446 research outputs found
Fault diagnosis of rolling bearing based on relevance vector machine and kernel principal component analysis
In order to improve the speed and accuracy of rolling bearing fault diagnosis on small samples, a method based on relevance vector machine (RVM) and Kernel Principle Component Analysis (KPCA) is proposed. Firstly, the wavelet packet energy of the vibration signal is extracted with the wavelet packet transform, which is used as fault feature vectors. Secondly, the dimension of feature vectors is reduced in order to weaken the correlation between the features. The important principal components are selected using KPCA as the new feature vectors under the criterion that the cumulative variance is greater than 95 %. Finally, the faults of rolling bearing are diagnosed through combining KPCA with RVM. Simulation experimental indicates the advantages of the presented method. Moreover, the proposed approach is applied to diagnoses rolling bearing fault. The results show that wavelet packet energy can express rolling bearing fault features accurately, KPCA can reduce the dimension of feature vectors effectively and the proposed method has better performance in the speed of fault diagnosis than the method based on support vector machine (SVM), which supplies a strategy of fault diagnosis for rolling bearing. In this paper, the performance of the proposed method is also compared with other diagnostic methods
Sensor-AssistedWeighted Average Ensemble Model for Detecting Major Depressive Disorder
The present methods of diagnosing depression are entirely dependent on self-report
ratings or clinical interviews. Those traditional methods are subjective, where the individual may
or may not be answering genuinely to questions. In this paper, the data has been collected using
self-report ratings and also using electronic smartwatches. This study aims to develop a weighted
average ensemble machine learning model to predict major depressive disorder (MDD) with superior
accuracy. The data has been pre-processed and the essential features have been selected using a
correlation-based feature selection method. With the selected features, machine learning approaches
such as Logistic Regression, Random Forest, and the proposedWeighted Average Ensemble Model are
applied. Further, for assessing the performance of the proposed model, the Area under the Receiver
Optimization Characteristic Curves has been used. The results demonstrate that the proposed
Weighted Average Ensemble model performs with better accuracy than the Logistic Regression and
the Random Forest approaches
Classification of the mechanomyogram signal using a wavelet packet transform and singular value decomposition
Title on author’s file: Classification of mechanomyogram signal using wavelet packet transform and singular value decomposition for multifunction prosthesis control2008-2009 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe
A method based on multiscale base-scale entropy and random forests for roller bearings faults diagnosis
A method based on multiscale base-scale entropy (MBSE) and random forests (RF) for roller bearings faults diagnosis is presented in this study. Firstly, the roller bearings vibration signals were decomposed into base-scale entropy (BSE), sample entropy (SE) and permutation entropy (PE) values by using MBSE, multiscale sample entropy (MSE) and multiscale permutation entropy (MPE) under different scales. Then the computation time of the MBSE/MSE/MPE methods were compared. Secondly, the entropy values of BSE, SE, and PE under different scales were regarded as the input of RF and SVM optimized by particle swarm ion (PSO) and genetic algorithm (GA) algorithms for fulfilling the fault identification, and the classification accuracy was utilized to verify the effect of the MBSE/MSE/MPE methods by using RF/PSO/GA-SVM models. Finally, the experiment result shows that the computational efficiency and classification accuracy of MBSE method are superior to MSE and MPE with RF and SVM
Structural Damage Classification using Support Vector Machines
In this research, a methodology to classify crack and corrosion metallic damages using a time-frequency representation method and support vector machines is investigated. Piezoelectric ceramic actuators are utilized to generate guided wave signals on a set of aluminum beam coupons with different damage features, such as types, locations, and thicknesses. The short-time Fourier transform is applied to analyze the measured signals. For damage classification, the spectrograms obtained from finite element models are employed to train a two-class support vector machine learning classifier. The classifier is able to correctly classify different types of damages based upon the measured signals collected from the unknown damage sources. A multiple-class classifier is also generated to predict the damage extent of crack samples
A Voice Disease Detection Method Based on MFCCs and Shallow CNN
The incidence rate of voice diseases is increasing year by year. The use of
software for remote diagnosis is a technical development trend and has
important practical value. Among voice diseases, common diseases that cause
hoarseness include spasmodic dysphonia, vocal cord paralysis, vocal nodule, and
vocal cord polyp. This paper presents a voice disease detection method that can
be applied in a wide range of clinical. We cooperated with Xiangya Hospital of
Central South University to collect voice samples from sixty-one different
patients. The Mel Frequency Cepstrum Coefficient (MFCC) parameters are
extracted as input features to describe the voice in the form of data. An
innovative model combining MFCC parameters and single convolution layer CNN is
proposed for fast calculation and classification. The highest accuracy we
achieved was 92%, it is fully ahead of the original research results and
internationally advanced. And we use Advanced Voice Function Assessment
Databases (AVFAD) to evaluate the generalization ability of the method we
proposed, which achieved an accuracy rate of 98%. Experiments on clinical and
standard datasets show that for the pathological detection of voice diseases,
our method has greatly improved in accuracy and computational efficiency
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