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A Fault Diagnosis Method for Oil Well Electrical Power Diagrams Based on Multidimensional clustering Performance Evaluation

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SENSORS 2025年第6期25卷 1688-1688页

作者： Liu, Xingyu Meng, Xin Hu, Ze Duan, Hancong Wang, Min Chen, Yaping Southwest Petr Univ Sch Elect Informat Chengdu 610500 Peoples R China Univ Elect Sci & Technol China Sch Comp Chengdu 611731 Peoples R China

In oilfield extraction activities, traditional downhole condition monitoring is typically conducted using dynamometer cards to capture the dynamic changes in the load and displacement of the sucker rod. However, this method has severe limitations in terms of real-time performance and maintenance costs, making it difficult to meet the demands of modern extraction. To overcome these shortcomings, this paper proposes a novel fault detection method based on the analysis of motor power parameters. Through the dynamic mathematical modeling of the pumping unit system, we transform the indicator diagram of beam-pumping units into electric power diagrams and conduct an in-depth analysis of the characteristics of electric power diagrams under five typical operating conditions, revealing the impact of different working conditions on electric power. Compared to traditional methods, we introduce fourteen new features of the electrical parameters, encompassing multidimensional analyses in the time domain, frequency domain, and time-frequency domain, significantly enhancing the richness and accuracy of feature extraction. Additionally, we propose a new effectiveness evaluation method for the FCM clustering algorithm, integrating fuzzy membership degrees and the geometric structure of the dataset, overcoming the limitations of traditional clustering algorithms in terms of accuracy and the determination of the number of clusters. Through simulations and experiments on 10 UCI datasets, the proposed effectiveness function accurately evaluates the clustering results and determines the optimal number of clusters, significantly improving the performance of the clustering algorithm. Experimental results show that the fault diagnosis accuracy of our method reaches 98.4%, significantly outperforming traditional SVM and ELM methods. This high-precision diagnostic result validates the effectiveness of the method, enabling the efficient real-time monitoring of the working status of beam-pumpin

关键词： beam-pumping system electrical parameters FCM clustering algorithm cluster validity function

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cluster validity functions for categorical data: a solution-space perspective

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DATA MINING AND KNOWLEDGE DISCOVERY 2015年第6期29卷 1560-1597页

作者： Bai, Liang Liang, Jiye Shanxi Univ Sch Comp & Informat Technol Key Lab Computat Intelligence & Chinese Informat Minist Educ Taiyuan 030006 Shanxi Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Network Data Sci & Technol Beijing 100190 Peoples R China

For categorical data, there are three widely-used internal validity functions: the -modes objective function, the category utility function and the information entropy function, which are defined based on within-cluster information only. Many clustering algorithms have been developed to use them as objective functions and find their optimal solutions. In this paper, we study the generalization, effectiveness and normalization of the three validity functions from a solution-space perspective. First, we present a generalized validity function for categorical data. Based on it, we analyze the generality and difference of the three validity functions in the solution space. Furthermore, we address the problem whether the between-cluster information is ignored when these validity functions are used to evaluate clustering results. To the end, we analyze the upper and lower bounds of the three validity functions for a given data set, which can help us estimate the clustering difficulty on a data set and compare the performance of a clustering algorithm on different data sets.

关键词： cluster analysis cluster validity function Generalization Effectiveness Normalization

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Fuzzy C-Means clustering with Spatially Weighted Information for Medical Image Segmentation

Fuzzy C-Means Clustering with Spatially Weighted Information...

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IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP)

作者： Kang, Myeongsu Kim, Jong-Myon Univ Ulsan Dept Elect Elect & Comp Engn Ulsan South Korea Univ Ulsan Dept IT Convergence Ulsan South Korea

ISBN: (纸本)9781479945030

Image segmentation is an essential process in image analysis and is mainly used for automatic object recognition. Fuzzy c-means (FCM) is one of the most common methodologies used in clustering analysis for image segmentation. FCM clustering measures the common Euclidean distance between samples based on the assumption that each feature has equal importance. However, in most real-world problems, features are not considered equally important. To overcome this issue, we present a fuzzy c-means algorithm with spatially weighted information (FCM-SWI) that takes into account the influence of neighboring pixels on the center pixel by assigning weights to the neighbors. These weights are determined based on the distance between a corresponding pixel and the center pixel to indicate the importance of the memberships. Such a process leads to improved clustering performance. Experimental results show that the proposed FCM-SWI outperforms other FCM algorithms (FCM, modified FCM, and spatial FCM, FCM with spatial information, fast generation FCM) in both compactness and separation. Furthermore, the proposed FCM-SWI outperforms the classical algorithms in terms of quantitative comparison scores corresponding to a T1-weighted MR phantom for gray matter, white matter, and cerebrospinal fluid (CSF) slice regions.

