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Probabilistic bearing fault diagnosis using Gaussian process with tailored feature extraction

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第3-4期119卷 2059-2076页

作者： Liang, Mingxuan Zhou, Kai China Jiliang Univ Coll Mech & Elect Engn Hangzhou 310018 Peoples R China Michigan Technol Univ Dept Mech Engn Engn Mech Houghton MI 49931 USA

Deep learning methods recently have gained growing interests and are extensively applied in the data-driven bearing fault diagnosis. However, current deep learning methods perform the bearing fault diagnosis in the form of deterministic classification, which overlook the uncertainties that inevitably exist in actual practice. To tackle this issue, in this research, we develop a probabilistic fault diagnosis framework that can account for the uncertainty effect in prediction, which bears practical significance. This framework uses the Gaussian process classifier (GPC) as the mainstay, which fundamentally is built upon the Bayesian inference. To establish the high-fidelity GPC, the tailored feature extraction method can be adaptively determined through the cross validation-based grid search upon a prespecified method pool consisting of various kernel principal component analysis (KPCA) methods and stacked autoencoder. This adaptive strategy can ensure the adequate GPC model training to accurately characterize the complex nonlinear relations between the data features and respective faults. Systematic case studies using the publicly accessible experimental rolling bearing dataset, i.e., CWRU bearing dataset are carried out to validate this new framework. The results clearly illustrate the unique capability of this framework in handling uncertainties. It is also found that this framework outperforms other well-established machine learning and deep learning models in terms of accuracy and robustness. Moreover, the sensor fusion that combines the spatial vibration measurements appears to be an effective technique to further enhance the fault diagnosis performance. By fully leveraging the probabilistic feature of the framework, the future research endeavor, such as the extended fault diagnosis using limited fault labels will be facilitated.

关键词： Rolling bearings Probabilistic fault diagnosis Gaussian process classifier (GPC) Kernel principal component analysis (KPCA) stacked autoencoder Sensor fusion

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Lightweight 3D Dense autoencoder Network for Hyperspectral Remote Sensing Image Classification

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SENSORS 2023年第20期23卷 8635-8635页

作者： Bai, Yang Sun, Xiyan Ji, Yuanfa Fu, Wentao Duan, Xiaoyu Guilin Univ Elect Technol Informat & Commun Schnool Guilin 541004 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Precis Nav Technol & Applicat Guilin 541004 Peoples R China Natl & Local Joint Engn Res Ctr Satellite Nav Posi Guilin 541004 Peoples R China

The lack of labeled training samples restricts the improvement of Hyperspectral Remote Sensing Image (HRSI) classification accuracy based on deep learning methods. In order to improve the HRSI classification accuracy when there are few training samples, a Lightweight 3D Dense autoencoder Network (L3DDAN) is proposed. Structurally, the L3DDAN is designed as a stacked autoencoder which consists of an encoder and a decoder. The encoder is a hybrid combination of 3D convolutional operations and 3D dense block for extracting deep features from raw data. The decoder composed of 3D deconvolution operations is designed to reconstruct data. The L3DDAN is trained by unsupervised learning without labeled samples and supervised learning with a small number of labeled samples, successively. The network composed of the fine-tuned encoder and trained classifier is used for classification tasks. The extensive comparative experiments on three benchmark HRSI datasets demonstrate that the proposed framework with fewer trainable parameters can maintain superior performance to the other eight state-of-the-art algorithms when there are only a few training samples. The proposed L3DDAN can be applied to HRSI classification tasks, such as vegetation classification. Future work mainly focuses on training time reduction and applications on more real-world datasets.

