Gas Spring Life Indicator Using Sensor-Based Wear Prediction
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Solution Overview
Problem
Gas spring assemblies in vehicles experience performance degradation due to cyclical flexing and environmental exposure, making it challenging to predict their remaining life effectively.
Innovation Solution
An indicator system comprising sensors and processors that monitor usage and environmental conditions to estimate the remaining life of gas spring assemblies by deriving data from signals and using models to predict wear and degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If gas spring assemblies are used in suspension systems with cyclical flexing and environmental exposure, then the suspension system provides load support and height adjustment functionality, but the flexible sleeve experiences performance degradation and reduced remaining life
Solution Approach 1:
The system performs preliminary monitoring of usage conditions and environmental exposure factors, then predicts remaining life before actual failure occurs. Sensors continuously collect data on cyclical flexing, temperature, and other environmental conditions, allowing the system to anticipate degradation and schedule maintenance proactively rather than reactively.
Solution Approach 2:
The system implements continuous feedback loops where sensors monitor the gas spring assembly's operational conditions and environmental exposure in real-time. This feedback is processed to update the remaining life prediction, creating a closed-loop system that adapts to actual usage patterns and environmental variations, thereby improving reliability assessment accuracy.
2Measurement precision
If sensors and processors are added to monitor usage conditions and environmental exposure, then the prediction accuracy of remaining life is improved, but the device complexity increases
Solution Approach 1:
The indicator system is designed with multi-functionality to justify its complexity. The same sensors and processors that predict remaining life also monitor usage conditions, track environmental exposure, and provide data for maintenance scheduling. This universal approach allows one system to perform multiple functions, reducing the need for separate dedicated components for each measurement task.
Solution Approach 2:
The system focuses on monitoring key parameters that have the greatest impact on remaining life prediction accuracy. Rather than attempting to measure all possible conditions, the system identifies and tracks critical parameters such as cyclical flexing frequency, temperature ranges, and exposure to environmental factors, using computational models to derive comprehensive assessments from these selected measurements.
Data Source
AI summary
An indicator of an estimated remaining life of an associated spring device can include at least one sensor and at least one processor. The at least one sensor can be operative to generate a signal having a relation to at least one of a usage condition and an environmental exposure condition of a spring device. The at least one processor can be communicatively coupled with the at least one sensor and programmed to receive signals from the at least one sensor, derive data from the received signals, and determine an estimated remaining life of at least the spring device using the data and an expression modeling a relationship between an estimated remaining life of the spring device and at least one of a usage condition and an environmental exposure condition. A gas spring and indicator assembly, a suspension system and a method are also included.