Handheld Tool State Classifier Using Sensor Data Fusion
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Solution Overview
Problem
Existing handheld machine tools lack effective methods for accurately determining their operational states, especially during continuous use, which can lead to undetected damage or functional restrictions, affecting performance and reliability.
Innovation Solution
A method involving multiple sensors, principal component analysis (PCA), and machine learning algorithms to extract and classify device states, including damage detection, by capturing sensor data during continuous operation and training classifiers to identify states such as new, used, and defective conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If continuous operation of handheld machine tool is performed without state monitoring, then productivity is improved, but reliability deteriorates due to undetected damage
Solution Approach 1:
The machine tool performs self-diagnosis by automatically capturing sensor data during operation, extracting features, and determining device states without external intervention. The system monitors its own operational state continuously, enabling uninterrupted productivity while maintaining reliability through autonomous damage detection.
2Reliability
If multiple sensors are added to monitor device states, then reliability is improved, but device complexity increases
Solution Approach 1:
Multiple sensors (acceleration, temperature, current, acoustic emission) are integrated into a unified monitoring system that captures data through a single interface. The sensor data fusion approach combines information from various sources into comprehensive device state assessments, improving reliability while managing complexity through integration rather than separate systems.
Solution Approach 2:
The patent extracts only the most relevant features from raw sensor data using feature extraction techniques. Instead of processing all raw sensor signals, the system identifies and extracts key features that indicate device states, reducing computational complexity while maintaining accurate state determination.
3Measurement precision
If sensor data is captured and analyzed in real-time, then measurement precision is improved, but loss of time increases due to data processing
Solution Approach 1:
The system performs preliminary feature extraction from sensor data during normal operation, preparing processed information in advance. By pre-processing sensor data and extracting relevant features continuously, the system maintains measurement precision while minimizing processing delays when state determination is needed.
Data Source
AI summary
The disclosure relates to a method for training a classifier to determine a handheld machine tool device state, comprising the following steps:—providing a handheld machine tool; —providing at least one sensor; —operating the handheld machine tool continuously; —terminating the continuous operation, in particular in the event of damage occurring; —capturing sensor data during the continuous operation; —extracting features on the basis of the sensor data; —ascertaining at least two handheld machine tool device states on the basis of the extracted features.