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Advances in Classifier Evaluation: Novel Insights for an Electric Data-Driven Motor Diagnosis

IEEE ACCESS 2016年 4卷 7028页

作者： Martin-Diaz, Ignacio Morinigo-Sotelo, Daniel Duque-Perez, Oscar Romero-Troncoso, Rene De J. Univ Valladolid Escuela Ingn Ind E-47002 Valladolid Spain Univ Guanajuato CA Telemat DICIS Leon 37320 Spain

Fault diagnosis of inductions motors has received much attention recently. Most of the works use data obtained either from the time domain or by applying advanced techniques in the frequency domain. Some researchers have employed a considerable effort in designing sophisticated algorithms to achieve the best performance of the diagnosis system. However, some contributions in the field have not taken advantage of the benefits that a good evaluation stage can bring to the developing of classifiers for fault diagnosis. In this paper, novel insights for the classifier evaluation are presented to promote better assessment practices in the field of electric machine diagnosis based on supervised classification. A case of study consisting of a motor with a broken rotor bar is described to analyze the performance of two classifiers by using scores focused on the fault detection. Also, different error estimation methods are considered to obtain unbiased predictive performances. Two statistical tests are also discussed to confirm the significance of the results under a single data set.

关键词： Broken rotor bar classification algorithm condition monitoring electric machines fault diagnosis performance evaluation

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Method for loose bolt positioning and prediction of bolt axial force in bolt group

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MEASUREMENT 2024年 227卷

作者： Zhou, Yang Yi, Hongzhen Yue, Xiaoyun Li, Ang Hao, Bin Yan, Xianfeng Zhao, Yitao Zhengzhou Univ Sch Mech & Power Engn Zhengzhou 450000 Peoples R China Mech & Elect Engn Co Ltd Shanxi Design & Res Inst Taiyuan 030000 Peoples R China

Bolt-looseness reduces the stiffness of the structure and poses safety risks. The accurate monitoring of bolt axial force is crucial for ensuring the service performance of components. However, existing methods are less suitable for large-scale bolt axial force detection owing to unclear mechanisms of force interaction among bolts and limited vision-based detection methods for identifying slight angle loosening and cycle rotation. This paper proposes a bolt-set simulation model to investigate the influence of loose bolts on the axial force of tight bolts, and model the correlation between loose and tight bolts. Based on the established model, a method for accurately positioning loose bolts and predicting the axial force was developed and validated through experiments. The method's accuracy in locating the loosened bolt exceeds 99%, with the error between the predicted axial force and experimental results being less than 4%. The proposed method allows a more precise evaluation of structural health.

关键词： Bolt loosening Axial force prediction Deep Learning Influence mechanism Correlation model classification algorithm

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Comparison of machine learning methods for the construction of a standalone gait diagnosis device

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IET SIGNAL PROCESSING 2020年第4期14卷 243-250页

作者： Han, Yi Chiew Wong, Kiing Ing Murray, Iain Curtin Univ Malaysia Dept Elect & Comp Engn CDT250 Miri Malaysia Curtin Univ Australia Sch Engn Comp & Math Sci Perth WA Australia

In this research, the authors investigate the feasibility of selecting three-dimensional thigh and shank angles as the features of machine learning methods. Four common machine learning techniques, i.e. random forest, k-nearest neighbour, support vector machine and perceptron, were compared in terms of accuracy and memory usage so that a real-time standalone gait diagnosis device can be constructed using low-end inertial measurement units (IMUs). With proper re-sampling and normalisation, they discovered that the support vector machine and perceptron resulted in the top two highest accuracies (96-99%) among the four machine learning methods. The memory requirement of the perceptron is the lowest among the machine learning methods. Therefore, perceptron was selected as the classification algorithm for the standalone gait diagnosis device. The trained perceptron was transferred to the thigh and shank's IMUs to process the data locally in real-time. The constructed standalone gait diagnosis device lit up green or red light emitting diodes when normal or abnormal gaits were detected, respectively. This standalone device was further tested in real-life and achieved a mean classification accuracy of 96.50%.

关键词： pattern classification perceptrons learning (artificial intelligence) support vector machines patient diagnosis gait analysis medical diagnostic computing biomedical equipment standalone device machine learning support vector machine perceptron real-time standalone gait diagnosis device low-end inertial measurement units classification algorithm three-dimensional thigh angles three-dimensional shank angles

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Multi-kernel regularized classifiers

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JOURNAL OF COMPLEXITY 2007年第1期23卷 108-134页

作者： Wu, Qiang Ying, Yiming Zhou, Ding-Xuan City Univ Hong Kong Dept Math Kowloon Hong Kong Peoples R China

A family of classification algorithms generated from Tikhonov regularization schemes are considered. They involve mufti-kernel spaces and general convex loss functions. Our main purpose is to provide satisfactory estimates for the excess misclassification error of these mufti-kernel regularized classifiers when the loss functions achieve the zero value. The error analysis consists of two parts: regularization error and sample error. Allowing mufti-kernels in the algorithm improves the regularization error and approximation error, which is one advantage of the mufti-kernel setting. For a general loss function, we show how to bound the regularization error by the approximation in some weighted L-q spaces. For the sample error, we use a projection operator. The projection in connection with the decay of the regularization error enables us to improve convergence rates in the literature even for the one-kernel schemes and special loss functions: least-square loss and hinge loss for support vector machine soft margin classifiers. Existence of the optimization problem for the regularization scheme associated with multi-kernels is verified when the kernel functions are continuous with respect to the index set. Concrete examples, including Gaussian kernels with flexible variances and probability distributions with some noise conditions, are used to illustrate the general theory. (c) 2006 Elsevier Inc. All rights reserved.

