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Intelligent fault diagnosis of rotating machinery using a new ensemble deep auto-encoder method

MEASUREMENT 2020年 151卷 107232-000页

作者： Zhang, Yuyan Li, Xinyu Gao, Liang Chen, Wen Li, Peigen Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan Hubei Peoples R China

In traditional intelligent fault diagnosis methods of rotating machinery, features are designed manually by experts, which makes these methods less automatic. deep auto-encoder (DA) provides an effective way to learn discriminative features. However, individual DA is of low generalization and not robust. This work develops an ensemble DA(EDA) method for intelligent fault diagnosis. EDA is constructed by combining sparse DA, denoising DA and contractive DA. Thus, EDA can effectively handle redundant information, noisy corruption and signal perturbation. To ensure the feature learning performance of each DA model, a self-adaptive fine-tuning is designed to obtain stable convergence. To enhance the discriminative features, a dynamic weighted average method is designed to aggregate these learned features. EDA is verified on three public datasets, and achieves the testing accuracies of 100%, 99.69% and 99.92%. Comparisons with other methods, including both traditional methods and individual DA methods, demonstrate that EDA obtains higher diagnosis accuracy. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Intelligent fault diagnosis Rotating machinery Ensemble model deep auto-encoder

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A robust and accurate indoor localization system using deep auto-encoder combined with multi-feature fusion

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2020年 1-11页

作者： Wang, Qinghu Northeast Univ Coll Comp Sci & Engn Shenyang 110169 Peoples R China Inner Mongolia Univ Nationalities Coll Comp Sci & Technol Tongliao 028000 Peoples R China

Many existing indoor localization systems use RSS as fingerprints, but RSS is a coarse-grained data, which not only fluctuates over time but also is not unique for a specific location due to rich multi-path effects and shadow fading in indoor environments. In order to further improve the localization accuracy, a robust and accurate indoor localization algorithm based on deep auto-encoder network combine with multi-feature fusion. To extract deep features hidden in CSI data and reduce the computational complexity of localization, an improved method is designed to achieve dimension reduction and feature extraction, which can avoid explicit information extraction of available CSI characteristics on the basis of effective representation of CSI fingerprint difference between different locations. Then, the fingerprint database is constructed by combination of RSS and CSI coding. Moreover, the Naive Bayer Classifier is adopted to improve the localization accuracy and stability. In the simulation experiment, the positioning effect of the proposed algorithm under different fingerprint libraries and the positioning effect under different positioning methods are mainly carried out. Experimental results show that the proposed method can effectively perform indoor positioning and has good practicability in the actual production environment.

关键词： Indoor localization deep learning Dimension reduction Multi-feature fusion deep auto-encoder Naive bayer classifier

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A Wasserstein gradient-penalty generative adversarial network with deep auto-encoder for bearing intelligent fault diagnosis

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MEASUREMENT SCIENCE AND TECHNOLOGY 2020年第4期31卷 045006-045006页

作者： Xiong, Xiong Jiang, Hongkai Li, Xingqiu Niu, Maogui Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China

It is a great challenge to manipulate unbalanced fault data in the field of rolling bearings intelligent fault diagnosis. In this paper, a novel intelligent fault diagnosis method called the Wasserstein gradient-penalty generative adversarial network with deep auto-encoder is proposed for intelligent fault diagnosis of rolling bearings. Firstly, the gradient penalty term is added to the Wasserstein generative adversarial network to enhance the stability and convergence of the network. Secondly, a deep auto-encoder network comprised of multiple auto-encoders is regarded as the discriminator. Finally, the sparse auto-encoder is placed at the end of the proposed method as the classifier to classify synthetic bearing faults. The results show that the proposed method has a better performance than traditional methods and the Wasserstein generative adversarial network.

关键词： rolling bearing intelligent fault diagnosis Wasserstein gradient-penalty generative adversarial network deep auto-encoder unsupervised learning

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A hybrid of Random Forest and deep auto-encoder with support vector regression methods for accuracy improvement and uncertainty reduction of long-term streamflow prediction

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JOURNAL OF HYDROLOGY 2021年 597卷 125717-125717页

作者： Abbasi, Mahdi Farokhnia, Ashkan Bahreinimotlagh, Masoud Roozbahani, Reza Univ Tehran Dept Irrigat & Reclamat Engn Karaj Iran Water Res Inst Tehran Iran

