查看更多>>摘要：? 2022 Elsevier LtdIn this paper, based on the research of fiber optic sensing technology, a multiparameter measuring system for hydraulic parameter monitoring is developed and evaluated. The sensing theory, design, calibration, and installation of the proposed metal-coated fiber Bragg grating temperature sensor, fiber Bragg grating flow sensor, and Fabry-Perot pressure sensor are detailed in the paper. A special hydraulic platform is designed for system test and the parameter monitoring condition of the hydraulic platform is displayed by the graphical user interface designed by using LabVIEW. The effectiveness of the designed system is verified by analyzing and comparing the obtained data of electrical sensors and the proposed sensors installed in the hydraulic platform. Experimental results indicate that the designed system using fiber optic sensing technology can accurately detect the parameters of the hydraulic equipment, and can be potentially used for real-time detection for condition monitoring of hydraulic equipment by analyzing the characteristic parameters.
查看更多>>摘要：? 2022 Elsevier LtdBelt vibration is related to the tension force along the entire length of the conveyor route. The ability to predict belt vibrations can provide information about the correct operation of the conveyor. Vibration analysis is usually based on theoretical models or stationary tests. In this article the vibration frequency of a working conveyor was measured, in the absence of the material and with the belt loaded. The tests were carried out in motion using a specially made device placed onto the conveyor belt. The distribution of the frequency of transverse vibration of the belt along the entire length of the conveyor route was identified. Using the phenomenon of belt vibration, the increase in belt tension in the top belt strand was determined. The comparison of the measured increases in the tensile force with the values obtained by computer simulation showed the high accuracy of the method.
查看更多>>摘要：? 2022 Elsevier LtdAdvanced driving assistance systems include some features such as changing lanes, avoiding obstacles and moving in cruise mode. Creating the perception of the environment is one of the most critical steps in developing an advanced driving assistance system. One of the main tasks in the perceptual layer is the timely detection of obstacles. In this study, a mixed method underpinned by the ultrasonic sensors and stereo vision was proposed to reduce the computational complexity in achieving acceptable frequency in the perception of the environment. The modified cross-check is employed for quality improvement while maintaining real-time. Moreover, we proposed an implementation form for the designed algorithm on GPU, which allows all data required for the cross-check process to be reached without changing the reference image. The proposed method accelerates the obstacle-detection 316 times faster than the typical stereo vision.
查看更多>>摘要：? 2022 Elsevier LtdTool wear prediction was significant for improving processing efficiency, ensuring product quality and reducing tool costs in manufacturing. In this paper, a novel deep learning method based on stacked sparse autoencoders (SSAE) and multi-sensor feature fusion was proposed for milling tool wear prediction. The signal features were extracted in time, frequency and time–frequency domains and the optimum multi-sensor features were determined by correlation analysis, which were input into the SSAE for deep feature learning. Backpropagation neural network (BPNN) was utilized to establish the prediction model of tool wear. Different milling wear experiment datasets were applied to verify the predictive performance of the trained models. The prediction results showed that the proposed model had the minimum root mean square error (RMSE) and maximum coefficient of determination (R2), which outperformed the comparative predictive models. The combination of multi-sensor feature fusion and deep learning method was demonstrated for improving the predictive performance.
查看更多>>摘要：? 2022 Elsevier LtdDifferent from most of deep learning-based rotating machinery diagnosis methods, graph convolutional network-based method can effectively mine relationship between nodes in the graph by feature aggregation and transformation. But the performance is limited to graph quality. Currently, edge connections of the graph are often established by calculating the feature similarity of single sensor data. To further improve graph quality, an improved multi-channel graph convolutional network (iMCGCN) for rotating machinery diagnosis is proposed in this paper. Multi-sensor data are used to construct graphs, where corresponding undirected k-nearest neighbor graphs (UK-NNGs) are constructed for each sensor data. A parallel graph data processing framework is designed to extract graph features from the constructed UK-NNGs. Then, an iMCGCN is constructed to learn graph features and achieve multi-channel feature fusion. Case studies are implemented to verify effectiveness of the proposed iMCGCN in learning health features for fault diagnosis.
