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Novel sparse auto-encoder framework with pseudo-labeled reinforcement for cross domain fault diagnosis with imbalanced samples

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第1期36卷

作者： Zhang, Faye Liu, Fuzheng Liu, Minghui Zhang, Yilan Jiang, Mingshun Sui, Qingmei Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China

By applying diagnostic expertise from one area to another that is closely related, transfer fault diagnosis is an efficient strategy for guaranteeing the secure and dependable operation of mechanical equipment. However, the majority of rotating machinery monitoring data are gathered in industrial applications under normal operating settings, which leads to an imbalance between positive and negative samples. Consequently, performing high-precision fault diagnosis with imbalanced data and different working conditions becomes challenging due to the escalating difficulty and cost of acquiring labeled fault samples. For cross domain fault diagnosis, an enhanced deep transfer sparse auto-encoder (SAE) framework is provided. This approach leverages a SAE to delve deeper into fault features within imbalanced data and reconstruct the samples accordingly. Furthermore, the research proposes a feature domain penalty term to facilitate cross-domain training by aligning the distribution of the source and target domain data which can reduce data distribution deviation. A pseudo-labeled reinforcement training method is presented to further improve cross-domain classification accuracy with imbalanced samples. Extensive experiments were conducted on two datasets to assess the proposed method. The results were compared with other algorithms, demonstrating the effectiveness and superiority of the proposed approach.

关键词： pseudo-labeled-reinforcement training rolling bearing fault diagnosis sparse auto-encoder transfer learning.

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sparse auto-encoder based feature learning for human body detection in depth image

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SIGNAL PROCESSING 2015年 112卷 43-52页

作者： Su, Song-Zhi Liu, Zhi-Hui Xu, Su-Ping Li, Shao-Zi Ji, Rongrong Xiamen Univ Sch Informat Sci & Technol Xiamen 361005 Peoples R China Xiamen Univ Fujian Key Lab Brain Like Intelligent Syst Xiamen 361005 Peoples R China

Human body detection in depth image is an active research topic in computer vision. But depth feature extraction is still an open problem. In this paper, a novel feature learning method based on sparse auto-encoder (SAE) is proposed for human body detection in depth image. The proposed learning based feature enables capturing the intrinsic human body structure. To further reduce the computation cost of SAE, both convolution neural network and pooling are introduced to reduce the training complexity. In addition, upon learning SAE based depth feature, we further pursuit the detector efficiency. A beyond sliding window localization strategy is proposed based on the fact that the depth values of object surface are almost the same. The proposed localization strategy first uses the histogram of depth to generate candidate detection window center, and then exploits the relationship between human body height and depth to determine the detection window size. Thus, it can avoid the time-consuming sliding window search, and further enables fast human body localization. Experiments on SZU Depth Pedestrian dataset verify the effectiveness of our proposed method. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Human detection Depth image Feature learning sparse auto-encoder

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sparse auto-encoder combined with kernel for network attack detection

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COMPUTER COMMUNICATIONS 2021年 173卷 14-20页

作者： Han, Xiaolu Liu, Yun Zhang, Zhenjiang Lu, Xin Li, Yang Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 CO Peoples R China Beijing Jiaotong Univ Sch Software Engn Beijing 100044 CO Peoples R China State Informat Ctr Postdoctoral Sci Workstat Beijing 100045 CO Peoples R China

In this study, we propose sparse auto-encoder combined with kernel for network attack detection for better network security. High-dimensional data seriously affects the accuracy and efficiency of network attack detection, leading to dimension disaster and model over fitting. To address this problem, we optimize the sparse auto-encoder with combined kernel to reconstruct the data features of network attack. Besides, we used the iterative method of adaptive genetic algorithm to optimize the objective function of sparse auto-encoder with combined kernel. The feature matrix after dimension reduction is obtained by sparse auto-encoder with combined kernel, which solves the dimensional reduction problem of nonlinear features and sparse features of network attack. The proposed model improves the efficiency of network attack detection. The simulation using experimental data based on botnet attack detection data set of the Internet of things(IOT) show that, compared with the traditional feature extraction algorithm and other deep learning feature extraction methods, the recognition rate based on sparse auto-encoder method with combined kernel for network attack detection can reach 98.68%, and the average dimension reduction time is 5.59 s, which depicts better recognition rate and computational efficiency.

