investigation into manageable segments rather than examining all candidates simultaneously, the system reduces overall diagnosis time while maintaining accuracy through systematic progression through each segment.
Solution Approach 2:
The system performs preliminary actions by pre-storing abnormal sound data, vehicle information, and maintenance history in databases before actual diagnosis occurs. When an abnormal sound is detected, the system immediately queries and evaluates candidates against this pre-prepared information, avoiding the need to gather all data from scratch during the diagnosis process.
2Loss of information
If multiple data items are collected for diagnosis, then the completeness of information is improved, but the complexity of data management increases
Solution Approach 1:
The server is designed as a universal platform that handles multiple functions: storing abnormal sound data, vehicle information, maintenance history, generating candidate causes, evaluating candidates, and providing diagnostic recommendations. This multi-functional design consolidates what would otherwise require separate systems for each function, reducing overall system complexity while maintaining information completeness.
Solution Approach 2:
The server acts as an intermediary between the sound detection device, vehicle data sources, and the diagnosis process. It centralizes data collection, storage, and processing, mediating between various data sources and the diagnostic algorithms, thereby simplifying data management complexity while ensuring all necessary information is captured and integrated.
Data Source
AI summary
In a case where input of data of a part of a plurality of items needed for a diagnosis is received, an item of which data is to be input next is changed based on the received data, so that an item that can efficiently reduce the candidates for the cause of the abnormal sound can be used as the item of which the data is to be input next, according to the received data. As a result, the candidates for the cause of the abnormal sound are efficiently narrowed down, and the number of man-hours needed to specify the cause of the abnormal sound can be reduced.