关键词： hyperspectral remote sensing image classification deep learning dense connection stacked autoencoder

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Face recognition algorithm based on stack denoising and self-encoding LBP

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JOURNAL OF INTELLIGENT SYSTEMS 2022年第1期31卷 501-510页

作者： Lu, Yanjing Khan, Mudassir Ansari, Mohd Dilshad Zhengzhou Tech Coll Zhengzhou 450121 Henan Peoples R China King Khalid Univ Coll Sci & Arts Tanumah Dept Comp Sci Abha Saudi Arabia CMR Coll Engn & Technol Hyderabad India

To optimize the weak robustness of traditional face recognition algorithms, the classification accuracy rate is not high, the operation speed is slower, so a face recognition algorithm based on local binary pattern (LBP) and stacked autoencoder (AE) is proposed. The advantage of LBP texture structure feature of the face image as the initial feature of sparse autoencoder (SAE) learning, use the unified mode LBP operator to extract the histogram of the blocked face image, connect to form the LBP features of the entire image. It is used as input of the stacked AE, feature extraction is done, realize the recognition and classification of face images. Experimental results show that the recognition rate of the algorithm LBP-SAE on the Yale database has achieved 99.05%, and it further shows that the algorithm has a higher recognition rate than the classic face recognition algorithm;it has strong robustness to light changes. Experimental results on the Olivetti Research Laboratory library shows that the developed method is more robust to light changes and has better recognition effects compared to traditional face recognition algorithms and standard stack AEs.

关键词： face recognition stacked autoencoder LBP operator denoising

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Encrypted Voice Traffic Fingerprinting: An Adaptive Network Traffic Feature Encoding Model

Encrypted Voice Traffic Fingerprinting: An Adaptive Network ...

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IEEE International Conference on Communications (IEEE ICC)

作者： Zhou, Tianci Zeng, Yong Chen, Yitao Liu, Zhihong Ma, Jianfeng Xidian Univ Sch Cyber Engn Xian Peoples R China WuHan Marine Commun Res Inst Wuhan Peoples R China

ISBN: (纸本)9781538674628

Recent studies show that the traffic fingerprints constructed by encrypted voice traffic features will uncover users' activities. Current researches have focused on using machine learning models to build traffic fingerprints, however, they pay less attention to the distribution of traffic data. We note that encrypted voice traffic data is bimodal distributed, which is different from other encrypted traffic following Gaussian distribution. It means that present encoding model may destroy the distribution characteristics of encrypted voice traffic data, leading to a decrease in classification accuracy. To ameliorate this issue, our study proposes the combination of Discrete Fourier Transform and stacked autoencoder as traffic feature encoding model. The former is able to map the encrypted voice traffic features from bimodal distribution to frequency-domain space while keeping the trend of its features. And the latter can further improve features utilization in classification model. Finally, we achieved 96.08% accuracy on Amazon Echo dataset and 99.53% accuracy on Google Home dataset, which get the SOTA(state of the art) results on both encrypted voice traffic datasets.

关键词： Encrypted traffic classification Discrete Fourier Transform stacked autoencoder Non-Gaussian distribution

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An Adaptive Soft Sensor Method based on Online Deep Evolving Fuzzy System for Industrial Process Data Streams 12

An Adaptive Soft Sensor Method based on Online Deep Evolving...

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IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)

作者： Gao, Yu Jin, Huaiping Wang, Bin Yang, Biao Yu, Wangyang Kunming Univ Sci & Technol Fac Informat Engn & Automat Dept Automat Kunming 650500 Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Artificial Intelligence Kunming 650500 Peoples R China Wuhan Maritime Commun Res Inst Wuhan 430223 Peoples R China

ISBN: (纸本)9798350321050

In recent years, deep learning techniques have been widely applied in soft sensor modeling. stacked autoencoder (SAE) networks are particularly effective at discovering complex data patterns due to their hierarchical structures. However, process data are typically generated as data streams, which poses a great challenge to capture the time-varying characteristics of the process for traditional soft sensor models based on SAE. Furthermore, the insufficiency of offline pre-training data further limits the feature representation capability of SAE. To address these problems, an online deep evolving fuzzy system (ODEFS) based adaptive soft sensor method for process data streams is proposed. In the offline modeling phase, quality-related stacked autoencoder (QSAE) is pre-trained as representation layer to mine quality-related feature representations, while an evolving fuzzy system with self-organization capability is built as the prediction layer. In the online implementation phase, the topology-preserving loss is added to the learning process of QSAE feature network to enable continuous learning of feature representations and alleviate the catastrophic forgetting problem. Meanwhile, the shallow EFS network handles concept drift in data patterns by self-adjusting the structure and parameters. The proposed ODEFS method can improve the feature representation capability of SAE in a data streaming environment and the ability to handle time-varying characteristics, thus ensuring better prediction accuracy. The effectiveness and superiority of the proposed method are verified on TE process.

