Early fault detection model for rolling bearing based on an iterative tunable Q-factor wavelet transform

To reduce the adverse effect of incorrect parameters for the traditional iterative tunable Q-factor wavelet transform, this paper proposes an iterative tunable Q-factor wavelet transform method for fault feature extraction. Firstly, before decomposing the bearing vibration signal by an iterative tunable Q-factor wavelet transform, the initial values of 3 basic factors should be set: the quality factor Q, redundancy r and the number of decomposition level J. Secondly, the kurtosis of a high resonance component, which is the result of an iterative tunable Q-factor wavelet transform, is calculated through multistep iteration until it meets the iteration stop condition. Finally, the envelope spectrum of the final low resonance component is calculated, and the type of bearing fault can be recognized according to the frequency of extreme points. The results show that this method can effectively suppress noise and in-band interference and avoid fault identification inaccuracies caused by improper parameters and can also identify the fault feature frequency more clearly

IТ-диагностика болезни Паркинсона на основе анализа голосовых маркеров и машинного обучения

The results of studying the parameters of the spectra of speech signals by machine learning with the use of neural networks are presented. This study was carried out in order to confirm experimentally the possibility of performing an assessment of these parameters for the detection of Parkinson’s disease in the early stages (IT diagnostics). During the study, the public database was used, which systematized the spectra of vowel sounds uttered by patients with Parkinson’s disease. The applied method is binary data classification. In the course of the study, the speech data spectrum was first preprocessed, which consisted of filtering it in order to remove its noise components and eliminate bursts and gaps in it. Then the parameters of the processed spectrum of speech data were determined: average value, maximum and minimum, peak, wavelet coefficients, MFCC and TQWT. After that, the object was selected using the PCA algorithm. The model was trained using the Knn and Random Forest algorithms, as well as the Bayesian neural network. The Bayesian optimization algorithm and the GridSearch method were used to find the best model hyperparameters. It has been established that when using Knn, Random Forest and Bayesian neural network, it is possible to increase the accuracy of recognition of Parkinson’s disease by 94.7; 88.16 and 74.74 %, respectively. A similar study by other scientists showed that the recognition accuracy of data sets was only 86 %.Представлены результаты исследования параметров спектров речевых сигналов с помощью машинного обучения с применением нейронных сетей, проведенного в целях экспериментального подтверждения возможности выполнения оценки этих параметров для выявления болезни Паркинсона на ранних стадиях (IТ-диагностика). В ходе исследования использовали общедоступную базу данных, в которой систематизированы спектры гласных звуков, произнесенных пациентами с болезнью Паркинсона. Примененный метод – бинарная классификация данных. Сначала выполняли предварительную обработку спектра речевых данных, состоявшую в его фильтрации, для удаления из него шумов и устранения присутствующих в нем всплесков и пробелов. Затем определяли параметры обработанного спектра речевых данных: среднее значение, максимум, минимум, пик, вейвлет-коэффициенты, MFCC и TQWT. После этого выбирали объект с помощью алгоритма PCA. Для обучения модели использовали алгоритмы Knn и Random Forest и нейронной сети Байеса. Для нахождения наилучших гиперпараметров модели применяли алгоритм оптимизации Байеса и метод GridSearch. Установлено, что при использовании Knn, Random Forest и нейронной сети Байеса можно обеспечить увеличение точности распознавания болезни Паркинсона на 94,7; 88,16 и 74,74 % соответственно. Аналогичное исследование, проведенное другими учеными, показало, что точность распознавания наборов данных составила всего 86 %

Feature extraction of rolling element bearing’ compound faults based on cyclic wiener filter with constructed reference signals

Feature extraction of rolling element bearing’s compound faults is a challenging task due to the complexity and the mutual coupling phenomenon among the kinds of faults. A new method based on cyclic wiener filter with constructed reference signals is proposed in the paper. The reference signals of the rolling element bearing’ inner race fault, outer race fault and rolling element fault are created respectively based on the rolling element bearing’ theoretical fault frequencies. Here, the created signals are used as the expected responses. Then the observed compound faults signal and the constructed reference signal are input into the cyclic wiener filter together. At last, the envelope demodulation method is applied on the filtered signals respectively and satisfactory fault feature extraction results are obtained. The effectiveness of the proposed method is verified through simulation. Furthermore, the advantages of the proposed method over other signal handling method such as spectral kurtosis (SK) are verified through experiment

A Tunable-Q wavelet transform and quadruple symmetric pattern based EEG signal classification method

