Power Tool Sensor Fusion for PPE-Free Operator Protection
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Solution Overview
Problem
Existing power tool safety systems rely on single types of sensors, which have inherent advantages and disadvantages, leading to suboptimal performance and user discomfort due to the need for personal protective equipment (PPE), especially in prolonged use.
Innovation Solution
A system employing sensor fusion that integrates multiple types of sensors, including IMU, distance, magnetic, electronic tag, and optical sensors, with a controller for self-calibration, self-assessment, and self-healing to enhance accuracy and reliability, allowing operation without PPE.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple types of sensors are employed for protection, then safety and measurement accuracy are improved, but device complexity increases
Solution Approach 1:
The system divides the protection function into multiple independent sensor networks (first, second, third sensor networks), each responsible for specific measurement tasks. This segmentation allows each sensor type to specialize in particular hazard detections while maintaining overall system reliability without requiring a single complex sensor system.
Solution Approach 2:
The controller merges data from multiple sensor networks through sensor fusion algorithms to create a comprehensive safety assessment. By combining measurements from different sensor types (accelerometers, gyroscopes, magnetic sensors, optical sensors), the system achieves improved reliability and accuracy while managing complexity through integrated processing.
2Measurement precision
If sensor fusion is implemented, then measurement precision and reliability are improved, but device complexity increases
Solution Approach 1:
The controller continuously monitors performance data from each sensor network and uses feedback loops to adjust sensor fusion weights and parameters. This feedback mechanism optimizes measurement precision by adapting to changing operational conditions while managing complexity through automated adjustment rather than manual configuration.
Solution Approach 2:
The system dynamically changes fusion parameters and sensor weighting based on operational context and sensor performance. By adjusting parameters such as sensor confidence weights, threshold values, and fusion algorithms based on real-time conditions, the system maintains high measurement precision without requiring fixed complex configurations.
3Reliability
If performance monitoring and self-healing are added, then reliability is improved, but device complexity increases
Solution Approach 1:
The controller performs self-assessment and self-healing by automatically monitoring sensor performance data and detecting degradation or failures. When issues are detected, the system autonomously adjusts sensor fusion parameters, switches to backup sensors, or recalibrates without external intervention, improving reliability while managing complexity through automated self-management.
Solution Approach 2:
The system continuously monitors sensor performance data to detect early signs of degradation before actual failures occur. By performing preliminary assessment and taking preventive actions (such as adjusting fusion weights or initiating calibration routines) before failures happen, the system maintains high reliability without requiring complex reactive repair mechanisms.
Data Source
AI summary
A system for protecting an operator (110) of a power tool (100) may include a first sensor network (240), a second sensor network (250), a third sensor network (252), and a controller (140) configured to detect a trigger event based on measurements made by the first, second and third sensor networks (240, 250 and 252) and initiate a protective action with respect to the power tool (100) responsive to detecting the trigger event. The controller (140) may be further configured to monitor performance data associated with each of the first, second and third sensor networks (240, 250 and 252) to perform sensor fusion based on the performance data.