Wireless Strain Sensor Fatigue Prediction
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Solution Overview
Problem
Current monitoring systems for fatigue damage in structural components of machines, especially those with rapidly changing load states, face challenges in accurately detecting critical load states due to low sampling rates and difficulties in processing and transmitting high volumes of data, particularly in harsh environments.
Innovation Solution
A monitoring system with wireless nodes equipped with strain sensing devices and processors that measure and predict fatigue life by calculating unknown forces using Newtonian force balance and stress-strain calculations, and employing neural networks for rapid data processing and high sampling rates, allowing for real-time strain calculation and fatigue life prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high sampling rates are used to capture critical load states, then measurement precision is improved, but data volume increases making processing and transmission difficult
Solution Approach 1:
The patent segments the data processing task by performing local processing at wireless nodes to filter and prepare data before transmission. Each node processes strain measurements locally to identify critical load states, reducing the volume of data that needs to be transmitted and processed centrally while maintaining detection accuracy.
Solution Approach 2:
The patent extracts only the essential information from high-volume data streams at the wireless nodes. By filtering and selecting only critical load state data for transmission rather than transmitting all raw data, the system reduces data volume while preserving measurement precision for fatigue analysis.
2Reliability
If high sampling rates are used to capture brief critical load states, then reliability is improved, but energy consumption increases
Solution Approach 1:
The patent implements periodic sampling at high rates only when critical load states are detected or anticipated. The system switches between high-rate sampling during critical events and lower-rate sampling during normal operation, maintaining detection reliability while reducing overall energy consumption of the wireless monitoring nodes.
Solution Approach 2:
The wireless nodes autonomously determine when high-rate sampling is necessary by monitoring their own data for critical patterns. This self-service approach allows the system to maintain high reliability for detecting brief critical load states without continuously consuming high energy, as the nodes activate intensive sampling only when needed.
3Device complexity
If manual visual inspection is used to monitor fatigue damage, then device complexity is reduced, but measurement precision and reliability deteriorate
Solution Approach 1:
The patent replaces manual visual inspection with wireless sensing nodes that automatically measure strain and detect fatigue damage. The mechanical/visual inspection method is substituted with electronic strain sensing and wireless data transmission, significantly improving measurement precision and reliability while keeping device complexity manageable through distributed sensing architecture.
4Device complexity
If low sampling rates are used to reduce computing burden, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent segments the sampling and processing tasks across multiple wireless nodes distributed throughout the structure. Each node performs local processing at appropriate sampling rates for its specific location, and only transmits processed data to central systems. This distribution reduces the computing burden at any single location while maintaining overall measurement precision through multiple measurement points.
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
Enables accurate detection of fatigue damage and prediction of remaining fatigue life, facilitating proactive maintenance and improving the reliability of structural components in machines with rapidly changing load states and harsh environments.
Implementation Method 1
calculating unknown forces using Newtonian force balance and stress-strain calculations
Data Source
AI summary
A monitoring system is provided, which may include a structural component configured to undergo mechanical loading and a wireless node attached to the structural component. The node may include a strain sensing device configured to measure strain experienced by the structural component at the location of the node. The node may also include a processor configured to predict, based on the strain measurements, fatigue life of the structural component.