Vibration analysis systems are used to assess the operational condition of machines and electromechanical components in various applications. This work presents a measurement and feature extraction system that analyzes dynamic strain patterns in signals measured by fiber Bragg grating (FBG). The features were used to identify different simulated operational conditions in an electromechanical relay. The selection of the best feature space in the first approach was performed by statistical criteria that determine the threshold values and frequency bands to calculate each signal's switching time and power spectral density (PSD). These parameters are used in the support vector machine (SVM) algorithm, which presents 98 % accuracy for distinguishing four distinct conditions. Another methodology for extracting features, called wavelet scattering transform (WST), was used to demonstrate that it is possible to achieve even better performance levels. The results allow extending the methodology to more complex systems.
This paper describes the automation of a forearm prosthesis using the signal collected by a Fiber Bragg Grating (FBG) sensor. The FBG sensor is applied to one subject's forearm to measure the deformation as a result of the index and middle fingers when moved individually. It is possible to control a one joint model prosthesis allied to a compliant hand mechanism through signal analyses. Each finger movement has its features, such as its amplitude, which opens the possibility of using those to control different parts of the prosthesis, joint rotation by the middle finger, and compliant hand movement by the index finger. This paper presents results regarding prosthesis assembling, Hypertext Transfer Protocol (HTTP) communication latency between prosthesis and computer and tests with pre-acquired and processed FBG signal data. The prosthesis wrist rotation movement is related to the middle finger signal, and its compliant mechanism actuation is due to index finger signal. The communication between prosthesis and the computer had a mean latency of 140 ms and a standard deviation of 18 ms. The results demonstrate the potential for using the sensor system and automated prosthesis in other applications involving real-time forearm sensing, multi-finger signal analysis, and prosthetic movement.
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