Chassis Condition Assessment via Vibration Analysis
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Solution Overview
Problem
Current vehicle diagnosis systems cannot predict or accurately assess the wear of components, which can lead to reliability and safety issues due to excessive wear, particularly in critical systems like brakes, as they only report faults when parameters exceed acceptable ranges, failing to provide timely warnings.
Innovation Solution
A method and system that utilize a first data set of loading and wear data from similar chassis types over their entire service life, combined with an individual second data set from the vehicle's target condition, to compare current vehicle data and determine the chassis condition, using acceleration sensors and machine-learning methods to analyze vibration behavior and road surface clusters for accurate assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current vehicle diagnosis systems only report faults when parameters exceed acceptable ranges, then the system complexity remains low, but the reliability and safety of the vehicle deteriorate due to inability to predict component wear and failures
Solution Approach 1:
The system creates a target condition data set during a preliminary learning phase (first predefined kilometers-travelled count) that represents the normal vibration behavior of the vehicle. This preliminary action enables future wear detection by comparing current vibrations against the established baseline, allowing prediction of component failures before they occur.
Solution Approach 2:
The diagnosis system is segmented into multiple independent components: vibration sensors for data collection, machine learning models for pattern recognition, comparison units for analyzing deviations from target conditions, and warning systems for alerting. This segmentation allows the system to achieve high reliability through specialized functions while managing overall complexity through modular architecture.
2Measurement precision
If vibration behavior analysis is used to assess chassis wear, then the measurement capability is enhanced, but the precision of wear assessment deteriorates due to lack of unique indicators and infinite combinations of component replacements
Solution Approach 1:
The system continuously monitors vibration behavior and compares it against the target condition data set, providing feedback on deviations that indicate wear. This feedback mechanism enables precise wear assessment by quantifying changes in vibration patterns over time, allowing the system to identify unique wear indicators even in the presence of multiple component variations.
Solution Approach 2:
The system analyzes changes in vibration parameters (frequency, amplitude, spectral characteristics) to detect wear. By monitoring parameter changes over time and comparing them against the established target condition, the system can precisely assess wear levels and identify component degradation patterns despite variations in chassis configurations.
3Reliability
If replacement of worn chassis components is performed, then the reliability is restored, but the device complexity increases due to infinite combinations of possible component replacements
Solution Approach 1:
The system replaces complex mechanical inspection and diagnosis procedures with automated vibration analysis and machine learning algorithms. Sensors continuously monitor chassis vibrations, and computational models automatically identify wear patterns and predict failures, eliminating the need for manual inspection and simplifying the maintenance decision-making process.
Solution Approach 2:
The diagnosis system performs self-assessment by continuously monitoring its own vehicle's vibration behavior and automatically comparing it against the target condition. The system generates its own diagnostic information and can trigger maintenance alerts without external intervention, enabling proactive maintenance while reducing the complexity of manual assessment procedures.
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
This approach allows for a clear and reliable assessment of the chassis condition, enabling early detection of wear and potential failures, thereby enhancing driving safety by providing precise information on the wear level of components, such as wheel suspensions and brakes, and facilitating timely replacements.
Implementation Method 1
the said first acoustic sensor, which detects a noise produced by the chassis component during driving operation of the vehicle and generates an electric signal
Implementation Method 2
for this purpose, at least one acceleration sensor is provided in the vehicle
Data Source
AI summary
A method for determining a condition of the components of an individual chassis includes preparing a first data set of vehicle data, which includes at least loading and/or wear data of the same or a similar chassis type over its entire lifetime, producing an individual second data set for a particular vehicle by determining the vehicle data as a target condition up to a predefined first kilometers-travelled count and/or a specified age of the individual vehicle, determining currently measured vehicle data from a predefined first kilometers-travelled count and/or a specified age of the particular vehicle, and comparing the currently measured vehicle data with the first data set and also the second data set, in order to determine the condition. Also disclosed is a vehicle system configured to execute the method and vehicle with the vehicle system.