关键词： Industrial process data streams Adaptive soft sensor Evolving fuzzy system stacked autoencoder Topology preserving loss

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A Convolutional Neural Network Model-Based Approach for Multi-Fault Diagnosis of Asynchronous Motors

A Convolutional Neural Network Model-Based Approach for Mult...

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IEEE/IAS Industrial and Commercial Power System Asia (IEEE I and CPS Asia)

作者： Shi, Jun Li, Jiangnan Wang, Tao Shenzhen Power Supply Bur Co Ltd Shenzhen Peoples R China CSG Elect Power Res Inst Guangzhou Peoples R China

ISBN: (纸本)9798350339345

In view of the low accuracy of fault diagnosis for asynchronous motors, this paper constructs an stacked fault diagnosis model based on stacked autoencoder and convolutional neural network. The model uses stacked autoencoder to reduce the dimensionality and noise of the asynchronous motor fault data, and inputs the processed data into the convolutional neural network for fault diagnosis. By removing the pooling layer in the traditional convolutional neural network and using LeakyRelu activation function, the robustness and generalization of the diagnosis model are further improved, and the accurate diagnosis of fault types is realized. The experimental results show that the proposed method can effectively improve the accuracy of multi-fault diagnosis for asynchronous motors, and has excellent performance.

关键词： convolutional neural network asynchronous motor fault diagnosis stacked autoencoder

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Data-feature-driven nonlinear process monitoring based on joint deep learning models with dual-scale

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INFORMATION SCIENCES 2022年 591卷 381-399页

作者： Yu, Jianbo Yan, Xuefeng East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China

The interactions among the gauged data in most exiting real-life cases are correlative inevitably given the complicated behavior of process systems, that is the observed input data should better be interpreted as generating from joint interaction of static and dynamic feature sources. Therefore, the traditional single static-based or dynamic-based methods will inevitably lose some of the important information features, which will result in unsatisfactory monitoring results. Motivated by this, we expect to divide the data into static and dynamic features to perform separate modeling. Hence, the kernel slow feature analysis method is proposed to achieve this purpose. The output from the KSFA model can be divided into static and dynamic sources according to the dynamic characteristics. Then, in order to extract the unsupervised dynamic features, a novel deep RNN-LSTM autoencoder model is developed, which includes three components: data conversion, deep model construction, and feature removal. Moreover, we use a stacked autoencoder for static sources to learn static features and construct high-order models for fault detection. Based on the well-built detection system formed by the joint deep models, dual-scale decision-making is integrated by the Bayesian inference method. Finally, the superiority of the proposal can be shown in two processes.(c) 2021 Elsevier Inc. All rights reserved.

关键词： Nonlinear process monitoring Static dynamic features sources Kernel slow feature analysis stacked autoencoder RNN-LSTM autoencoder

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Historical Document Image Segmentation Combining Deep Learning and Gabor Features 17th

Historical Document Image Segmentation Combining Deep Learni...

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17th International Conference on Document Analysis and Recognition (ICDAR)

作者： Mehri, Maroua Sellami, Akrem Tabbone, Salvatore Univ Sousse LATIS Lab Adv Technol & Intelligent Syst Ecole Natl Ingenieurs Sousse Sousse 4023 Tunisia Univ Lorraine IDMC Inst Sci Digital Management & Cognit Pole Herbert Simon 13 Rue Michel Ney F-54000 Nancy France Univ Lille CNRS UMR 9189 CRIStAL Campus SciBatiment ESPRITAve Henri Poincare F-59655 Villeneuve Dascq France Univ Lorraine LORIA CNRS UMR 7503 Campus Sci615 Rue Jardin Bot F-54506 Vandoeuvre Les Nancy France

