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Motor fault detection and diagnosis using a hybrid FMM-CART model with online learning

JOURNAL OF INTELLIGENT MANUFACTURING 2016年第6期27卷 1273-1285页

作者： Seera, Manjeevan Lim, Chee Peng Loo, Chu Kiong Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur Malaysia Deakin Univ Ctr Intelligent Syst Res Geelong Vic Australia

In this paper, a hybrid online learning model that combines the fuzzy min-max (FMM) neural network and the classification and regression tree (CART) for motor fault detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, incorporates the advantages of both FMM and CART for undertaking data classification (with FMM) and rule extraction (with CART) problems. In particular, the CART model is enhanced with an importance predictor-based feature selection measure. To evaluate the effectiveness of the proposed online FMM-CART model, a series of experiments using publicly available data sets containing motor bearing faults is first conducted. The results (primarily prediction accuracy and model complexity) are analyzed and compared with those reported in the literature. Then, an experimental study on detecting imbalanced voltage supply of an induction motor using a laboratory-scale test rig is performed. In addition to producing accurate results, a set of rules in the form of a decision tree is extracted from FMM-CART to provide explanations for its predictions. The results positively demonstrate the usefulness of FMM-CART with online learning capabilities in tackling real-world motor fault detection and diagnosis tasks.

关键词： classification and regression tree Fault detection and diagnosis Fuzzy min-max neural network Induction motor

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Optimal engineering system design guided by data-mining methods

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TECHNOMETRICS 2005年第3期47卷 336-348页

作者： Kim, P Ding, Y Kyungpook Natl Univ Sch Business Adm Taegu 702701 South Korea Texas A&M Univ Dept Ind Engn College Stn TX 77843 USA

An optimal engineering design problem is challenging because nonlinear objective functions usually need to be evaluated in a high-dimensional design space. This article presents a data-mining-aided optimal design method, that is able to find a competitive design solution with a relatively low computational cost. The method consists of four components: (1) a uniform-coverage selection method, that chooses design representatives from among a large number of original design alternatives for a nonrectangular design space;(2) feature functions, of which evaluation is computationally economical as the surrogate for the design objective function;(3) a clustering method, that generates a design library based on the evaluation of feature functions instead of an objective function;and (4) a classification method to create the design selection rules, eventually leading us to a competitive design. Those components are implemented to facilitate the optimal fixture layout design in a multistation panel assembly process. The benefit of the data-mining-aided optimal design is clearly demonstrated by comparison with both local optimization methods (e.g., simplex search) and random search-based optimizations (e.g., simulated annealing).

关键词： classification and regression tree fixture layout optimization K-means clustering kriging model multistation assembly processes uniform design

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Split variable selection for tree modeling on rank data

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2012年第9期56卷 2830-2836页

作者： Kung, Yi-Hung Lin, Chang-Ting Shih, Yu-Shan Natl Chung Cheng Univ Dept Math Minhsiung Taiwan Natl Hlth Res Inst Inst Populat Hlth Sci Taipei Taiwan

A variable selection method for constructing decision trees with rank data is proposed. It utilizes conditional independence tests based on loglinear models for contingency tables. Compared with other selection methods, our method is computationally more efficient. Moreover, our method is relatively unbiased and powerful in selecting the correct split variables. Simulation results and a real data study are given to demonstrate the strength of our method. (C) 2012 Elsevier B.V. All rights reserved.

关键词： classification and regression tree Conditional independence Distance-based model Log linear model Selection bias

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An exploratory study of object-oriented software component size determinants and the application of regression tree forecasting models

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INFORMATION & MANAGEMENT 2004年第1期42卷 61-73页

作者： Pendharkar, PC Penn State Univ Harrisburg Sch Business Adm Middletown PA 17057 USA

Software component size estimation is an important task in software project management. For a component-based approach, two steps may be used to estimate the overall size of object-oriented (OO) software: a designer uses metrics to predict the size of the software components and then utilizes the sizes to estimate the overall project size. Using OO software metrics literature, we identified factors that may affect the size of an OO software component. Using real-life data from 152 software components, we then determined the effect of the identified factors on the prediction of OO software component size. The results indicated that certain factors and the type of OO software component play a significant role in the estimate. It is shown how a regression tree data mining approach can be used to learn decision rules to guide future estimates. (C) 2004 Elsevier B.V. All rights reserved.

