Vehicle Failure Warning Using Real-Time Predictive Data Screening
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Solution Overview
Problem
Conventional remote monitoring and diagnostic systems for vehicles are retrospective and sluggish, leading to irreversible damage and safety risks due to delayed detection of vehicle failures, as they only diagnose issues after they have occurred and are slow to update data.
Innovation Solution
A vehicle failure warning system that collects data from multiple vehicles, screens it using a data screening module, generates a failure prediction model using a big-data processing algorithm, and predicts imminent failures through a failure prediction module, issuing alerts before the failure occurs, thereby reducing maintenance costs and preventing dangerous situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional remote monitoring and diagnostic systems are used, then vehicle data can be collected and diagnosed, but the system is retrospective and sluggish, leading to delayed detection of vehicle failures
Solution Approach 1:
The system performs preliminary actions by continuously collecting and analyzing vehicle data to establish baseline healthy states and early failure patterns before actual failures occur. The data collection module gathers historical data, and the failure prediction module analyzes this data to detect early signs of failure, enabling proactive maintenance before the vehicle actually fails.
Solution Approach 2:
The system implements feedback mechanisms where the failure prediction module continuously monitors vehicle data, compares it against learned patterns from healthy and failing vehicles, and provides real-time feedback about failure risk. This feedback loop enables the system to adapt and improve its prediction accuracy over time, reducing both false positives and missed detections.
2Ease of manufacture
If conventional retrospective diagnostic systems are used, then maintenance can be performed after failure occurs, but this leads to irreversible damage and safety risks
Solution Approach 1:
The system performs preliminary diagnostic actions by analyzing vehicle data to predict failures before they occur. The failure prediction module identifies early patterns indicating impending failure, allowing maintenance to be scheduled proactively. This prevents irreversible damage and safety risks by addressing issues while they are still in early stages, rather than waiting for complete failure.
Solution Approach 2:
The system takes preliminary anti-action by predicting and alerting potential failures before they manifest as actual problems. The failure prediction module generates warnings that enable preventive maintenance actions, counteracting the development of failure conditions before they can cause damage or safety hazards.
3Ease of operation
If manufacturer-provided remote monitoring systems are used, then vehicle monitoring is available, but the system is conservative and slow in data update
Solution Approach 1:
The system performs preliminary data processing and analysis actions locally or in the cloud before failures occur. By continuously collecting and pre-analyzing vehicle data, the system prepares failure predictions in advance, enabling rapid response when actual failures are detected without the need for slow, conservative update cycles.
Solution Approach 2:
The system implements dynamic data update mechanisms where the frequency and depth of data collection and analysis adapt based on vehicle conditions. When early failure patterns are detected, the system increases monitoring intensity and update speed, while maintaining simplicity during normal operation. This dynamic approach balances speed and ease of operation based on actual needs.
Data Source
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AI summary
The present invention relates to a vehicle failure warning system (2), wherein the vehicle failure warning system (2) is in communication connection with a plurality of vehicles and is capable of warning the vehicles of an imminent failure. The vehicle failure warning system (2) comprises: a data collection module (15) configured to collect vehicle data from the plurality of vehicles within a time span to form a data cluster; a data screening module (18) configured to screen data from the data cluster based on characteristics of a failure prediction model to be generated; a prediction model generation module (16) configured to construct the failure prediction model for predicting a vehicle failure, from the screened data using a big-data processing algorithm; and a failure prediction module (17) configured to predict, in the situation where the failure prediction model is called and based on real-time vehicle data, whether there is an imminent failure in the vehicle. The invention further relates to a corresponding vehicle failure warning method.