Operator Fatigue Prediction Using Machine Data and Healing Models
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Solution Overview
Problem
Monitoring operator fatigue in industrial settings is challenging due to privacy concerns surrounding health data, and existing technologies lack the ability to predict fatigue without accessing sensitive health information.
Innovation Solution
A system utilizing a predictive model with a Rainflow counting algorithm and a healing function, which analyzes machine data to determine operator fatigue and accounts for rest periods to reduce degradation, allowing for fatigue prediction without requiring health data access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If health data is collected to monitor operator fatigue, then fatigue monitoring accuracy is improved, but operator privacy is compromised
Solution Approach 1:
The patent uses machine operational data as an intermediary to indirectly assess operator fatigue without directly collecting sensitive health information. Sensors on the machine capture operational parameters that reflect operator state, serving as a mediator between the operator's physiological condition and the monitoring system, thereby preserving privacy while enabling fatigue detection
Solution Approach 2:
The patent replaces direct physiological sensing (mechanical/biological measurement of health data) with indirect mechanical sensing of machine operational parameters. Instead of measuring operator heart rate, muscle activity, or other biological signals, the system measures machine vibrations, operational patterns, and performance metrics that correlate with operator fatigue
2Object-affected harmful factors
If machine data is analyzed to predict fatigue, then privacy protection is improved, but measurement precision of fatigue state deteriorates
Solution Approach 1:
The patent makes the machine data serve multiple functions: it controls machine operation and simultaneously provides information about operator fatigue state. The same sensors and data streams used for machine control are repurposed to assess operator condition, extracting multiple values from the same data source without requiring additional privacy-sensitive measurements
Solution Approach 2:
The system continuously analyzes machine data and provides feedback about operator fatigue levels, which can then inform adjustments in machine operation or operator scheduling. This closed-loop feedback mechanism refines fatigue detection accuracy over time by correlating machine performance patterns with known fatigue states
3Measurement precision
If continuous monitoring is implemented to track fatigue, then fatigue prediction accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent pre-processes and stores machine operational data as it is generated during normal operation, organizing it into usable formats before fatigue analysis is needed. This preliminary data preparation reduces the computational burden during actual fatigue assessment, as the data is already structured and ready for analysis rather than requiring intensive real-time processing
Data Source
AI summary
The example embodiments are directed to a system and method which can predict a degradation in the health of an asset that heals based on data sensed from a machine or equipment operated by the asset that heals and in consideration of healing of the asset. In one example, a method may include one or more of storing time-series data of an operation of a machine, predicting a fatigue value of an operator of the machine via a predictive model that comprises a Rainflow counting algorithm that determines a degradation of the operator based on a changing attribute in the time-series data and a healing function that determines a healing component of the operator based on rest of the operator, and outputting information about the predicted fatigue value via a user interface.