关键词： software engineering software size data mining forecasting classification and regression tree

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Prediction of the crack condition of highway pavements using machine learning models

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STRUCTURE AND INFRASTRUCTURE ENGINEERING 2019年第7期15卷 940-953页

作者： Inkoom, Sylvester Sobanjo, John Barbu, Adrian Niu, Xufeng FAMU FSU Coll Engn Dept Civil & Environm Engn Tallahassee FL 32310 USA Florida State Univ Dept Stat Tallahassee FL 32306 USA

Departments of Transportation regularly evaluate the condition of pavements through visual inspections, nondestructive evaluations, image recognition models and learning algorithms. The above methodologies, though efficient, have drawn attention due to their subjective errors, uncertainties, noise effects and overfitting. To improve on the outcomes of the shallow learning models already used in pavement crack prediction, this paper reports on an investigation of the use of recursive partitioning and artificial neural networks (ANN;deep learning frameworks) in predicting the crack rating of pavements. Explanatory variables such as the average daily traffic and truck factor, roadway functional class, asphalt thickness, and pavement condition time series data are employed in the model formulation. Overall, it is observed that the recursive partitioning (regression tree - R-2 > 0.8 and classification tree - R-2 > 0.6) and ANN (continuous response - R-2 > 0.8 and categorical response - R-2 > 0.6) are compelling machine learning models for the prediction of the crack ratings based on their goodness-of-fit statistics, mean absolute deviation (MAD < 0.4) and the root mean square errors (RMSE between 0.30 and 0.65).

关键词： Pavement crack prediction recursive partitioning artificial neural network classification and regression tree machine learning infrastructure performance modelling

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Within-yard habitat use by white-tailed deer at varying winter severity

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FOREST ECOLOGY AND MANAGEMENT 2003年第2-3期172卷 173-182页

作者： Morrison, SF Forbes, GJ Young, SJ Lusk, S Univ New Brunswick New Brunswick Cooperat Fish & Wildlife Res Unit Fredericton NB E3B 6C2 Canada Fraser Papers Inc Edmundston NB E3V 1S9 Canada Dept Nat Resources & Energy Fish & Wildlife Branch Edmundston NB E3B 5H1 Canada

Vegetative, topological, and spatial features that were associated with habitat use of radio-collared white-tailed deer (Odocoileus virginianus, n = 17 in 1996-1997 and n = 13 in 1997-1998) were identified as snow depth changed in the Odell Deeryard, NB, Canada. Logistic regressions and classification trees revealed that use of forest stands varied with snow depth, and between the 1996-1997 and 1997-1998 yarding periods. Stand use at all snow depths was inversely related to the distance to stands with >50% softwood canopy cover (SCC). Stand use during periods of low snow depth (<20 cm) also was predicted by features such as the basal area of spruce (Picea spp. A. Dietr.) <2.5 m(2)/ha and browse availability >10% of ground cover. At moderate snow depths (2050 cm) deer were more likely to use stands within 290 m of 30-50% SCC, within 215 m of >50% SCC, and having low slopes (<4.5degrees). During periods of deep snow (>50 cm) only those features that described shelter were useful in predicting habitat use variation between years. Deer were not associated with browse-related features when snow depth exceeded 50 cm. In 1996-1997, a winter of average snow depth, deer were more likely to use stands with low perimeter:area ratios (<0.04 m/m(2)), within 220 m of cedar/hemlock-dominated (Thuja occidentalis L./Tsuga canadensis (L.) Carr.) stands, and with low basal areas of pine/larch (<0.3 m(2)/ha, Pinus spp. L./Larix laricina (Du Roi) K. Koch). In 1997-1998, an exceptionally harsh winter, deer left the yard in late January and moved to nearby timber harvest operations. However, while in the yard, deer selected stands at lower elevations (<180 m), but did not differentiate between softwood shelter species. Results suggest that deer use stands based on the spatial arrangement of shelter within the deeryard, and not just that of the individual stand. We suggest that the supply of browse during winter should be an explicit part of any deeryard management plan. (C) 2002 Elsevier

