Piezoelectric MEMS Contact Detection via Dual-Transducer Segmentation
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Solution Overview
Problem
Existing piezoelectric MEMS vibration sensing devices face challenges in effectively detecting and classifying vibrations associated with object surfaces, particularly in distinguishing between different types of contacts and accurately determining their location and severity.
Innovation Solution
A system comprising a first piezoelectric MEMS transducer coupled to an object surface to detect vibrations, a second transducer to detect acoustic vibrations, and classification circuitry to process data from both transducers, categorizing combinations of signals received during time frames to differentiate contact types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single piezoelectric MEMS transducer is used to detect vibrations, then the device complexity is reduced, but the measurement precision and ability to distinguish contact types deteriorates
Solution Approach 1:
The system divides the detection function into two separate piezoelectric MEMS transducers: one configured to detect vibrations propagating through the object (contact vibrations) and another to detect acoustic vibrations in the air (acoustic signals). This segmentation allows each transducer to specialize in detecting specific vibration modes, improving the precision of contact detection and classification while maintaining manageable device complexity through modular functionality.
2Measurement precision
If multiple transducers are used to improve contact classification, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The system merges the detection capabilities of two piezoelectric MEMS transducers with different detection modes (contact vibration detection and acoustic vibration detection) into a unified classification system. By combining the output signals from both transducers and processing them together through classification circuitry, the system achieves improved contact classification accuracy while managing complexity through integrated signal processing that leverages the complementary information from both sensors.
3Difficulty of detecting and measuring
If vibration detection sensitivity is increased, then the detection capability improves, but the noise floor increases
Solution Approach 1:
The system introduces an intermediary classification mechanism that processes and differentiates between genuine contact vibration signals and noise signals from both transducers. The classification circuitry acts as an intermediary that analyzes the characteristics of detected vibrations, distinguishing between actual contact events and background noise, thereby maintaining high detection sensitivity while filtering out harmful noise effects through intelligent signal discrimination.
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 system achieves accurate detection and classification of vibrations and contacts, providing detailed information on contact location, severity, and type, thereby enhancing contact detection and classification capabilities.
Implementation Method 1
a first piezoelectric microelectromechanical systems (MEMS) transducer... configured to generate a first analog signal... when the first analog signal is transduced by the first piezoelectric MEMS transducer from vibrations propagating through the object
Data Source
AI summary
Systems, devices, methods, and implementations related to contact detection are described herein. In one aspect, a system is provided. The system includes a first piezoelectric microelectromechanical systems (MEMS) transducer coupled to configured to generate a first analog signal when the first analog signal is transduced from vibrations propagating through the object. The system includes a second piezoelectric MEMS transducer having configured to generate a second analog signal transduced from acoustic vibrations at a location of the object, and classification circuitry coupled to the output of first piezoelectric MEMS transducer and the output of the second piezoelectric MEMS transducer, where the classification circuitry is configured to process data from the first analog signal and data from the second analog signal, and to categorize combinations of the first analog signal and the second analog signal received during one or more time frames.