scholarly journals Using a Machine Learning Algorithm Integrated with Data De-Noising Techniques to Optimize the Multipoint Sensor Network

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1070 ◽  
Author(s):  
Yibeltal Chanie Manie ◽  
Jyun-Wei Li ◽  
Peng-Chun Peng ◽  
Run-Kai Shiu ◽  
Ya-Yu Chen ◽  
...  

In this paper, for an intensity wavelength division multiplexing (IWDM)-based multipoint fiber Bragg grating (FBG) sensor network, an effective strain sensing signal measurement method, called a long short-term memory (LSTM) machine learning algorithm, integrated with data de-noising techniques is proposed. These are considered extremely accurate for the prediction of very complex problems. Four ports of an optical coupler with distinct output power ratios of 70%, 60%, 40%, and 30% have been used in the proposed distributed IWDM-based FBG sensor network to connect a number of FBG sensors for strain sensing. In an IWDM-based FBG sensor network, distinct power ratios of coupler ports can contain distinct powers or intensities. However, unstable output power in the sensor system due to random noise, harsh environments, aging of the equipment, or other environmental factors can introduce fluctuations and noise to the spectra of the FBGs, which makes it hard to distinguish the sensing signals of FBGs from the noise signals. As a result, noise reduction and signal processing methods play a significant role in enhancing the capability of strain sensing. Thus, to reduce the noise, to improve the signal-to-noise ratio, and to accurately measure the sensing signal of FBGs, we proposed a long short-term memory (LSTM) deep learning algorithm integrated with discrete waveform transform (DWT) data smoother (de-noising) techniques. The DWT data de-noising methods are important techniques for analyzing and de-noising the sensor signals, and it further improves the strain sensing signal measurement accuracy of the LSTM model. Thus, after de-noising the sensor data, these data are fed into the LSTM model to measure the sensing signal of each FBG. The experimental results prove that the integration of LSTM with the DWT data de-noising technique achieved better sensing signal measurement accuracy, even in noisy data or environments. Therefore, the proposed IWDM-based FBG sensor network can accurately sense the signal of strain, even in bad or noisy environments; can increase the number of FBG sensors multiplexed in the sensor system; and can enhance the capacity of the sensor system.

2020 ◽  
Vol 38 (6) ◽  
pp. 1589-1603
Author(s):  
Yibeltal Chanie Manie ◽  
Peng-Chun Peng ◽  
Run-Kai Shiu ◽  
Yuan-Ta Hsu ◽  
Ya-Yu Chen ◽  
...  

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Yibeltal Chanie Manie ◽  
Run-Kai Shiu ◽  
Peng-Chun Peng ◽  
Bao-Yi Guo ◽  
Mekuanint Agegnehu Bitew ◽  
...  

A fiber Bragg grating (FBG) sensor is a favorable sensor in measuring strain, pressure, vibration, and temperature in different applications, such as in smart structures, wind turbines, aerospace, industry, military, medical centers, and civil engineering. FBG sensors have the following advantages: immune to electromagnetic interference, light weight, small size, flexible, stretchable, highly accurate, longer stability, and capable in measuring ultra-high-speed events. In this paper, we propose and demonstrate an intensity and wavelength division multiplexing (IWDM) FBG sensor system using a Raman amplifier and extreme learning machine (ELM). We use an IWDM technique to increase the number of FBG sensors. As the number of FBG sensors increases and the spectra of two or more FBGs are overlapped, a conventional peak detection (CPD) method is unappropriate to detect the central Bragg wavelength of each FBG sensor. To solve this problem, we use ELM techniques. An ELM is used to accurately detect the central Bragg wavelength of each FBG sensor even when the spectra of FBGs are partially or fully overlapped. Moreover, a Raman amplifier is added to a fiber span to generate a gain medium within the transmission fiber, which amplifies the signal and compensates for the signal losses. The transmission distance and the sensing signal quality increase when the Raman pump power increases. The experimental results revealed that a Raman amplifier compensates for the signal losses and provides a stable sensing output even beyond a 45 km transmission distance. We achieve a remote sensing of strain measurement using a 45 km single-mode fiber (SMF). Furthermore, the well-trained ELM wavelength detection methods accurately detect the central Bragg wavelengths of FBG sensors when the two FBG spectra are fully overlapped.


2011 ◽  
Vol 109 ◽  
pp. 79-83 ◽  
Author(s):  
Hyung Joon Bang ◽  
Soo Hyun Kim

This paper introduces a FBG (fiber Bragg grating) based AE (acoustic emission) sensing system for use in health monitoring of composite wind turbine blades. In this study, a multiplexing high speed FBG sensor system was developed with a spectrometer-type demodulator based on a linear photo detector. Pencil break test was performed using an FBG sensor and the results were compared with the results of piezo-based AE sensor. For the performance test of fracture sensing in composite materials, a down-scaled wind turbine blade was fabricated and drop impact tests were performed. Arrayed 4 FBG sensors were installed in the skin of wind turbine blade and impacts of 15 J energy were applied by a drop weight. The frequency characteristics of impact induced AE signals were examined with short-time Fourier transform focused on the leading waves. Finally, the onset of fractures in composite structure was successfully assessed using arrayed FBG sensors.


2019 ◽  
Vol 248 ◽  
pp. 217-230 ◽  
Author(s):  
Wei Wang ◽  
Tianzhen Hong ◽  
Xiaodong Xu ◽  
Jiayu Chen ◽  
Ziang Liu ◽  
...  

2021 ◽  
Vol 54 (3-4) ◽  
pp. 439-445
Author(s):  
Chih-Ta Yen ◽  
Sheng-Nan Chang ◽  
Cheng-Hong Liao

This study used photoplethysmography signals to classify hypertensive into no hypertension, prehypertension, stage I hypertension, and stage II hypertension. There are four deep learning models are compared in the study. The difficulties in the study are how to find the optimal parameters such as kernel, kernel size, and layers in less photoplethysmographyt (PPG) training data condition. PPG signals were used to train deep residual network convolutional neural network (ResNetCNN) and bidirectional long short-term memory (BILSTM) to determine the optimal operating parameters when each dataset consisted of 2100 data points. During the experiment, the proportion of training and testing datasets was 8:2. The model demonstrated an optimal classification accuracy of 76% when the testing dataset was used.


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