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Successive variational mode decomposition and blind source separation based on salp swarm optimization for bearing fault diagnosis

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年第11-12期125卷 5541-5556页

作者： Thelaidjia, Tawfik Chetih, Nabil Moussaoui, Abdelkrim Chenikher, Salah Larbi Tebessi Univ Fac Sci & Technol Dept Elect Engn LABGET Lab Tebessa Algeria Res Ctr Ind Technol CRTI POB 64 Algiers 16014 Algeria Univ 8 May 1945 Lab Elect Engn Guelma LGEG Guelma Algeria

In this paper, we are interested in developing a new approach that combines successive variational mode decomposition and blind source separation based on salp swarm optimization for bearing fault diagnosis. Firstly, vibration signals are preprocessed using successive variational mode decomposition to increase the signal-to-noise ratio. Then, the dynamic time-warping algorithm is adopted to select the most effective modes which will be considered mixture signals. In the second step, we apply the salp swarm algorithm (SSA) for estimating the de-mixing matrix to extract independent components from mixture signals. However, SSA suffers from the problem of population diversity. Consequently, it offers somewhat different independent sources at every execution of the program. To overcome this shortcoming, the SSA-based source estimation will be executed several times with different ranges of initial positions. Then, a fuzzy C-mean algorithm is introduced to select the reliable independent components. The suggested method is tested based on two experiments and compared to state-of-the-art methods. The obtained results demonstrate the effectiveness of the suggested method in recovering reliable independent components and extracting the fault frequency of bearings.

关键词： Bearings Blind source separation Successive variational mode decomposition Fault diagnosis dynamic time warping algorithm Fuzzy C-mean Salp swarm optimization Vibration control

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Predicting aircraft taxiing estimated time of arrival by cluster analysis

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IET INTELLIGENT TRANSPORT SYSTEMS 2022年第2期16卷 252-262页

作者： Tang, Xinmin Ji, Xiaoqi Liu, Jinan Nanjing Univ Aeronaut & Astronaut Civil Aviat Coll Nanjing 211100 Peoples R China

In order to predict the aircraft taxiing estimated time of arrival (ETA) to reduce scene conflicts and improve the operating efficiency of airports, the Kalman filter algorithm is employed to preprocess historical trajectory data for an airport scene. For the first time, in order to measure the distance between trajectory samples, the aircraft taxiing period and the number of aircraft in a scene are used as sample features for aircraft taxiing ETA prediction. The dynamic time warping algorithm is employed to extract the different features of the trajectory. The Euclidean distance between two sample features is taken as their similarity. The initial clustering centre is determined based on the divided difference maximum principle by using the K-means algorithm to cluster samples and selecting the cluster with the highest matching degree according to the time period of the aircraft under planning and the number of aircraft operating in the scene. The trajectory sequence for the sample at the cluster centre and the static path planned by the tower are combined to predict the aircraft taxiing ETA. The accuracy of this method at predicting the aircraft taxiing ETA was demonstrated in simulations by comparing and analysing actual trajectory data with predicted taxiing ETA.

关键词： aircraft taxiing estimated time aircraft taxiing ETA prediction historical trajectory data actual trajectory data predicted taxiing ETA dynamic time warping algorithm path planning Kalman filter algorithm cluster analysis sample features airports initial clustering centre time-of-arrival estimation Kalman filters K-means algorithm cluster samples Euclidean distance aircraft airport scene difference maximum principle

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Deduction of the transformation regulation on voltage curve for lithium-ion batteries and its application in parameters estimation

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ETRANSPORTATION 2022年 12卷

作者： Liu, Yang Zhang, Caiping Jiang, Jiuchun Zhang, Linjing Zhang, Weige Wang, Le Yi Beijing Jiaotong Univ Natl Act Distribut Network Technol Res Ctr Beijing 100044 Peoples R China Hubei Univ Technol Sch Elect & Elect Engn Wuhan 430068 Peoples R China Wayne State Univ Dept Elect & Comp Engn Detroit MI 48202 USA

