Vehicle Sensor Data Analytics Using Data Index Maps
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Solution Overview
Problem
Vehicle health management systems face challenges in efficiently processing large volumes of sensor data from vehicles, leading to delays in identifying relevant information for real-time analytics and maintenance planning.
Innovation Solution
A method and system for data stream analytics that processes sensor data in both time and frequency domains, identifies differences exceeding predefined thresholds, and records these differences in a data index map (DIM) for further analysis, enabling real-time monitoring and action triggering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all sensor data is stored for offline processing, then complete data is available for analysis, but processing time and resources increase significantly
Solution Approach 1:
The patent extracts only the most relevant features from the complete sensor data using signal processing techniques (FFT, wavelet transforms, statistical measures). Instead of storing and processing all raw data, the system extracts key characteristics such as frequency components, time-domain statistics, and spectral features, thereby reducing data volume while maintaining analytical value.
Solution Approach 2:
The patent segments the continuous sensor data stream into discrete analysis windows or epochs. Each segment is processed independently to identify anomalies or patterns, allowing the system to handle large volumes of data in manageable portions rather than processing everything at once, thus reducing overall processing time while maintaining detection accuracy.
2Reliability
If all sensor data is stored for offline processing, then complete data is available for analysis, but system complexity and resource requirements increase
Solution Approach 1:
The patent extracts only the most relevant features from the complete sensor data using signal processing techniques (FFT, wavelet transforms, statistical measures). Instead of storing and processing all raw data, the system extracts key characteristics such as frequency components, time-domain statistics, and spectral features, thereby reducing data volume while maintaining analytical value.
Solution Approach 2:
The patent performs preliminary signal processing and feature extraction at the edge device or data acquisition stage, before data is stored or transmitted for offline analysis. By preprocessing the data and extracting relevant features in advance, the system reduces the complexity of subsequent offline processing and minimizes the computational resources required at centralized facilities.
3Speed
If real-time processing is implemented, then faster anomaly detection is achieved, but processing accuracy may be compromised
Solution Approach 1:
The patent performs preliminary signal processing and feature extraction at the edge device or data acquisition stage, before data is stored or transmitted for offline analysis. By preprocessing the data and extracting relevant features in advance, the system reduces the complexity of subsequent offline processing and minimizes the computational resources required at centralized facilities.
Solution Approach 2:
The patent segments the continuous sensor data stream into discrete analysis windows or epochs. Each segment is processed independently to identify anomalies or patterns, allowing the system to handle large volumes of data in manageable portions rather than processing everything at once, thus reducing overall processing time while maintaining detection accuracy.
Data Source
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AI summary
A method includes receiving a stream of sensor data regarding operating conditions of a vehicle (303), and processing first and second data sets of the stream of sensor data in time and frequency domains. The method also includes identifying differences within and between processing results from processing the first and second data sets, and determining that at least one difference in the identified differences is greater than or equal to one or more predefined thresholds. Further, the method includes, based on determining the at least one difference in the identified differences being greater than or equal to the one or more predefined thresholds, recording one or more processing results (920) corresponding to the identified at least one difference in a data index map (DIM) (922), and analyzing contents of the DIM (922) to evaluate operating conditions of the vehicle (303).