关键词： browse classification and regression tree deeryard habitat radio-telemetry shelter white-tailed deer winter severity

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EEG-based classification for elbow versus shoulder torque intentions involving stroke subjects

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COMPUTERS IN BIOLOGY AND MEDICINE 2009年第5期39卷 443-452页

作者： Zhou, Jie Yao, Jun Deng, Jie Dewald, Julius P. A. No Illinois Univ Dept Comp Sci De Kalb IL 60115 USA Northwestern Univ Dept Phys Therapy & Human Movement Sci Evanston IL 60208 USA Northwestern Univ Dept Biomed Engn Evanston IL 60208 USA Northwestern Univ Dept Phys Med & Rehabil Evanston IL 60208 USA

The ultimate aim for classifying elbow versus shoulder torque intentions is to develop robust brain-computer interface (BCI) devices for patients who suffer from movement disorders following brain injury such as stroke. In this paper, we investigate the advanced classification approach classifier-enhanced time-frequency synthesized spatial pattern algorithm (classifier-enhanced TFSP) in classifying a subject's intent of generating an isometric shoulder abduction (SABD) or elbow flexion (EF) torque using signals obtained from 163 scalp electroencephalographic (EEG) electrodes. Two classifiers, the support vector classifier (SVC) and the classification and regression tree (CART), are integrated in the TFSP algorithm that decomposes the signal into a weighted time, frequency and spatial feature space. The resulting high-performing methods (SVC-TFSP and CART-TFSP) are then applied to experimental data collected in four healthy subjects and two stroke subjects. Results are compared with the original TFSP, and significantly higher reliability in both healthy subjects (92% averaged over four healthy subjects) and stroke subjects (75% averaged over two subjects) are achieved. The accuracies of classifier-enhanced TFSP methods are further improved after a rejection scheme is applied (similar to 100% in healthy subjects and > 80% in stroke subjects). The results are among the highest reliability reported in literature for tasks with spatial representations on the motor cortex as close as shoulder and elbow. The paper also discusses the impact of applying rejection strategy in detail and reports the existence of an optimal rejection rate on a stroke subject. The results indicate that the proposed algorithms are promising for future use of rehabilitative BCI applications in neurologically impaired patients. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Electroencephalograph (EEG) classification Stroke Elbow flexion Shoulder abduction Brain-computer interface Time-frequency synthesized spatial pattern algorithm Support-vector machine classification and regression tree

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Prediction of current-induced scour depth around pile groups using MARS, CART, and ANN approaches

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MARINE GEORESOURCES & GEOTECHNOLOGY 2021年第5期39卷 577-588页

作者： Samadi, Mehrshad Afshar, Mohammad Hadi Jabbari, Ebrahim Sarkardeh, Hamed IUST Sch Civil Engn POB 16765-163 Tehran Iran Hakim Sabzevari Univ Fac Engn Dept Civil Engn Sabzevar Iran

