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Fault diagnosis of rotating machines based on modified hierarchical fluctuation dispersion entropy and multi-cluster feature selection

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2023年第12期37卷 6257-6270页

作者： Li, Baoyue Yu, Yonghua Hu, Jia Cao, Bingxin Yao, Yangfeng Xu, Defeng Wuhan Univ Technol Sch Naval Architecture Ocean & Energy Power Engn Wuhan 430063 Peoples R China Wuhan Univ Technol Key Lab Marine Power Engn & Technol Minist Transport Peoples Republ China Wuhan 430063 Peoples R China Natl Engn Lab Ship & Marine Engn Power Syst Wuhan 430063 Peoples R China

The vibration signal of rotating mechanical equipment contains a large amount of information that can be used for the fault diagnosis of rotating mechanical equipment. However, the vibration information is distributed in multiple dimensions, and a single-scale analysis cannot effectively reflect its damage characteristics, reducing the accuracy of fault diagnosis. Accordingly, an improved hierarchical fluctuation dispersion entropy (MHFDE) method based on the improved hierarchical processing is proposed. MHFDE can simultaneously mine low- and high-frequency features in the time series, avoiding information omission. Comparison results of the simulated signals show that the proposed method has the advantages of high stability and accurate measurement of complexity. In combination with the multi-cluster feature selection (MCFS) and kernel limit learning machine (KELM) optimized by whale optimization algorithm (WOA), a rotating machinery damage recognition method based on MHFDE-MCFS and WOA-KEM was proposed. Three sets of typical rotating machinery datasets are used to verify the effectiveness of the proposed method. The results show that this method can not only accurately and stably identify the damage types of the three selected machinery but also have a higher accuracy of damage recognition compared with the existing feature extraction methods.

关键词： Fault diagnosis Kernel extreme learning machine Modified hierarchical fluctuation dispersion entropy multi-cluster feature selection Rotating machinery Whale optimization algorithm

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A Comparative Study on feature selection Techniques for multi-cluster Text Data 4th

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4th International Conference on Harmony Search, Soft Computing and Applications (ICHSA)

作者： Gupta, Ananya Begum, Shahin Ara Assam Univ Dept Comp Sci Silchar 788011 India

ISBN: (纸本)9789811307614;9789811307607

Text clustering involves data that are of very high dimension. feature selection techniques find subsets of relevant features from the original feature space that help in efficient and effective clustering. selection of relevant features merely on ranking scores without considering correlation interferes with the clustering performance. An efficient feature selection technique should be capable of preserving the multi-cluster structure of the data. The purpose of the present work is to demonstrate that feature selection techniques which take into consideration the correlation among features in multi-cluster scenario show better clustering results than those techniques that simply rank features independent of each other. This paper compares two feature selection techniques in this regard viz. the traditional Tf-Idf and the multi-cluster feature selection (MCFS) technique. The experimental results over the TDT2 and Reuters-21,578 datasets show the superior clustering results of MCFS over traditional Tf-Idf.

关键词： feature selection multi-cluster feature selection Tf-Idf clustering Text data

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Application of improved bubble entropy and machine learning in the adaptive diagnosis of rotating machinery faults

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ALEXANDRIA ENGINEERING JOURNAL 2023年 80卷 22-40页

作者： Gong, Jiancheng Yang, Xiaoqiang Qian, Kun Chen, Zhaoyi Han, Tao Army Engn Univ PLA Field Engn Coll Nanjing 210007 Jiangsu Peoples R China

This paper proposes an improved bubble entropy algorithm called Improved Hierarchical Refined Composite multiscale multichannel Bubble Entropy (IHRCMMCBE) to characterize the fault characteristics of rotating machinery. By introducing the refined composite multiscale analysis algorithm, the improved hierarchical decomposition algorithm, and the multi-channel data analysis method, the bubble entropy algorithm can more fully characterize the fault characteristics. Then, this method is combined with the machine learning algorithm to realize automatic fault diagnosis of rotating machinery. multi-cluster feature selection (MCFS) is used for feature selection to screen out useless features and improve feature extraction efficiency and feature quality. The MCFS method has excellent performance and does not need parameters, so it is suitable for adaptive fault diagnosis. Meanwhile, the EigenClass classifier using the concept of generalized eigenvalue is used to realize fault recognition. Besides, the recently proposed Gorilla Troops Optimizer (GTO) algorithm is used to improve EigenClass, and a GTO-optimized EigenClass (GTO-EigenClass) classifier is proposed to adaptively select parameters and achieve adaptive fault identification. The proposed fault diagnosis method of rotating machinery based on the improved bubble entropy is independent of data and application scenarios and is hardly affected by subjective factors. The ablation experiment proves the effectiveness of each improvement of bubble entropy. Also, the performance verification experiments and the comparative experiments prove that the method can effectively characterize different types of rotating machinery, different fault types, fault degrees, and composite faults, and it can achieve higher calculation efficiency and classification accuracy. When the sample length is 1024, the average accuracy of bearing and gearbox fault identification can reach 97.85 % and 97.57 % respectively, and when the sample length is