Streamflow forecasting is an important component of water resources planning and management. Data-Driven Models (DDMs) are common approaches for streamflow prediction. DDMs try to obtain a mathematical relation between variables without any prior knowledge or assumption about the physical relation between them. Hence, these models are effective for complex process modeling. In this study, we use the Support Vector Regression (SVR) and Multiple Linear Regression (MLR) models for long-term streamflow prediction. Moreover, three approaches, Random Forest (RF), deep auto-encoder (DAE), and Principle Component Analysis method (PCA) are employed as pre-processing methods and they are combined to SVR and MLR (six hybrid models produced) in order to improve the accuracy of prediction and reduce its uncertainty. Different evaluation criteria, including R-2, RMSE, NSE, P-factor, and R-factor are computed to evaluate the efficiency of the hybrid models. The results of our study on Bookan dam indicate that the DAE-SVR model, in comparison with the PCA-MLR, RF-MLR, DAE-MLR, PCA-SVR hybrid models, provides a significant improvement in the runoff prediction accuracy and the reduction of its uncertainty. DAE-SVR and RF-SVR have the highest values in NSE (0.93 and 0.87, respectively), and the lowest values in RMSE (33.85 and 43.68 MCM, respectively) for the first forecast horizon. The uncertainty analysis of the streamflow prediction shows that DAE-SVR has the best performance at the 95% confidence level due to its P-factor and R-factor values (0.9 and 0.64, respectively). Furthermore, the DAE-SVR performance is likewise better than other hybrid models in the two, three, and four months forecast horizons.

关键词： Long-term streamflow prediction Dimension reduction approach Variable selection approach Random Forest deep auto-encoder Support vector regression

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A novel deep auto-encoder considering energy and label constraints for categorization

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 176卷 114936-114936页

作者： Song, Wei Zhang, Yuxuan Park, Soon Cheol Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Jiangsu Prov Engn Lab Pattern Recognit & Computat Wuxi 214122 Jiangsu Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230026 Anhui Peoples R China Jeonbuk Natl Univ Dept Comp Sci Jeonju 561756 Jeonbuk South Korea

deep auto-encoder (DAE) is one of the representative deep learning algorithms for feature extraction. However, it often shows relatively poor generalization performance to express data without considering the probability distribution of data. Additionally, it cannot be directly applied to classification, because label information is ignored in DAE to judge the given categories. To tackle these issues, in this paper, we propose an energy and label constrained DAE (ELDAE) by integrating energy and label constraints to improve the feature extraction ability of network for classification. Specifically, as the probability distribution for fitting data can be reflected by energy of a network, the energy constraint is designed in this study to improve the probability of ELDAE for fitting data, and make a better expression to data. Moreover, the label constraint is integrated in ELDAE using label information to describe categorization rule, contributing to enhancing the accuracy of classification. We first give the complexity analysis of ELDAE, which is crucial to the property of speed. To exhibit the performance of the proposed ELDAE, we perform comprehensive experiments on benchmark USPS and MNIST datasets, and parameter sensitivity analysis is then provided to investigate the effects of three key parameters including the balance coefficients of weight decay, energy constraint and label constraint. In addition, we compare ELDAE with six state-of-the-art algorithms including auto-encoder (AE), Sparse AE (SAE), deep AE (DAE), deep Belief Network (DBN), Noisy AE (NAE) and Semi-supervised AE (SSAE). The comparative experimental results demonstrate that ELDAE performs better than the other six competitors in terms of classification accuracy, and keeps the same order of magnitude in terms of training time and testing time.

关键词： deep learning deep auto-encoder Semi-supervised learning Energy constraint Categorization

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Turn-to-turn short circuit of motor stator fault diagnosis in continuous state based on deep auto-encoder

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IET ELECTRIC POWER APPLICATIONS 2019年第10期13卷 1598-1606页

作者： Wang, Botao Shen, Chuanwen Xu, Kexing Zheng, Tingting Xi An Jiao Tong Univ Sch Elect Engn Xianning West Rd Xian Shaanxi Peoples R China

In this study, a turn-to-turn short circuit of motor stator fault diagnosis system based on deep auto-encoder and soft-max classifier is proposed. It is also considered in the proposed system that an unbalanced power supply has similar current characteristics and is possible to be misidentified. The determination of neural network parameters and their effect in training are given systematically, which improves the performance compared with traditional auto-encoder. The proposed fault diagnosis system can map the preprocessed three-phase motor current signals to a two-dimensional vector, corresponding to certain area of the feature plane to identify different fault types. The proposed system is verified via simulation experiment using MATLAB and physical experiment using a motor in laboratory. The conclusion illustrates that when signals originating from discrete motor speed, load, and fault severity are used in training, the fault diagnosis is still effective in continuous state. The accuracy is 89% for speed verification and 95% for load verification in simulation and is above 99% in the physical experiment.

关键词： fault diagnosis stators neural nets learning (artificial intelligence) induction motors turn-to-turn short circuit continuous state deep auto-encoder motor stator fault diagnosis system soft-max classifier similar current characteristics traditional auto-encoder three-phase motor current signals different fault types discrete motor speed fault severity

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Performance degradation assessment of slewing bearings based on deep auto-encoder and optimized particle filtering

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JOURNAL OF VIBRATION AND CONTROL 2019年第17期25卷 2380-2394页

作者： Pan, Yubin Hong, Rongjing Chen, Jie Wu, Weiwei Nanjing Tech Univ 30Puzhu Rd S Nanjing 211800 Jiangsu Peoples R China Yangzhou Univ Yangzhou Jiangsu Peoples R China