States estimations play a vital role in the normal operation of the battery system. However, the existing state estimation methods are not fully applicable to the needs for simplicity, speed, and accuracy in cloud-based data analysis. To solve the above problems, we propose a parameter estimation method based on voltage curve transformation. In this paper, the charging voltage curve is studied for the state of-charge (SOC) and state-of-health (SOH) estimation. By comparing the voltage curve with an open circuit voltage (OCV) curve, it is proved that the parameters including initial SOC and capacity have a linear relationship with the curve shape. Furthermore, the dynamic time warping (DTW) algorithm is utilized to visualize the relationship. The data of cells with different state parameters are employed to verify the correction. For implementation, a curve fitting method based on the Least Absolute Deviations (LAD) is adopted to achieve the estimation of state parameters of the battery. Under the condition that SOC range is from 40% to 85%, the root-mean-square error (RMSE) of initial SOC is about 1.6% and the RMSE of SOH is about 2.1%.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Lithium-ion battery State estimation Voltage curve transformation Differential voltage analysis dynamic time warping algorithm Curve fitting

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Measurement of micro-harmonic vibration frequency from the modulated self-mixed interferometric signal using dynamic time warping method

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2022年 168卷 108712-108712页

作者： Bhardwaj, Vibhor Kumar Maini, Surita St Longowal Inst Engn & Technol Dept Elect & Instrumentat Engn Longowal 148106 Punjab India Bhagwant Inst Technol Dept Elect & Commun Engn Muzaffarnagar 251315 Uttar Pradesh India

The vibration frequency is one of the key factors that contains vital information about the subjects/machines and offers major analytical support. The contactless measurement of vibration frequency is the crucial requirement of many industrial, scientific, and biomedical applications like predictive maintenance, non-destructive testing, and reverse engineering, chest vibration, etc. The paper presents a self-mixed optical feedback interferometry (SM-OFI) sensor to measure the vibration frequency of the micro-harmonic vibrating surface. The method employs a dynamic time warping algorithm (DTW) to compute the Euclidian distance between the locally generated reference signal and the SM interferometric signal obtained from a vibrating target. The method is tested experimentally on a customized SM-OFI emitting a wavelength of 650 nm under weak feedback conditions. The proposed method was able to measure the unknown frequency with 98% accuracy in all sets of experiments. The method also exhibits an R-squared value of 0.99 with a relative error of less than unity. The comprehensive analysis of the experimental results concludes that the proposed method provides an accurate and precise vibration frequency measurement scheme for the low bandwidth range. This low bandwidth range measurement promises a non-contact measurement in industries and biomedical applications during the COVID-19 scenario.

关键词： Self-mixed effect Optical feedback dynamic time warping algorithm Vibration frequency measurement Euclidian distance

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SCIENTIFIC REPORTS 2024年第1期14卷 1-21页

作者： Zhang, Yidan Nie, Lei Beihua Univ Coll Sports Jilin 132000 Peoples R China

In order to eliminate the impact of camera viewpoint factors and human skeleton differences on the action similarity evaluation and to address the issue of human action similarity evaluation under different viewpoints, a method based on deep metric learning is proposed in this article. The method trains an automatic encoder-decoder deep neural network model by means of a homemade synthetic dataset, which maps the 2D human skeletal key point sequence samples extracted from motion videos into three potential low-dimensional dense spaces. Action feature vectors independent of camera viewpoint and human skeleton structure are extracted in the low-dimensional dense spaces, and motion similarity metrics are performed based on these features, thereby effectively eliminating the effects of camera viewpoint and human skeleton size differences on motion similarity evaluation. Specifically, when extracting the action information feature vectors using the automatic encoder-decoder network model, a sliding window method is used to divide the key point sequences of each limb part into sequence patches, and the action information feature vectors independent of the camera viewpoint and skeleton structure are extracted in a smaller time unit, so as to obtain a more refined action similarity evaluation result. In addition, the dynamic time warping (DWT) algorithm is exploited to align the sequence of action information feature vectors temporally, which solves the problem of temporal axis discrepancies in realizing similarity metrics based on action information feature vectors. More accurate and reliable human action similarity evaluation results were achieved by the loss function composed of three components, namely, cross-reconstruction loss, reconstruction loss and triplet loss. Finally, the performance of the algorithm is evaluated in a homemade dataset, and the experimental results show that the method could effectively eliminate the influence of the differences in camera viewpoi

关键词： Deep metric learning Human motion similarity evaluation Automatic encoder-decoder network Human skeleton structure information dynamic time warping algorithm

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Computer-assisted stochastic multi-well correlation: Sedimentary facies versus well distality