关键词： Bubble entropy Rotating machinery fault diagnosis Machine learning multi-cluster feature selection

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Elman Neural Network Soft-Sensor Model of PVC Polymerization Process Optimized by Chaos Beetle Antennae Search Algorithm

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IEEE SENSORS JOURNAL 2021年第3期21卷 3544-3551页

作者： Gao, Shuzhi Zhang, Yimeng Zhang, Yimin Zhang, Guoguang Shenyang Univ Chem Technol Equipment Reliabil Inst Shenyang 110142 Peoples R China

The conversion rate of vinyl chloride monomer (VCM) is an important product quality indicator in the process of Polyvinyl chloride (PVC) polymerization. Due to the complexity of the PVC polymerization process and the limitation of site conditions, it is difficult to obtain the VCM conversion rate online in real ***, this article puts forward a soft-sensor model based on Beetle Antennae Search Algorithm (BAS) to optimize Elman neural network(Elman). Firstly, multi-cluster feature selection (MCFS) is used to reduce the dimensionality of the high-dimensional input variables, so that we get auxiliary variables of the soft-sensor model. Then, using Elman neural network as a soft-sensor model, and it is trained by the proposed optimization algorithm, which combines the chaotic map and the Beetle Antennae Search Algorithm (CBAS). The simulation results show that the model can significantly improve the prediction accuracy of the VCM conversion rate while realizing the real-time control of the PVC polymerization production process.

关键词： Polymers feature extraction Prediction algorithms Optimization Predictive models Recurrent neural networks PVC polymerization process multi-cluster feature selection soft-sensor Elman neural networks chaotic map beetle antennae search algorithm

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Modified Hierarchical multiscale Dispersion Entropy and its Application to Fault Identification of Rotating Machinery

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IEEE ACCESS 2020年 8卷 161361-161376页

作者： Zhou, Fuming Shen, Jinxing Yang, Xiaoqiang Liu, Xiaolin Liu, Wuqiang Army Engn Univ Coll Field Engn Nanjing 210007 Peoples R China

The rotating machinery possesses complicated structures and various fault types, whose health state monitoring is essential for the normal production and operation of the equipment. To distinguish different working states of rotating machinery efficiently and accurately, this article presents a novel approach for extracting fault features of vibration signals called modified hierarchical multiscale dispersion entropy (MHMDE). And on this basis, an innovative approach for fault diagnosis of rotating machinery based on MHMDE, multi-cluster feature selection (MCFS) and particle swarm optimization kernel extreme learning machine (PSO-KELM) is developed. Firstly, MHMDE is employed to extract the high-dimensional fault features of rotating machinery. This approach can effectively overcome the shortcomings that multi-scale entropy only focuses on the information in the low-frequency components but discards the high-frequency information, as well as the significant dropping of efficiency if the number of hierarchical layers of hierarchical entropy is large. Then MCFS is employed to screen the sensitive features from the high-dimensional fault features. Finally, the sensitive feature vectors are input into the PSO-KELM-based fault classifier to complete the rotating machinery fault diagnosis. It is proved that the presented approach can effectively identify different fault states of rotating machinery through three typical examples. Meanwhile, the presented approach is compared with multi-scale dispersion entropy (MDE) and hierarchical dispersion entropy (HDE), etc. The results show that the presented approach possesses more superior performance.

关键词： Rotating machinery fault diagnosis modified hierarchical multiscale dispersion entropy multi-cluster feature selection PSO-KELM

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feature selection for Breast Cancer Detection from Ultrasound Images

Feature Selection for Breast Cancer Detection from Ultrasoun...

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International Conference on Informatics, Electronics and Vision (ICIEV)

作者： Nayeem, Mohd Ashique Ridwan Joadder, Md A. Mannan Shetu, Shahrin Ahammad Jamil, Farzin Raeeda Al Helal, Abdullah United Int Univ Dept Elect & Elect Engn Dhaka Bangladesh

ISBN: (纸本)9781479951802

Breast cancer is the most lethal form of cancer in women after lung cancer. Early detection of cancer is likely to improve the patient's ability to deal with the disease and live further. Ultrasound imaging technique is one of the available tools for cancer diagnosis. Over the years, several high precision features were suggested by researchers to distinguish between malignant and benign lesions. This work employs more than fifty of these features which may serve as a reference feature pool to the researchers. Eventually, we seek to select an optimized subset of this feature set by using three different feature selection methods. In this work, we have successfully employed multi-cluster feature selection, a recently developed feature selection method, to find a feature set that best describes breast cancer. Thus, we propose a Computer Aided Diagnosis tool with an optimum combination of 25 different features to differentiate between malignant and benign tumors. These features were fed into Sparse Representation Classifier to classify tumors. The proposed technique was examined on ultrasound scans of 504 pathologically diagnosed breast tumors including 454 benign and 50 malignant tumors. The resulting Area Under the Receiver Operating Characteristic Curve was found to be 93.31%.

关键词： breast cancer ultrasonography feature extraction feature selection multi-cluster feature selection sparse representation classifier

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