关键词： fuzzy c-means image segmentation cluster validity function ground truth verification

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Takagi-Sugeno Fuzzy Modeling and Control of Nonlinear System with Adaptive clustering Algorithms 10

Takagi-Sugeno Fuzzy Modeling and Control of Nonlinear System...

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10th International Conference on Modelling, Identification and Control (ICMIC)

作者： Zhao, Kai Li, Shurong Kang, Zhongjian China Univ Petr Coll Informat & Control Engn Qingdao 266580 Peoples R China Anyang Inst Technol Coll Comp Sci & Informat Engn Anyang 455000 Peoples R China Beijing Univ Posts & Telecommun Automat Sch Beijing 100876 Peoples R China

ISBN: (纸本)9781538654163

In order to control a nonlinear system, a model needs to be established to predict its behavior. At present, there are many methods for nonlinear system modeling. Among them T-S fuzzy prediction model has attracted extensive attention due to its better generalization and excellent approximation in the dense region. clustering algorithms can be used for the premise identification of the T-S model. But the optimal premise is not easy to be determined because of the difficulty to obtain optimal clustering number. For solving the shortcoming, a clustering validity function is described, based on which the clustering performance of adaptive fuzzy C-means clustering algorithm (adaptive FCM) is compared to that of the adaptive alternative fuzzy C-mean clustering algorithm (adaptive AFCM) with three datasets. Furthermore, two modeling algorithms for T-S fuzzy model using the adaptive FCM and the adaptive AFCM are designed, combining with the RLS, named adaptive FCM-RLS and adaptive AFCM-RLS. Finally, in order to demonstrate the effectiveness of the modeling methods in this paper, the T-S fuzzy model of a batch progress is constructed by adaptive FCM-RLS. With the T-S model, fuzzy generalized predictive controller is designed. Simulation results show that fuzzy-GPC controller has the better performances than GPC controller designed with least square method.

关键词： Nonlinear system cluster validity function Fuzzy model T-S RLS Adaptive FCM Fuzzy-GPC

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clustering by a Genetic Algorithm with Biased Mutation Operator

Clustering by a Genetic Algorithm with Biased Mutation Opera...

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2010 IEEE World Congress on Computational Intelligence

作者： Auffarth, Benjamin Univ Barcelona Inst Bioengn Catalonia Bcn 08028 Spain

ISBN: (纸本)9781424481262

In this paper we propose a genetic algorithm that partitions data into a given number of clusters. The algorithm can use any cluster validity function as fitness function. cluster validity is used as a criterion for cross-over operations. The cluster assignment for each point is accompanied by a temperature and points with low confidence are preferentially mutated. We present results applying this genetic algorithm to several UCI machine learning data sets and using several objective cluster validity functions for optimization. It is shown that given an appropriate criterion function, the algorithm is able to converge on good cluster partitions within few generations. Our main contributions are: 1. to present a genetic algorithm that is fast and able to converge on meaningful clusters for real-world data sets, 2. to define and compare several cluster validity criteria.

关键词： UCI machine learning cluster validity function criterion function crossover operation fitness function genetic algorithm genetic algorithms learning (artificial intelligence) mutation operator optimization pattern clustering

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A Self-Adaptive FCM for the Optimal Fuzzy Weighting Exponent

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2019年第2期18卷

作者： Ren, Min Wang, Zhihao Jiang, Jirong Shandong Univ Finance & Econ Sch Math & Quantitat Econ Jinan Shandong Peoples R China Shandong Univ Finance & Econ Sch Management & Engn Jinan Shandong Peoples R China

Fuzzy weighting exponent m is an important parameter of fuzzy c-means (FCM), closely related to the performance of the algorithm. First, an improved fuzzy correlation degree was put forward to measure the relevance between the clusters, based on which a new cluster validity function was defined to evaluate the quality of the fuzzy partition. Then a self-adaptive FCM for the optimal value of m was proposed with the aid of the global search ability of improved particle swarm algorithm to find out both the final clustering centroids and the optimal value of fuzzy weighting exponent automatically. The improved particle swarm algorithm updated the speed and the position based on dynamic inertia weight and learning factors, and introduced mutation of genetic algorithm to keep the diversity of the particles, preventing premature convergence. The experimental results showed that the proposed algorithm automatically calculated the optimal value of m and meanwhile achieved better clustering results.

关键词： Fuzzy weighting exponent fuzzy c-means algorithm cluster validity function particle swarm optimization

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