fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural *** paper introduces an innovative approach to fat...
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fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural *** paper introduces an innovative approach to fatigue damage monitoring in composite structures,leveraging a hybrid methodology that integrates the Whale Optimization algorithm(WOA)-Backpropagation(BP)neural network with an ultrasonic guided wave feature selection ***,a network of piezoelectric ceramic sensors is employed to transmit and capture ultrasonic-guided waves,thereby establishing a signal space that correlates with the structural ***,the relief-f algorithm is applied for signal feature extraction,culminating in the formation of a feature *** matrix is then utilized to train the WOA-BP neural network,which optimizes the fatigue damage identification model *** proposed model’s efficacy in quantifying fatigue damage is tested against fatigue test datasets,with its performance benchmarked against the traditional BP neural network *** findings demonstrate that the WOA-BP neural network model not only surpasses the BP model in predictive accuracy but also exhibits enhanced global search *** effect of different sensor-receiver path signals on the model damage recognition results is also *** results of the discussion found that the path directly through the damaged area is more accurate in modeling damage recognition compared to the path signals away from the damaged ***,the proposed monitoring method in the fatigue test dataset is adept at accurately tracking and recognizing the progression offatigue damage.
As the fault detection methods diagnose defects in the earlier stage, the subsequent costs will be reduced. feature extraction from the vibration signal is the foremost step for incipient fault detection of gearboxes....
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As the fault detection methods diagnose defects in the earlier stage, the subsequent costs will be reduced. feature extraction from the vibration signal is the foremost step for incipient fault detection of gearboxes. However, the current statistical features in the time and frequency domains cannot diagnose the early or low intensity faults. In this research, for the first time, besides these features, some other features are extracted by combining variational mode decomposition and time synchronous average (VMD-TSA) to overcome the problem. The combinations have occurred in two ways. first, the Intrinsic Mode functions (IMfs) of the TSA signal are calculated by VMD, and the Amplitude Energy (AE) and Permutation Entropy (PE) of the first four IMfs are computed. Secondly, the IMfs of vibration signals are calculated, and the TSA features are extracted from the most informative IMf. Moreover, 16 features in the time domain, 13 features in the frequency domain, and 9 features by TSA are extracted from the vibration signals. These features are extracted from healthy and four faulty conditions: crack, spalling, chipping, and wear in three different severities. After feature extraction, the relief-f algorithm selects the informative features, and selected features are utilized for fault detection by a feed-forward Neural network (fNN) classifier. In this study, the ability of the VMD-TSA method is compared with others like Empirical Mode Decomposition-TSA (EMD-TSA) and Ensemble Empirical Mode Decomposition-TSA (EEMD-TSA), which shows that the proposed method is more powerful than others in early fault detection. Besides, the classification accuracy of these methods is compared with some other feature selection methods like Laplacian Score (LS), Principal Component Analysis (PCA), and Minimum Redundancy-Maximum Relevance (MRMR). Also, the performance of the fNN classifier is compared with the Support Vector Machine (SVM). As shown in this study, the VMD-TSA features impro
Southern corn leaf blight (SCLB) seriously threatens corn production. The timely and accurate monitoring of SCLB conditions (e.g., detection during the asymptomatic stage and severity classification during the symptom...