Scour around piles is considered as one of the most important phenomena which could undermine the stability of piles. Data-driven methods are now being used more and more to predict complex phenomena such as scour around of pile groups. In this study, three data-driven methods including multivariate adaptive regression spline (MARS), classification and regression tree (CART) and artificial neural network (ANN), are used for determining current-induced scour depth around pile groups in clear water conditions. Obtained results are compared with those of M5 Model tree (M5MT) which shows the higher precision of them. The calculated statistical indices revealed the higher accuracy of MARS in prediction of scour depth (correlation coefficient [CC] = 0.872 and root mean square error [RMSE] = 0.233) in comparison with ANN (CC = 0.843 and RMSE = 0.256), CART (CC = 0.734 and RMSE = 0.323) and M5MT (CC = 0.761 and RMSE = 0.404) for testing data. Furthermore, the developed discrepancy ratio (DDR) index confirms the outperformance of MARS in comparison with other approaches for prediction of scour depth. Finally, the parametric investigations conducted through MARS show that the parameter of has the highest effect on the scour depth. This finding is in agreement with the results of CART.

关键词： Artificial neural network classification and regression tree multivariate adaptive regression splines model tree pile groups scour depth

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A Voltage Phasor Based Fault-classification Method for Phasor Measurement Unit Only State Estimator Output

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2015年第1期43卷 22-31页

作者： Gao, Fenghua Thorp, James S. Gao, Shibin Pal, Anamitra Vance, Katelynn A. Southwest Jiaotong Univ Dept Elect Engn Chengdu 610031 Peoples R China Virginia Tech Bradley Dept Elect & Comp Engn Blacksburg VA USA

Abstract-This article presents a fault-classification method for transmission lines based on voltage phasors using classification and regression trees. The proposed method is intended to aid system operators in understanding the outputs of a phasor measurement unit only state estimator. Faults are classified into four categories when the estimator is positive sequence and into ten categories when the estimator is three phase. The fault data are generated in PowerWorld (R) (PowerWorld Corporation, Champaign, IL, USA) and DSA Tools (R) (Powertech Labs Inc., Surrey, British Columbia, Canada). The pre-fault state consists of a variety of operating conditions and loading angles of faulted lines. The fault condition comprises different fault types, fault locations, fault impedances, and fault incidence angles. Fault classification is done using MATLAB (R) (The MathWorks, Natick, Massachusetts, USA).The approach is successfully tested on the IEEE-118 bus system. The results demonstrate that the technique developed here is effective and robust, irrespective of the pre-fault and fault conditions.

关键词： voltage phasors fault type classification and regression tree fault voltage state estimation phasor measurement units fault classification

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Unimodal transform of variables selected by interval segmentation purity for classification tree modeling of high-dimensional microarray data

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TALANTA 2011年第3期85卷 1689-1694页

作者： Du, Wen Cu, Ting Tang, Li-Juan Jiang, Jian-Hui Wu, Hai-Long Shen, Guo-Li Yu, Ru-Qin Hunan Univ Coll Chem & Chem Engn Lab Tobacco Chem State Key Lab Chemo Biosensing & Chemometr Changsha 410082 Hunan Peoples R China

As a greedy search algorithm, classification and regression tree (CART) is easily relapsing into overfitting while modeling microarray gene expression data. A straightforward solution is to filter irrelevant genes via identifying significant ones. Considering some significant genes with multi-modal expression patterns exhibiting systematic difference in within-class samples are difficult to be identified by existing methods, a strategy that unimodal transform of variables selected by interval segmentation purity (UTISP) for CART modeling is proposed. First, significant genes exhibiting varied expression patterns can be properly identified by a variable selection method based on interval segmentation purity. Then, unimodal transform is implemented to offer unimodal featured variables for CART modeling via feature extraction. Because significant genes with complex expression patterns can be properly identified and unimodal feature extracted in advance, this developed strategy potentially improves the performance of CART in combating overfitting or underfitting while modeling microarray data. The developed strategy is demonstrated using two microarray data sets. The results reveal that UTISP-based CART provides superior performance to k-nearest neighbors or CARTs coupled with other gene identifying strategies, indicating UTISP-based CART holds great promise for microarray data analysis. (C) 2011 Elsevier B.V. All rights reserved.

关键词： classification and regression tree Interval segmentation purity Variable selection Gene expression Mean shift

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