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MARINE AND PETROLEUM GEOLOGY 2022年 135卷 105371-105371页

作者： Baville, Paul Apel, Marcus Hoth, Silvan Knaust, Dirk Antoine, Christophe Carpentier, Cedric Caumon, Guillaume Univ Lorraine GeoRessources CNRS F-54000 Nancy France Equinor ASA N-4035 Stavanger Norway

Computer-assisted stratigraphic correlation can help to produce several scenarios reflecting interpretation uncertainties. In this work, we propose a method which translates sedimentary concepts into a correlation cost for each possible stratigaphic correlation. All these correlation costs are used to populate a cost matrix in order apply the dynamic time warping algorithm and to compute the n-best correlation sets having the n-least cumulative costs. The proposed cost function reflects prior knowledge about sediment transport direction, and it tested on two wells penetrating a Middle Jurassic reservoir in the North Sea. Well markers are described by two parameters: (1) the sedimentary facies corresponding to a depositional environment, and (2) the relative distality of the well computed from its position along the sediment transport direction. The main principle of correlation used in this article assumes that a well marker (described by a facies and a distality) cannot be correlated with another well marker described by a depositionally deeper facies at a more proximal position, or a depositionally shallower facies at a more distal position. This approach produces consistent stratigraphic well correlations, and highlights the sensitivity of the solution to the facies zonation and to the relative well distality. Therefore, the proposed rule offers a way to coherently consider chronostratigraphic correlation and the associated uncertainties at the parasequence scale, i.e., at a smaller scale than generally considered in deterministic correlation.

关键词： Stratigraphic correlation Uncertainty assessment Stochastic simulation Sediment routing dynamic time warping algorithm

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SmartMouse : Classifying Mouse Actions Using Smartwatch

SmartMouse : Classifying Mouse Actions Using Smartwatch

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作者： Dharmia Rajendran, Karthika California State University Northridge

学位级别：硕士

Smartwatches have become the most popular wearable device available today. In addition to ﬁtness applications, smartwatches provide a rich user interface that has enabled many applications including instant messaging, email and easy notifications. Since the smartwatch is worn on the wrist, it introduces a unique opportunity to understand users' arm, hand and possibly ﬁnger movements, using built-in accelerometer and gyroscope sensors. The mouse is an integral part of the laptop/desktop experience. This thesis explores whether motion sensors from the smartwatch can detect mouse actions, specifically movement in the 2D plan, scrolling, and clicks. The experimental results show that the mouse actions, including right clicking, left clicking, scrolling up or down, and cursor movements across the screen, can be accurately tracked. It is possible to classify in real-time mouse operation using hand and finger gesture recognition from only smart watch motion sensors. Thus, this work opens a new area for human computer interaction, that eliminates the physical mouse in place of the more ubiquitous smart watch.

关键词： Mouse Smartwatch Support Vector Machine algorithm dynamic time warping algorithm Smartmouse accelerometer sensor gyroscope sensor Human computer interaction

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Rotor position tolerant detection method for SRM drive system with misaligned position sensors

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IET ELECTRIC POWER APPLICATIONS 2019年第6期13卷 730-741页

作者： Han, Guoqiang Chen, Hao Guan, Guorui China Univ Min & Technol Sch Elect & Power Engn Jiangsu Prov Engn Lab High Efficient Energy Stora Xuzhou Jiangsu Peoples R China

Accurate rotor position is necessary for the control of switched reluctance motors (SRMs). Generally, three position sensors are ideally installed 120 electrical degrees apart for three-phase SRMs. However, the placement of position sensors may be inaccurate, particularly in small-size SRMs, and there is no method reported to deal with this problem up to now. Firstly, the position signals and the influence of the misaligned sensors on the detected rotor position are investigated. Then a novel method is developed to identify the misaligned position sensors with dynamic time warping (DTW) algorithm. The DTW distance versus the misaligned angle is calculated, and the relationship between them is described with linear regression analysis algorithm. Then the rotor position tolerant detection method is realised by compensating the misaligned angle in real time. Finally, comparative experiments are carried out on a three-phase 12/8 SRM drive system, and it can be found that the proposed strategy is capable of providing an accurate rotor position for the SRM drive system. The presented method is easy for online implementation and can be integrated into the control system conveniently.

