Guide Vibration Sensing for Low-Energy Fatigue Detection
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Solution Overview
Problem
Existing guide monitoring technologies require significant computational effort and energy resources for fatigue detection, making them inefficient and resource-intensive, especially in environments with limited installation space.
Innovation Solution
A guide system equipped with a sensor for detecting vibration signals and an evaluation device that digitally filters these signals to determine movement profiles, allowing for fatigue detection with reduced computing effort and energy consumption, using techniques like low-pass filtering and discrete wavelet transformation to analyze specific frequency bands and movement phases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire movement profile is evaluated for fatigue detection, then the prediction accuracy and reliability of fatigue detection is improved, but the computational effort and energy consumption increase significantly
Solution Approach 1:
The movement profile is divided into multiple segments or portions, and only selected portions are used for fatigue detection. This segmentation allows the system to maintain reliable fatigue detection by focusing on critical segments while reducing the overall computational burden and energy consumption associated with processing the entire movement profile.
Solution Approach 2:
Instead of evaluating the complete movement profile, the system applies partial action by selecting and evaluating only specific portions or segments of the movement profile that are most relevant for fatigue detection. This approach maintains adequate prediction reliability while significantly reducing computational effort and energy requirements.
2Reliability
If the entire movement profile is evaluated for fatigue detection, then the prediction accuracy and reliability of fatigue detection is improved, but the device complexity increases
Solution Approach 1:
The evaluation device is designed to process segmented portions of the movement profile rather than the entire profile. This reduces the complexity of the evaluation device by limiting the data volume and processing requirements while maintaining reliable fatigue detection through strategic selection of critical movement segments.
Solution Approach 2:
The evaluation device performs partial evaluation of the movement profile by focusing only on selected segments. This partial action approach simplifies the device architecture and reduces computational complexity while preserving the essential functionality needed for reliable fatigue detection.
3Productivity
If digital filtering is applied to the vibration signal, then the computational effort for fatigue detection is reduced, but the processing time may increase
Solution Approach 1:
Digital filtering is applied as a preliminary processing step before fatigue detection analysis. By pre-filtering the vibration signal to remove irrelevant frequency components and noise, the system reduces the computational effort required for subsequent fatigue detection while managing processing time through efficient filter design and optimization.
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 efficient and predictive maintenance with reduced unplanned downtimes by accurately detecting fatigue with lower computational and energy requirements, allowing for autonomous operation in guides with limited space.
Implementation Method 1
A sensor is provided on the guide component for detecting a vibration signal or vibration
Implementation Method 2
Provision is also made for the vibration signal detected by the sensor to be digitally filtered by the evaluation device
Data Source
Figure 1a~1b
Figure 2a~2b
Figure 3
AI summary
According to the invention, a sensor arrangement, particularly for a guide, is provided, comprising a sensor and an evaluation device. Based on a vibration signal detected by the sensor, the evaluation device determines a motion profile of the guide component, a portion of which is used for fatigue detection. Alternatively or additionally, the vibration signal detected by the sensor can be filtered or digitally filtered, and the motion profile of the guide component can be determined based on the filtered or digitally filtered signal.