Sensor-Driven Machine Function Adjustment for Operator Safety
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Solution Overview
Problem
Workplace accidents involving heavy machines or manufacturing equipment often occur due to factors such as operator training, skill level, focus, or improper operation, leading to injuries for both the operator and surrounding employees.
Innovation Solution
A dynamic machine functionality system that uses machine learning (ML) and artificial intelligence (AI) to analyze data from sensors and wearable devices, adjusting machine features such as motion limitations, velocity, and functionality based on operator conditions and environmental factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine functionality is kept available for all operators, then productivity is maintained, but safety risk increases due to operator fatigue, stress, or improper operation
Solution Approach 1:
The system dynamically adjusts machine functionality availability based on real-time operator condition monitoring. Sensors continuously collect data on operator stress, attention, and fatigue levels, and the system automatically modifies which machine features are accessible depending on the operator's current state, resolving the contradiction between maintaining productivity and ensuring safety
Solution Approach 2:
The system implements continuous feedback loops where sensor data about operator conditions is constantly monitored and fed back to adjust machine functionality. This closed-loop control ensures that safety requirements are met while minimizing impact on productivity through automated, real-time adjustments rather than static restrictions
2Reliability
If machine functionality is restricted to ensure safety, then safety risk is reduced, but productivity decreases due to limited machine features
Solution Approach 1:
Rather than applying static restrictions to machine functionality, the system uses dynamic adjustment based on real-time operator assessment. When operators are assessed as being in good condition, full machine functionality is available; when fatigue or stress is detected, restrictions are applied only temporarily and automatically, thus minimizing productivity loss while maintaining safety
Solution Approach 2:
The system changes operational parameters of the machine based on operator condition. Instead of permanently restricting functionality, it temporarily adjusts available features, speed limits, or operational modes based on measured parameters like heart rate variability, attention levels, and stress indicators, allowing full productivity recovery when operators are fit to work
3Measurement precision
If comprehensive sensor data is collected to accurately assess operator condition, then assessment accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the operator monitoring function into multiple independent sensor modules, each measuring specific physiological or behavioral parameters (heart rate, skin conductance, eye movement, etc.). This modular approach improves measurement precision through specialized sensors while managing complexity by keeping each sensor module independent and standardized
Solution Approach 2:
The system uses multi-functional sensor platforms that can detect multiple types of operator conditions simultaneously. A single sensor system assesses various parameters including stress, fatigue, attention, and emotional state, reducing overall system complexity compared to using separate specialized systems for each measurement type
Data Source
AI summary
A method, computer system, and a computer program product for a dynamic machine functionality is provided. The present invention may include setting a threshold for a machine. The present invention may include setting one or more features of the machine available to a user based on the set threshold. The present invention may include determining the machine is being operated. The present invention may include collecting a plurality of data based on the machine operation. The present invention may include transmitting the plurality of data to a machine learning (ML) module. The present invention may include analyzing the collected plurality of data using the ML module. The present invention may include determining that at least one machine functionality of the one or more features should be adjusted based on analysis of the collected plurality of data. The present invention may include adjusting the at least one machine functionality.