关键词： position control reluctance motor drives rotors machine control position measurement regression analysis reluctance motors compensation sensors dynamic time warping algorithm misaligned angle rotor position tolerant detection method misaligned position sensors switched reluctance motors three-phase SRMs small-size SRMs position signals misaligned sensors SRM drive system DTW algorithm linear regression analysis algorithm

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Constructing gene regulatory networks from microarray data using non-Gaussian pair-copula Bayesian networks

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JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY 2020年第4期18卷 2050023-2050023页

作者： Chatrabgoun, O. Hosseinian-Far, A. Daneshkhah, A. Malayer Univ Dept Stat Malayer Iran Univ Northampton Dept Business Syst & Operat Northampton NN1 5PH England Coventry Univ Fac Engn Coventry CV1 5FB W Midlands England

Many biological and biomedical research areas such as drug design require analyzing the Gene Regulatory Networks (GRNs) to provide clear insight and understanding of the cellular processes in live cells. Under normality assumption for the genes, GRNs can be constructed by assessing the nonzero elements of the inverse covariance matrix. Nevertheless, such techniques are unable to deal with non-normality, multi-modality and heavy tailedness that are commonly seen in current massive genetic data. To relax this limitative constraint, one can apply copula function which is a multivariate cumulative distribution function with uniform marginal distribution. However, since the dependency structures of different pairs of genes in a multivariate problem are very different, the regular multivariate copula will not allow for the construction of an appropriate model. The solution to this problem is using Pair-Copula Constructions (PCCs) which are decompositions of a multivariate density into a cascade of bivariate copula, and therefore, assign different bivariate copula function for each local term. In fact, in this paper, we have constructed inverse covariance matrix based on the use of PCCs when the normality assumption can be moderately or severely violated for capturing a wide range of distributional features and complex dependency structure. To learn the non-Gaussian model for the considered GRN with non-Gaussian genomic data, we apply modified version of copula-based PC algorithm in which normality assumption of marginal densities is dropped. This paper also considers the dynamic time warping (DTW) algorithm to determine the existence of a time delay relation between two genes. Breast cancer is one of the most common diseases in the world where GRN analysis of its subtypes is considerably important;Since by revealing the differences in the GRNs of these subtypes, new therapies and drugs can be found. The findings of our research are used to construct GRNs with high perform

关键词： Gene regulatory networks Gaussian graphical models dynamic time warping algorithm modified PC algorithm pair-copula constructions

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Research on monitoring method for thinning spinning process of the ultra-thin-walled cylinder based on the drum shape

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SCIENCE PROGRESS 2020年第1期103卷 36850419877727页

作者： Wen, Xue Tan, Jian-ping Liu, Su-qi Li, Xin-he Cent South Univ Coll Mech & Elect Engn Changsha 410083 Hunan Peoples R China

In order to perceive the state of the process of reverse thinning spinning of the ultra-thin wall tube, a monitoring method based on drum shape is proposed. The method uses the non-uniform rational basis spline curve fitting method based on the moving least squares method to reconstruct the outer contour data of the spinning zone collected by the line laser displacement sensor and extracts the outer contour curve of the drum zone according to the spinning characteristics. Then, the dynamic time warping algorithm is used to analyze the similarity between the current and previous drum curves in order to judge whether the current spinning state is abrupt or not. At the same time, the current spinning state is judged by combining the curvature comb of the drum curve, the number of convex areas of the drum curve, the drum ratio, and the drum change trend. Finally, the drum shape detection and the spinning condition monitoring experiments are carried out. The experimental results show that this method can obtain the outer contour shape of the drum section at one time, and the accuracy is 0.05 mm with respect to the measured value of the three coordinates. The method can realize the visual monitoring of the abrupt change of the spinning process by judging the similarity of the drum curve at the adjacent time under different spinning states. In the state of instability, the curvature comb of the drum curve is discontinuous, the curve is G0 continuous, the drum ratio exceeds the critical drum ratio 2.0, and there is more than one convex region, which realizes the monitoring of spinning state more accurately. The experimental results verify the correctness of the proposed method and prove that the method can be used as a new method to judge the stability of the ultra-thin-walled cylinder spinning process.

关键词： Ultra-thin wall tube spinning drum curve fitting linear laser displacement sensor dynamic time warping algorithm curvature comb drum ratio

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