Electroencephalography (EEG) signals have been widely used to diagnose brain diseases for instance epilepsy, Parkinson's Disease (PD), Multiple Skleroz (MS), and many machine learning methods have been proposed to develop automated disease diagnosis methods using EEG signals. In this method, a multilevel machine learning method is presented to diagnose epilepsy disease. The proposed multilevel EEG classification method consists of pre-processing, feature extraction, feature concatenation, feature selection and classification phases. In order to create levels, Tunable-Q wavelet transform (TQWT) is chosen and 25 frequency coefficients sub-bands are calculated by using TQWT in the pre-processing. In the feature extraction phase, quadruple symmetric pattern (QSP) is chosen as feature extractor and extracts 256 features from the raw EEG signal and the extracted 25 sub-bands. In the feature selection phase, neighborhood component analysis (NCA) is used. The 128, 256, 512 and 1024 most significant features are selected in this phase. In the classification phase, k nearest neighbors (kNN) classifier is utilized as classifier. The proposed method is tested on seven cases using Bonn EEG dataset. The proposed method achieved 98.4% success rate for 5 classes case. Therefore, our proposed method can be used in bigger datasets for more validation

Multi-time-scale features for accurate respiratory sound classification

The COVID-19 pandemic has amplified the urgency of the developments in computer-assisted medicine and, in particular, the need for automated tools supporting the clinical diagnosis and assessment of respiratory symptoms. This need was already clear to the scientific community, which launched an international challenge in 2017 at the International Conference on Biomedical Health Informatics (ICBHI) for the implementation of accurate algorithms for the classification of respiratory sound. In this work, we present a framework for respiratory sound classification based on two different kinds of features: (i) short-term features which summarize sound properties on a time scale of tenths of a second and (ii) long-term features which assess sounds properties on a time scale of seconds. Using the publicly available dataset provided by ICBHI, we cross-validated the classification performance of a neural network model over 6895 respiratory cycles and 126 subjects. The proposed model reached an accuracy of 85% ± 3% and an precision of 80% ± 8%, which compare well with the body of literature. The robustness of the predictions was assessed by comparison with state-of-the-art machine learning tools, such as the support vector machine, Random Forest and deep neural networks. The model presented here is therefore suitable for large-scale applications and for adoption in clinical practice. Finally, an interesting observation is that both short-term and long-term features are necessary for accurate classification, which could be the subject of future studies related to its clinical interpretation

Multi-Time-Scale Features for Accurate Respiratory Sound Classification

The COVID-19 pandemic has amplified the urgency of the developments in computer-assisted medicine and, in particular, the need for automated tools supporting the clinical diagnosis and assessment of respiratory symptoms. This need was already clear to the scientific community, which launched an international challenge in 2017 at the International Conference on Biomedical Health Informatics (ICBHI) for the implementation of accurate algorithms for the classification of respiratory sound. In this work, we present a framework for respiratory sound classification based on two different kinds of features: (i) short-term features which summarize sound properties on a time scale of tenths of a second and (ii) long-term features which assess sounds properties on a time scale of seconds. Using the publicly available dataset provided by ICBHI, we cross-validated the classification performance of a neural network model over 6895 respiratory cycles and 126 subjects. The proposed model reached an accuracy of 85%±3% and an precision of 80%±8%, which compare well with the body of literature. The robustness of the predictions was assessed by comparison with state-of-the-art machine learning tools, such as the support vector machine, Random Forest and deep neural networks. The model presented here is therefore suitable for large-scale applications and for adoption in clinical practice. Finally, an interesting observation is that both short-term and long-term features are necessary for accurate classification, which could be the subject of future studies related to its clinical interpretation

Bearing incipient fault diagnosis based upon maximal spectral kurtosis TQWT and group sparsity total variation denoising approach

Localized faults in rolling bearing tend to result in periodic shocks and thus arouse periodic responses in the vibration signal. In this paper, a novel fault diagnosis method based on maximal spectral kurtosis tunable Q-factor wavelet transformation (TQWT) and group sparsity total variation denoising (GS-TVD) is proposed to address the issue of bearing incipient failure. Firstly, the range of Q-factor was pre-selected according to the spectral distribution of impulse component, and bearing vibration signal was transformed by the TQWT method. Then, the spectral kurtosis of each scale transform coefficients was calculated, and the optimal Q-factor and decomposition scale can be selected according to the kurtosis maximum principle. In order to remove the interference components and high-frequency noise from the reconstructed vibration signal generated by inverse TQWT, the GS-TVD approach is employed, thus the cyclic periodicity characteristic and transient impulses can be detected obviously. The two cases experimental results indicate that the proposed technique is more effective and applicable for bearing incipient fault diagnosis compared with traditional method