Vehicle Sensor Status Prediction Using Remote Data Analysis
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Solution Overview
Problem
Existing methods for predictive maintenance in vehicles, particularly for sensors like oxygen sensors, face challenges in analyzing large amounts of data and identifying potential malfunctions in real-time, leading to unscheduled maintenance and inconvenience for customers.
Innovation Solution
A data-driven approach using a computing system to collect and analyze data from vehicle systems, selecting relevant data sets based on correlation coefficients, transforming data using mathematical properties, and classifying it with a neural network to predict the status of sensors, such as oxygen sensors, to identify clogging or degradation before it occurs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data collection and analysis is performed locally at the vehicle by the ECU, then real-time processing is achieved, but processing and storage capabilities are limited making analysis of significant amounts of data infeasible
Solution Approach 1:
The patent introduces a remote server as an intermediary between the vehicle's ECU and the data analysis process. The ECU collects sensor data and transmits it to the remote server, which then performs the complex analysis of significant amounts of historical and real-time data. This intermediary approach allows the vehicle's limited ECU to leverage the remote server's sophisticated processing and storage capabilities without requiring local hardware upgrades.
2Measurement precision
If sophisticated tools are used for remote data analysis, then predictive maintenance accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the data analysis system into distinct functional modules: data collection module (ECU), data transmission module, data storage module (remote server), and analysis module (neural networks). Each module performs a specific function, allowing the sophisticated analysis tools to be deployed remotely without increasing the complexity of individual vehicle components. The segmentation enables independent optimization of each module while maintaining overall system efficiency.
3Reliability
If more sensor data is collected and analyzed, then predictive maintenance capability is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and storing sensor data in structured formats at the remote server before analysis is needed. Historical data is collected, organized, and made readily accessible in advance, allowing the neural networks to perform rapid analysis when predictive maintenance predictions are required. This pre-positioning of data eliminates the need for time-consuming data aggregation during the prediction process.
Data Source
AI summary
A system and method of determining system status in a vehicle system. The method including collecting, by a computing system, a plurality of data associated with a test specimen and the vehicle system, selecting a relevant data set of the plurality of data, the selecting based on at least one correlation coefficient associated with the plurality of data, and transforming at least a portion of the selected relevant data to form a transformed data set, the transforming based on mathematical properties. The method also includes collecting statistics associated with the selected relevant data set and the transformed data set to form a statistics data set, classifying the selected relevant data set, transformed data set, and the statistics data set; and predicting a status of a system based on the classifying.