Tire Sensor Data Processing for Outlier Detection and Smoothing
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Solution Overview
Problem
Existing methods for processing measured values from tire sensors are unreliable due to the potential for erroneous readings from sensor malfunctions or unknown influences, leading to faulty data processing.
Innovation Solution
A method that processes measured values by considering previously acquired data, using averaging, median calculation, and Butterworth filtering to identify atypical deviations and smooth out fluctuations, thereby reducing the likelihood of incorrect processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If measured values are processed without considering previously acquired data, then processing speed is fast, but reliability of data processing deteriorates due to sensor malfunctions or unknown influences
Solution Approach 1:
The method creates a reference profile of measured values before actual processing begins. This preliminary reference data is stored and used to compare against newly acquired measured values, enabling detection of deviations caused by sensor malfunctions or unknown influences without requiring complex real-time analysis of historical data
Solution Approach 2:
The method implements feedback by comparing newly acquired measured values against the reference profile of previously acquired data. When deviations exceed a threshold, the system identifies potential sensor malfunctions or unknown influences and adjusts processing accordingly, creating a closed-loop system that continuously improves reliability
2Measurement precision
If individual measured values are processed independently, then processing is simple, but measurement precision deteriorates due to fluctuations and outliers
Solution Approach 1:
The method merges multiple measured values within a sliding time window to form a composite measurement result. By combining several individual measurements that show similar trends, the system achieves higher precision while filtering out random fluctuations and outliers through the reference profile comparison
3Stability of the object's composition
If all measured values are averaged without time consideration, then fluctuations are reduced, but time-based patterns and trends are lost
Solution Approach 1:
The method segments the measurement data by introducing a sliding time window that divides the continuous stream of measured values into discrete time-based groups. Each segment is processed independently with reference to its temporal context, maintaining stability through averaging while preserving time-based patterns and trends through the sliding window mechanism
Data Source
Figure 1~2
AI summary
The invention relates to a method for processing measured values, comprising the following steps: - providing a sensor (1), wherein the sensor (1) is designed to measure a physical quantity of a tire (2); - providing a tire (2); - arranging the sensor (1) in the tire (2); - acquiring measured values of the physical quantity by means of the sensor (1); - determining a measurement time at which the physical quantity was measured, wherein the measurement time can be uniquely assigned to a measured value; - providing a transmission unit (6), wherein the transmission unit (6) is designed to transmit measured values and measurement times; - transmitting the measured values and information about the measurement times by means of the transmission unit (6) to a storage unit (7); - processing a measured value, always taking into account previously acquired measured values.