Semi-Trailer Wheel Vibration Classification for Early Anomaly Detection
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Solution Overview
Problem
Semi-trailer trucks and roads experience degradation or anomalies that can lead to damage, and existing systems lack effective early detection methods.
Innovation Solution
A system utilizing a vibration sensor and deep learning to predict anomalies in semi-trailer truck wheels and roads by analyzing vibration measurements and velocity profiles, with an electronic processor identifying potential issues and performing mitigation actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vibration sensors and deep learning systems are deployed to detect anomalies early, then reliability is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary classification of vibration measurements into multiple vibration levels before detailed anomaly analysis. This preliminary action filters and organizes data in advance, enabling the deep learning system to process only relevant anomalies, thus improving reliability while managing complexity through staged processing
Solution Approach 2:
The vibration measurement spectrum is segmented into multiple vibration levels (first vibration level, second vibration level, etc.) with different significance. This segmentation allows the system to prioritize and process critical anomalies separately from normal variations, improving detection reliability without overwhelming the system with all vibration data equally
2Measurement precision
If multiple vibration levels are classified to improve measurement precision, then measurement precision is improved, but processing time increases
Solution Approach 1:
Vibration measurements are preliminarily classified into multiple vibration levels before detailed anomaly identification. This preliminary classification organizes data by significance, allowing the system to quickly handle high-priority anomalies at higher vibration levels while processing lower levels with less urgency, thus improving measurement precision without excessive time loss
Solution Approach 2:
The system processes vibration measurements in a periodic manner, continuously monitoring and classifying vibrations into different levels. This periodic processing allows the system to maintain high measurement precision through continuous multi-level classification while managing time loss by systematically cycling through different vibration levels rather than processing all data simultaneously
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Early detection and mitigation of anomalies prevent damage to semi-trailer trucks and roads by providing timely alerts and operational adjustments.
Implementation Method 1
a vibration sensor positioned to sense vibrations of the wheel system
Data Source
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AI summary
Systems and methods for predicting anomalies in a wheel system of a semi-trailer truck. One example system includes: a vibration sensor positioned to sense vibrations of the wheel system and an electronic processor communicatively coupled to the vibration sensor. The electronic processor is configured to determine a velocity profile of the semi-trailer truck; obtain a vibration measurement; determine, from the vibration measurement, a classified vibration level; determine whether the classified vibration level is indicative of an anomaly; identify, based on whether the classified vibration level is indicative of the anomaly and both the velocity profile and a reference classified vibration level, an anomaly existing within either or both of the semi-trailer truck wheel system or the road; and perform a mitigation action in response to identifying the anomaly.