Handheld Vibration Inspection Using Force and Acceleration Sensing
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Solution Overview
Problem
Conventional methods for inspecting the condition of mechanical facilities rely heavily on subjective human judgment, leading to variations in accuracy due to worker variability, and existing devices that measure tremor are not suitable for evaluating the condition of target objects.
Innovation Solution
An inspection device and method that utilize force and acceleration sensors attached to a user's hand to acquire data during a vibrating action, combined with a learned model generated from user-specific information, to objectively judge the condition of a target object with high accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a worker judges the condition of a target object by vibrating it by hand, then the inspection method is simple and easy to operate, but the judgment result varies due to worker subjectivity
Solution Approach 1:
The patent replaces the mechanical human judgment system with an automated sensor-based measurement system. Force sensors and acceleration sensors objectively measure the physical quantities during vibration, eliminating worker subjectivity while maintaining operational simplicity through automated data processing.
Solution Approach 2:
The patent introduces sensors as intermediary devices between the worker's hand vibration and the judgment result. The force sensor and acceleration sensor act as mediators that objectively capture the vibration characteristics, transforming subjective human judgment into objective measurable data.
2Device complexity
If conventional inspection methods are used, then the device complexity is low, but the judgment accuracy is insufficient
Solution Approach 1:
The patent makes the inspection device universally applicable by using standard sensors (force sensor and acceleration sensor) that can be attached to any target object. The same basic device structure serves multiple inspection purposes while achieving high measurement precision through learned models adapted to different objects.
Solution Approach 2:
The patent changes the inspection parameters from subjective human judgment to objective physical measurements (force and acceleration). By measuring these physical parameters and processing them through learned models, the system achieves high measurement precision while keeping the device relatively simple.
3Measurement precision
If objective measurement devices are used, then the judgment accuracy improves, but the device complexity increases
Solution Approach 1:
The patent merges multiple measurement functions into a single integrated system. The force sensor and acceleration sensor work together to capture vibration characteristics, and their data is processed jointly by learned models to achieve accurate judgment, reducing overall system complexity compared to separate specialized devices.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution enables accurate and objective judgment of the target object's condition by minimizing the influence of worker subjectivity and utilizing a learned model to analyze force and acceleration data, resulting in precise assessments.
Implementation Method 1
a force sensor attached to a finger of a hand of a user
Implementation Method 2
an acceleration sensor attached to the hand
Data Source
AI summary
An inspection device includes a force acquisition unit that acquires a first input indicating force for holding a target object, an acceleration acquisition unit that acquires a second input indicating acceleration, and a judgment unit that judges condition of the target object from the first input and the second input by using a learned model for judging the condition of the target object generated from the first input and the second input by using user information. A learning device includes a data acquisition unit that acquires learning data including first data indicating force for holding a target object, second data indicating acceleration, and third data indicating the condition of the target object corresponding to a combination of the first data and the second data and a model generation unit that generates a learned model for judging the condition of the target object.