查看更多>>摘要：? 2022 Elsevier LtdLinear and nonlinear analyses served to detect the degree of tooth damage in a cylindrical gear. The gear in question is widely used in industrial applications and is often subject to gear tooth damage. Multiple-sourced vibrations affecting the test stand may distort or conceal the vibration components responsible for tooth failure. Given its construction, the test stand offers low stiffness, and additional control and measurement equipment further contribute to overall vibrations. Therefore, the vibration signal is likely to contain various disturbances, which further hinder correct diagnosis. Basic vibroacoustic signal analysis methods, which were employed at a preliminary stage, failed to produce unambiguous and satisfactory results. Hence, nonlinear recurrence analysis of gear operation dynamics was applied. It is assumed that the occurrence of damage to the gear will impede the regularity of drive transmission. The adopted research method addresses the limitations of a two-state diagnosis of gear condition (no damage/damage) by allowing the extent of gear tooth damage to be specified.
查看更多>>摘要：? 2022 Elsevier LtdThe study is focused on executing machining operations by using 18 tools of milling, drilling, and turning thus 6 tools were used for each machining process. The VB of tool was measured for categorizing the tools ranging from level-1 to level-5 based on the severity of tool wear. The first model was designed based on LightGBM whereas the second model was developed by designing six algorithms i.e. LR, RF, CART, NB, SVM, and KNN. All algorithms were combined to develop an ensemble stacking model. Manual hyperparameter tuning was done for the LightGBM model whereas automatic hyperparameter tuning was adopted for the Stacking model by using GridSearchCV. The force signals’ features extraction was done by SSA whereas dimensionality reduction was accomplished by PCA. One-hot encoding technique has transformed target variables into binary form. The application of techniques of dropout and early stopping to both models has overcome overfitting.
查看更多>>摘要：? 2022 Elsevier LtdWith the increasing application of digital image correlation (DIC) technology in the field of high-temperature measurement, thermal radiation and disturbance in high-temperature environment restrict the development of high-temperature DIC technology. In this study, we propose a method based on image processing to improve the measuring accuracy of DIC technique of high temperature for solving the above mentioned problems. Firstly, the image dehazing enhancement processing and the gray scale range stretching processing are used to eliminate the influence of the thermal radiation background light to enhance the image contrast. Then, the inverse filtering image recovery processing is used to eliminate the effect of the thermal perturbation. Lastly, a series of high-temperature image acquisition and thermal disturbance elimination experiments at 1200 °C are conducted. The image collected by the camera is enhanced and restored according to the above mentioned image processing method. Experimental results show that this method can acquire high-contrast clear images under high-temperature conditions, effectively eliminate the interference of thermal disturbance noise in high-temperature measurement and improve the measurement accuracy.
查看更多>>摘要：? 2022In this article, we present a triangular-wavy-substrate pressure sensor (TWSPS) consisting a microstructural substrate, a polyvinylidene fluoride (PVDF) film, an elastic layer, and a flexible measuring circuit for real-time monitoring of external pressure variation. A force-electric coupling model is computed to clarify the physical mechanism of strain and response charge amplification with four types of substrate structures in the proposed sensor system. The simulation results show that the response charge initially increases, and then decreases when the contact angle (α) increases. The maximum value of the response charge is about 1.21e?10C at α of 54°. Moreover, the experimental results show that the TWSPS can measure low pressures within 500 Pa with linear response and high sensitivity (19 mV/kPa). More importantly, the design strategy of substrate stain amplification introduced in this work can be fitted with any piezoelectric or piezoresistive material to form a high-performance pressure sensor.
查看更多>>摘要：? 2022 Elsevier LtdSteel-to-timber bolted connections play an essential role in enabling glulam column and beam to work together. However, these connections are prone to bolt looseness due to shrinkage and creep effects of timber material during a long service life, which may cause consequent connection failures and structural safety hazards. Recently, detection and evaluation of bolt looseness have gained considerable attention, whereas the existing vision-based method is challenging to inspect early bolt looseness, and the percussion-enabled approach is susceptible to being affected by harsh environmental noises. Thus, in this study, the authors proposed a pre-tightening torque measurement method for steel-to-timber bolted connection based on variations of electromechanical impedance (EMI) of designed Lead Zirconate Titanate (PZT) sensors. A theoretical formula is derived first based on the piezoelectric equation and boundary conditions to illustrate the feasibility of using EMI variations to reflect looseness levels. Moreover, a sensor that can be easily integrated into the steel-to-timber connection and work as a washer is designed and fabricated. Consequently, the environmental performance against temperature and humidity of designed sensors is investigated by preliminary tests. Based on preliminary tests results, a frequency range that showed lower environmental sensitivity is selected as the excitation signal in the following bolt looseness measuring experimental studies. Experimental results well validate the effectiveness of EMI method in the monitoring of pre-tightening torque of steel-to-timber bolted connections, showing a great potential to inspect preload looseness of glulam structures in field applications.
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