关键词： classification algorithm multi-kernel regularization scheme convex loss function misclassification error regularization error and sample error

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Adaptive algorithm for classifying LiDAR data into water and land points by multifeature statistics

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JOURNAL OF APPLIED REMOTE SENSING 2016年第4期10卷 045020-1-045020-11页

作者： Wei, Li-Xin Yang, Biao Jiang, Jian-Ping Cao, Guan-Zhong Hohai Univ Inst Remote Sensing & Spatial Informat Engn Sch Earth Sci & Engn 8 Focheng West Rd Nanjing 211100 Jiangsu Peoples R China Hydrol & Water Resources Survey Lower Changjiang River Bur Changjiang Water Resources Commiss 66 Dama Rd Nanjing 210011 Jiangsu Peoples R China

Classifying light detection and ranging (LiDAR) data into water and land points is an issue for the application of low-attitude airborne LiDAR (e.g., digital terrain model generation and river shoreline extraction). To solve the problem of distinguishing the water points from the land points in complex landscapes, an adaptive classification algorithm of water LiDAR point clouds is proposed, which consists of the following steps. First, the descriptors of local terrain slope and point density are designed by analyzing the characteristics of low-altitude airborne LiDAR water point clouds. Then Bayes' theorem is introduced to establish membership functions of the elevation, slope, and density. Next, the adaptive weights of the individual membership functions are determined according to the t-test of the independent samples of water and land points. Finally, a classification model based on multifeature statistics is obtained, and the adaptive classification threshold of the model is determined by the probability density of the training samples. Typical experiments conducted in the middle-lower Yangtze River riparian zone indicate that water classification accuracies higher than 99% are obtained by this algorithm, even in complex landscapes with mudflats and inland plains. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： light detection and ranging low-attitude airborne laser scanning water point clouds classification algorithm digital terrain model

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Speech feature selection and emotion recognition based on weighted binary cuckoo search

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ALEXANDRIA ENGINEERING JOURNAL 2021年第1期60卷 1499-1507页

作者： Zhang, Zicheng Nanjing Univ Sch Informat Management Nanjing 210023 Peoples R China Jiangsu Key Lab Data Engn & Knowledge Serv Nanjing 210023 Peoples R China

In this paper, a hybrid system is proposed for speech emotion recognition (SER). The algorithm in this paper adopts a two-stage design concept. In the first stage, we use the ensemble learning model random forest algorithm to obtain the importance of each feature. We use Emo- DB for experimental comparison and find that the combination of the logistic regression algorithm and the WBCS algorithm achieves best results. Cross-training method is used to ensure the features adapt to various situations. Under 100 training sets, the sentiment classification results are satisfactory. The proposed method is more accurate than state-of-the-art intelligent optimization dimensionality reduction algorithms. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University.

关键词： classification algorithm Weighted binary cuckoo search Feature dimensionality reduction Speech emotion recognition

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Human performance modeling for manufacturing based on an improved KNN algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2016年第1-4期84卷 473-483页

作者： Li, Ni Kong, Haipeng Ma, Yaofei Gong, Guanghong Huai, Wenqing Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

Human performance is a key factor in a manufacturing system. Behavior modeling is a very important but difficult problem when describing human activities. Performance modeling based on data mining is an effective way to help managers predict personnel's capabilities and to recruit appropriate new staff with relevant skills, which can be significant to ensure an enterprise's competitiveness. The K-nearest neighbor (KNN) algorithm is the most common classification algorithm in cases of no prior knowledge of data distribution. In the paper, an improved KNN algorithm was proposed to cope with the human performance prediction problem with the improvements in three aspects, which are the neighboring distance calculation based on entropy, the classification determination strategy, and the quantitative description method of human performance. The improved KNN algorithm was proved to have better classification ability in comparison with other seven typical KNN algorithms and five classic classification algorithms using data sets from the University of California, Irvine (UCI) machine learning repository. The improved KNN algorithm was further tested with a real-world case of performance prediction, and the effectiveness of the algorithm was confirmed.