Due to the low speed and heavy load conditions of slewing bearings, extracting of effective features for fault diagnosis and prediction is difficult but crucial. Moreover, challenges such as large data volumes, unlabeled and multi-source bring more difficulties for advanced prognosis and health management methods. To solve these problems, a novel method for performance degradation assessment of bearings based on raw signals is proposed. In this methodology, a combination of deep auto-encoder (DAE) algorithm and particle filter algorithm is utilized for feature extraction and remaining useful life (RUL) prediction. First, the raw vibration signal is employed to train parameters of a restricted Boltzmann machine to build the DAE model. Through encoding and decoding multi-source data, root mean square error of reconstruction error between the raw signal and reconstructed signal is employed to detect incipient faults of slewing bearings. Then, degradation trend model is established by particle filtering to predict RUL of bearings. The effectiveness of proposed method is validated using simulated and experimental vibration signals. Results illustrate that proposed method can evaluate the performance degradation process and RUL of slewing bearings.

关键词： Slewing bearing deep auto-encoder feature extraction particle filter remaining useful life prediction

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A new framework using deep auto-encoder and energy spectral density for medical waveform data classification and processing

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BIOCYBERNETICS AND BIOMEDICAL ENGINEERING 2019年第1期39卷 148-159页

作者： Karim, Ahmad M. Guzel, Mehmet S. Tolun, Mehmet R. Kaya, Hilal Celebi, Fatih, V Aksaray Univ Elect & Elect Engn Aksaray Turkey Ankara Univ Dept Comp Engn TR-06830 Ankara Turkey Baskent Univ Dept Comp Engn Ankara Turkey AYBU Dept Comp Engn Ankara Turkey

This paper proposes a new framework for medical data processing which is essentially designed based on deep autoencoder and energy spectral density (ESD) concepts. The main novelty of this framework is to incorporate ESD function as feature extractor into a unique deep sparse auto-encoders (DSAEs) architecture. This allows the proposed architecture to extract more qualified features in a shorter computational time compared with the conventional frameworks. In order to validate the performance of the proposed framework, it has been tested with a number of comprehensive medical waveform datasets with varying dimensionality, namely, Epilepsy Serious Detection, SPECTF Classification and Diagnosis of Cardiac Arrhythmias. Overall, the ESD function speeds up the deep auto-encoder processing time and increases the overall accuracy of the results which are compared to several studies in the literature and a promising agreement is achieved. (C) 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.

关键词： Energy spectral density deep auto-encoder deep learning Medical waveform data process

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Towards Unknown Traffic Identification Using deep auto-encoder and Constrained Clustering 19th

Towards Unknown Traffic Identification Using Deep Auto-Encod...

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19th Annual International Conference on Computational Science (ICCS)

作者： Zhang, Yongzheng Zhao, Shuyuan Sang, Yafei Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9783030227340;9783030227333

Nowadays, network traffic identification, as the fundamental technique in the field of cybersecurity, suffers from a critical problem, namely "unknown traffic". The unknown traffic refers to network traffic generated by previously unknown applications ( i.e., zero-day applications) in a pre-constructed traffic classification system. The ability to divide the mixed unknown traffic into multiple clusters, each of which contains only one application traffic as far as possible, is the key to solve this problem. In this paper, we propose the DePCK to improve the clustering purity. There are two main innovations in our framework: (i) It learns to extract bottleneck features via deep auto-encoder from traffic statistical characteristics;(ii) It uses the flow correlation to guide the process of pairwise constrained k-means. To verify the effectiveness of our framework, we make contrast experiments on two real-world datasets. The experimental results show that the clustering purity rate of DePCK can exceed 94.81% on the ISP-data and 91.48% on the WIDE-data [1], which outperform the state-of-the-art methods: RTC [20], and k-means with log data [15].

关键词： Unknown traffic deep auto-encoder Bottleneck features Pairwise constrained k-means

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Spatial Fuzzy Clustering and deep auto-encoder for Unsupervised Change Detection in Synthetic Aperture Radar Images 38

Spatial Fuzzy Clustering and Deep Auto-encoder for Unsupervi...

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38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Li, Yangyang Zhou, Linhao Peng, Cheng Jiao, Licheng Xidian Univ Key Lab Intelligent Percept & Image Understanding Joint Int Res Lab Intelligent Percept & Computat Minist EducInt Res Ctr Intelligent Percept & Com Xian 710071 Shaanxi Peoples R China

ISBN: (纸本)9781538671504

Change detection in synthetic aperture radar (SAR) images is to detect the changes happening during a period of time in the same area, which has important application research value. In this paper, we propose a novel method based on spatial fuzzy clustering (SFCM) and deep auto-encoder for the change-detection of SAR. In this method, the difference image (DI) is generated by the log-ratio operator. Then, the spatial fuzzy clustering (SFCM) algorithm is used to analyze the DI. The spatial fuzzy clustering (SFCM) algorithm adds spatial information to the fuzzy cluster, which effectively reduces the influence of speckle noise. Finally, we choose appropriate samples to train the deep auto-encoder. Real data and theoretical analysis show the effectiveness and robustness of the proposed method.

关键词： change detection spatial fuzzy clustering deep auto-encoder synthetic aperture radar (SAR)

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