ISBN: (纸本)9783031416842;9783031416859

Due to the idiosyncrasies of historical document images (HDI), growing attention over the last decades is being paid for proposing robust HDI analysis solutions. Many research studies have shown that Gabor filters are among the low-level descriptors that best characterize texture information in HDI. On the other side, deep neural networks (DNN) have been successfully used for HDI segmentation. As a consequence, we propose in this paper a HDI segmentation method that is based on combining Gabor features and DNN. The segmentation method focuses on classifying each document image pixel to either graphic, text or background. The novelty of the proposed method lies mainly in feeding a DNN with a Gabor filtered image (obtained by applying specific multichannel Gabor filters) instead of an original image as input. The proposed method is decomposed into three steps: a) filtered image generation using Gabor filters, b) feature learning with stacked autoencoder, and c) image segmentation with 2D U-Net. In order to evaluate its performance, experiments are conducted using two different datasets. The results are reported and compared with those of a recent state-of-the-art method.

关键词： Historical document image segmentation Gabor filters Deep neural networks stacked autoencoder 2D U-Net architecture

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Nonlinear Dynamic System Based on SAE-LDS Model for Fault Diagnosis 13

Nonlinear Dynamic System Based on SAE-LDS Model for Fault Di...

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13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Tang, Xiaochu Zhang, Yi Zhang, Xinmin Song, Zhihuan School of Automation Shenyang Aerospace University Liaoning Province Shenyang110136 China College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Zhejiang Province Hangzhou310027 China

ISBN: (纸本)9798350361674

Dynamic, uncertainty and nonlinearity are three very important characteristics of complex industrial process data. In this paper, a method of fault detection and classification based on stacked autoencoder-linear dynamic system (SAE-LDS) model is proposed. This method aims to simultaneously consider multiple characteristics of industrial process data for fault detection and classification. Firstly, the deep learning algorithm SAE is used to mine the nonlinear characteristic of process data. On this basis, the SAE model is embedded into the LDS model. The LDS model is used to further extract the dynamics and uncertainty, so as to establish a SAE-LDS model that can comprehensively consider multiple characteristics of data. Finally, the SAE-LDS model is applied to the TE process, compared with the traditional method for fault detection and fault classification. The experimental results show that SAE- LDS model has good effectiveness. © 2024 IEEE.

关键词： data-driven fault classification fault detection linear dynamic system stacked autoencoder

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stacked Denoising Extreme Learning Machine autoencoder Based on Graph Embedding for Feature Representation

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IEEE ACCESS 2019年 7卷 13433-13444页

作者： Ge, Hongwei Sun, Weiting Zhao, Mingde Yao, Yao Dalian Univ Technol Coll Comp Sci & Technol Dalian 116023 Peoples R China McGill Univ Sch Comp Sci Montreal PQ H3A 0E9 Canada

Extreme learning machine is characterized by less training parameters, fast training speed, and strong generalization ability. It has been applied to obtain feature representations from the complex data in the tasks of data clustering or classification. In this paper, a graph embedding-based denoising extreme learning machine autoencoder (GDELM-AE) is proposed for capturing the structure of the inputs. Specifically, in GDELM-AE, a graph embedding framework that contains an intrinsic graph and a penalty graph constructed by local Fisher discrimination analysis is integrated into the autoencoder. So, it can exploit both local structure and global structure information in extreme learning machine (ELM) spaces. Further, we propose a stacked graph embedded denoising (SGD)-ELM by stacking several GDELM-AEs. The experimental results on several benchmarks validate that GDELM-AE can obtain efficient and robust feature representation of original data;moreover, the stacked GDELM-AE can obtain high-level and noise-robust representations. The comparative results with the state-of-the-art algorithms indicate that the proposed algorithm can obtain better accuracy as well as faster training speed.

关键词： Extreme learning machine stacked autoencoder denoising graph embedding

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