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Southern corn leaf blight (SCLB) seriously threatens corn production. The timely and accurate monitoring of SCLB conditions (e.g., detection during the asymptomatic stage and severity classification during the symptomatic stage) is valuable for precision agriculture, because the application of pesticides depends on disease conditions. Compared with time-consuming and laborious field surveys, spectroscopy is a promising tool for plant disease monitoring. The unique advantages of combining multiple spectral enhancement features for monitoring rice and wheat diseases have been recognized. However, physiological and biochemical differences between maize leaves and rice and wheat leaves, along with the specific spectral response of SCLB, are likely to affect the performance of combining multiple spectral enhancement features. In addition, similar previous studies have not combined spectral slope features, i.e., first-order spectral derivatives (fSDs), with spectral bands (SBs) and spectral indices (SIs) and wavelet features (Wfs) to improve plant disease detection. Thus, the performance of a method that combines fSDs, Wfs, SBs, and SIs for SCLB asymptomatic detection, symptomatic detection, and symptomatic severity classification should be evaluated further. Here, the utility of combining SBs, SIs, Wfs, and fSDs was quantified and evaluated in the asymptomatic detection, symptomatic detection, and symptomatic severity classification of SCLB. Various forms of spectral enhancement features that were sensitive to SCLB infection from the asymptomatic stage to the severe stage were first identified and combined using the relief-f and sequential floating forward selection algorithms on the basis of two independent inoculation experiments. finally, SCLB asymptomatic detection, symptomatic detection, and symptomatic severity classification models were developed and evaluated using the support vector machine algorithm. Results showed that combining fSDs with SBs, SIs, and Wfs ach
Rolling bearing is an important part of mechanical equipment. Timely detection of rolling bearing fault is one of the important factors to ensure the safe operation of equipment. In order to diagnose rolling bearing f...
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Rolling bearing is an important part of mechanical equipment. Timely detection of rolling bearing fault is one of the important factors to ensure the safe operation of equipment. In order to diagnose rolling bearing fault accurately, a novel rolling bearing fault diagnosis method based on adaptive feature selection and clustering is proposed. firstly, the vibration signal obtained from rolling bearing is decomposed by ensemble empirical mode decomposition(EEMD) to extract as much important information as possible. feature extraction is performed for each intrinsic mode function(IMf) component and the original signal, and finally 240 features are obtained. And the Chi-square Test algorithm, Variance-relief-f algorithm, and hierarchical clustering algorithm are used to filter all the features in layers to obtain the optimal features. Then the optimal features are input into fuzzy c-means(fCM) clustering to complete fault diagnosis. After the fault diagnosis analysis offour groups of vibration signal data, it is found that whether the characteristic number parameters are set based on engineering experience or adaptive feature selection, good fault diagnosis results are obtained. furthermore, through comparative experiments, the fault diagnosis effect of the method based on adaptive parameter setting is better. The results indicate that the proposed adaptive parameter fault diagnosis method is feasible and effective for rolling bearing fault diagnosis.
Activity recognition is required in various applications such as medical monitoring and rehabilitation. Previously developed activity recognition systems utilizing triaxial accelerometers have provided mixed results, ...
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Activity recognition is required in various applications such as medical monitoring and rehabilitation. Previously developed activity recognition systems utilizing triaxial accelerometers have provided mixed results, with subject-to-subject variability. This paper presents an accurate activity recognition system utilizing a body worn wireless accelerometer, to be used in the real-life application of patient monitoring. The algorithm utilizes data from a single, waist-mounted triaxial accelerometer to classify gait events into six daily living activities and transitional events. The accelerometer can be worn at any location around the circumference of the waist, thereby reducing user training. feature selection is performed using relief-f and sequential forward floating search (SffS) from a range of previously published features, as well as new features introduced in this paper. Relevant and robust features that are insensitive to the positioning of accelerometer around the waist are selected. SffS selected almost half the number offeatures in comparison to relief-f and provided higher accuracy than relief-f. Activity classification is performed using Naive Bayes and k-nearest neighbor (k-NN) and the results are compared. Activity recognition results on seven subjects with leave-one-person-out error estimates show an overall accuracy of about 98% for both the classifiers. Accuracy for each of the individual activity is also more than 95%.
A fast image stitching algorithm based on improved speeded up robust feature (SURf) is proposed to overcome the real-time performance and robustness of the original SURf based stitching algorithms. The machine learnin...
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ISBN:
(纸本)9781479974344
A fast image stitching algorithm based on improved speeded up robust feature (SURf) is proposed to overcome the real-time performance and robustness of the original SURf based stitching algorithms. The machine learning method is adopted to build a binary classifier, which identify the key feature points extracted by SURf and remove the non-key feature points. In addition, the relief-f algorithm is used for dimension reduction and simplification of the improved SURf descriptor to achieve image registration. The threshold-based weighted fusion algorithm is used to achieve seamless image stitching. finally, several experiments are conducted to verify the real-time performance and robustness of the improved algorithm.
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