AI Training Data for Hand-Held Power Tool State Transitions
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Solution Overview
Problem
Existing methods for generating training datasets for artificial intelligence in hand-held power tools lack precision in accounting for both user inputs and real-time operating states, leading to suboptimal control during transitions between different operating states.
Innovation Solution
A method that involves receiving sensor data from hand-held power tools to ascertain operating states and predict event timepoints, using a state determination module to adjust control parameters based on both user inputs and current tool states, ensuring precise control by calculating output target values that consider both user-defined and state-adapted settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If control is based solely on user input, then ease of operation is maintained, but control precision during state transitions deteriorates
Solution Approach 1:
The control system continuously monitors operating state variables (current, speed, torque) and uses this feedback to automatically adjust control parameters during state transitions. The AI model predicts transition events based on real-time sensor data, enabling the system to adapt control settings proactively rather than relying solely on user input, thus maintaining precision without compromising ease of operation.
Solution Approach 2:
The hand-held power tool performs self-diagnosis and self-adjustment by using its own sensor data to detect operating states and predict transitions. The control system autonomously modifies control parameters based on predicted events, making the system self-regulating without requiring additional user intervention, thereby maintaining both precision and ease of operation.
2Measurement precision
If AI model complexity is increased to improve state recognition accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The AI model is divided into modular components: a state determination module that processes sensor data, an event prediction module that identifies transition events, and a control module that adjusts parameters. This segmentation allows each component to be optimized independently and facilitates deployment on resource-constrained embedded systems while maintaining high recognition accuracy.
Solution Approach 2:
The AI model is trained offline using pre-collected operational data to learn patterns of state transitions. This preliminary training enables the model to achieve high recognition accuracy without requiring complex real-time computations during operation, as the heavy computational work is completed in advance during the training phase.
3Adaptability or versatility
If control parameters are continuously adjusted based on real-time state detection, then adaptability improves, but loss of time in processing and responding increases
Solution Approach 1:
The AI model predicts state transitions before they occur by analyzing trends in sensor data and recognizing patterns learned during offline training. This preliminary prediction allows the control system to prepare adjustment parameters in advance, reducing the actual processing time required when transitions occur, thus maintaining high adaptability without significant time loss.
Solution Approach 2:
The system uses event-driven control where continuous monitoring is supplemented by periodic predictions from the AI model. Instead of continuously adjusting control parameters, the system skips to key transition events predicted by the AI model and makes adjustments only at these critical moments, reducing processing time while maintaining adaptability.
Data Source
AI summary
A method is for generating a training dataset for training an artificial intelligence system in order to ascertain an operating state and/or to predict an event timepoint of a hand-held power tool. The method includes providing a plurality of measured values of an operating variable of a hand-held power tool, and identifying an event timepoint within the plurality of measured values. The method also includes arranging a plurality of labeled measured values in a time series based on timestamps of the measured values, and providing a training dataset including the time series of labeled measured values of the operating variable.