关键词： Improved KNN Human performance modeling Data mining classification algorithm Behavior modeling

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Feature extraction and selection for objective gait analysis and fall risk assessment by accelerometry

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BIOMEDICAL ENGINEERING ONLINE 2011年第1期10卷 1-19页

作者： Caby, Benoit Kieffer, Suzanne de Saint Hubert, Marie Cremer, Gerald Macq, Benoit Catholic Univ Louvain Telecommun & Teledetect Lab Louvain Belgium Mt Godinne Univ Clin Dept Geriatr Mt Godinne Belgium

Background: Falls in the elderly is nowadays a major concern because of their consequences on elderly general health and moral states. Moreover, the aging of the population and the increasing life expectancy make the prediction of falls more and more important. The analysis presented in this article makes a first step in this direction providing a way to analyze gait and classify hospitalized elderly fallers and non-faller. This tool, based on an accelerometer network and signal processing, gives objective informations about the gait and does not need any special gait laboratory as optical analysis do. The tool is also simple to use by a non expert and can therefore be widely used on a large set of patients. Method: A population of 20 hospitalized elderlies was asked to execute several classical clinical tests evaluating their risk of falling. They were also asked if they experienced any fall in the last 12 months. The accelerations of the limbs were recorded during the clinical tests with an accelerometer network distributed on the body. A total of 67 features were extracted from the accelerometric signal recorded during a simple 25 m walking test at comfort speed. A feature selection algorithm was used to select those able to classify subjects at risk and not at risk for several classification algorithms types. Results: The results showed that several classification algorithms were able to discriminate people from the two groups of interest: fallers and non-fallers hospitalized elderlies. The classification performances of the used algorithms were compared. Moreover a subset of the 67 features was considered to be significantly different between the two groups using a t-test. Conclusions: This study gives a method to classify a population of hospitalized elderlies in two groups: at risk of falling or not at risk based on accelerometric data. This is a first step to design a risk of falling assessment system that could be used to provide the right treatment as soon

关键词： Radial Basis Function classification algorithm Direct Feature Feature Selection algorithm Stop Line

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Review on Biomedical Sensors, Technologies and algorithms for Diagnosis of Sleep Disordered Breathing: Comprehensive Survey

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IEEE REVIEWS IN BIOMEDICAL ENGINEERING 2022年 15卷 4-22页

作者： Ahmadzadeh, Sahar Luo, Jikui Wiffen, Richard Univ Bolton Sch Engn Bolton BL3 5AB England Univ Bolton Dept Engn Bolton BL3 5AB England Zhejiang Univ Coll Informat Sci & Elect Engn Key Lab Adv Micro Nano Elect Devices & Smart Syst Hangzhou 310027 Peoples R China Life Healthcare Ltd Chester CH1 2NP Cheshire England

This paper provides a comprehensive review of available technologies for measurements of vital physiology related parameters that cause sleep disordered breathing (SDB). SDB is a chronic disease that may lead to several health problems and increase the risk of high blood pressure and even heart attack. Therefore, the diagnosis of SDB at an early stage is very important. The essential primary step before diagnosis is measurement. Vital health parameters related to SBD might be measured through invasive or non-invasive methods. Nowadays, with respect to increase in aging population, improvement in home health management systems is needed more than even a decade ago. Moreover, traditional health parameter measurement techniques such as polysomnography are not comfortable and introduce additional costs to the consumers. Therefore, in modern advanced self-health management devices, electronics and communication science are combined to provide appliances that can be used for SDB diagnosis, by monitoring a patient's physiological parameters with more comfort and accuracy. Additionally, development in machine learning algorithms provides accurate methods of analysing measured signals. This paper provides a comprehensive review of measurement approaches, data transmission, and communication networks, alongside machine learning algorithms for sleep stage classification, to diagnose SDB.

关键词： Biomedical monitoring Sleep apnea Monitoring Blood pressure Wireless sensor networks SDB body area network classification algorithm invasive non-invasive respiratory monitoring system architecture sensor sleep stages wearable technology

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Comparison of advanced imputation algorithms for detection of transportation mode and activity episode using GPS data

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TRANSPORTATION PLANNING AND TECHNOLOGY 2016年第2期39卷 180-194页

作者： Feng, Tao Timmermans, Harry J. P. Eindhoven Univ Technol Dept Built Environm Urban Planning Grp POB 513 NL-5600 MB Eindhoven Netherlands

Global Positioning System (GPS) technologies have been increasingly considered as an alternative to traditional travel survey methods to collect activity-travel data. algorithms applied to extract activity-travel patterns vary from informal ad-hoc decision rules to advanced machine learning methods and have different accuracy. This paper systematically compares the relative performance of different algorithms for the detection of transportation modes and activity episodes. In particular, naive Bayesian, Bayesian network, logistic regression, multilayer perceptron, support vector machine, decision table, and C4.5 algorithms are selected and compared for the same data according to their overall error rates and hit ratios. Results show that the Bayesian network has a better performance than the other algorithms in terms of the percentage correctly identified instances and Kappa values for both the training data and test data, in the sense that the Bayesian network is relatively efficient and generalizable in the context of GPS data imputation.

关键词： Bayesian network decision tree classification algorithm data imputation activity-travel data Global Positioning System (GPS) Travel survey rules

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