关键词： Big data Feature extraction sparse auto-encoder Kernel function Network attack detection

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sparse auto-encoder with regularization method for health indicator construction and remaining useful life prediction of rolling bearing

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MEASUREMENT SCIENCE AND TECHNOLOGY 2020年第10期31卷

作者： She, Daoming Jia, Minping Pecht, Michael G. Southeast Univ Sch Mech Engn Nanjing 211189 Peoples R China Univ Maryland Ctr Adv Life Cycle Engn College Pk MD 20740 USA

Remaining useful life (RUL) prediction, allowing for mechanical predictive maintenance, reduces unplanned and expensive maintenance greatly. One of the great challenges of data-driven RUL prediction is to extract the features that describe the actual degradation process. This paper presents a health indicator (HI) construction method based on a sparse auto-encoder with regularization (SAEwR) model for rolling bearings. This paper includes two modules, HI construction and RUL prediction. In the stage of the HI construction, the original features are compressed and extracted by the SAEwR model. The extracted features are sorted according to the trendability, and the features with large trendability are selected to construct the HI by using minimum quantization error. In the module of RUL prediction, the maximum likelihood estimation method is used to estimate the parameters of the prediction model, and a particle filter-based RUL prediction with degradation model is proposed. The proposed method is benchmarked with variational auto-encoder, auto-encoder methods and principal component analysis. The data from PRONOSTIA and ABLT-1A platform support the value of our approach.

关键词： deep learning sparse auto-encoder regularization health indicator RUL prediction particle filter

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A fast reconstruction method of the dense point-cloud model for cultural heritage artifacts based on compressed sensing and sparse auto-encoder

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OPTICAL AND QUANTUM ELECTRONICS 2019年第10期51卷 1-16页

作者： Chen, Xiaoxue Zhou, Mingquan Zou, Linbo Fan, Li Hu, Jiabei Geng, Guohua Zhang, Haibo Northwest Univ Sch Informat Sci & Technol Xian Shaanxi Peoples R China

Since the point cloud data of cultural heritage artifacts obtained by the laser scanner is enormous and dense, these lead to a large quantity of network resource for storing, processing, and transmission. This paper provided a fast reconstruction method of the dense point cloud model for cultural relics based on sparse auto-encoder and compressed sensing. Firstly, the octree method based on the hash function was utilized to extract local features and remove redundant points. Secondly, the point cloud, which can be seen as the 3D geometric signal, is projected to the discrete Laplacian sparse basis via the point cloud adjacency matrix. Then, aiming at the bottleneck of slow recovery caused by tremendous scale of inverse problem based on compressed sensing theory, the sparse auto-encoder was applied to reduce the dimension and speed up the recovery. Finally, the OMP algorithm was applied to reconstruct 3D point cloud model based on the stochastic Gauss matrix. In order to test the performance of our methods, the 3D point cloud model of terracotta warriors and horses head were used. And the experimental results demonstrated that our approach can obviously accelerate the process of reconstruction of the dense point cloud model for the cultural heritage artifacts and ensure the recovery accuracy.

关键词： Cultural relics digital protection 3D dense point cloud Discrete Laplace operator sparse auto-encoder Compressed sensing

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Integrating Stacked sparse auto-encoder Into Matrix Factorization for Rating Prediction

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IEEE ACCESS 2021年 9卷 17641-17648页

作者： Zhang, Yihao Zhao, Chu Chen, Mian Yuan, Meng Chongqing Univ Technol Sch Artificial Intelligence Chongqing 400054 Peoples R China

Currently, collaborative filtering technology has been widely used in personalized recommender systems. The problem of data sparsity is a severe challenge faced by traditional collaborative filtering methods based on matrix factorization techniques. A lot of improved collaborative filtering methods have been proposed to alleviate the data sparsity problem;However, due to the sparsity of the user rating matrix, the latent factor learned by these improved methods may be not efficient. In this paper, we propose a novel recommendation algorithm named SSAERec by integrating stacked sparse auto-encoder into matrix factorization for rating prediction, which can learn effective representation from user-item rating matrix. Extensive experiments on three real-world datasets demonstrate the proposed method outperforms other baselines in the rating prediction task.

关键词： sparse matrices Collaboration Recommender systems Feature extraction Deep learning Task analysis Data models sparse auto-encoder collaborative filtering data sparsity rating prediction

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An unsupervised online monitoring method for tool wear using a sparse auto-encoder

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2020年第5-6期106卷 2493-2507页

作者： Dou, Jianming Xu, Chuangwen Jiao, Shengjie Li, Baodong Zhang, Jilin Xu, Xinxin Changan Univ Sch Construct Machinery Xian 710064 Peoples R China Lanzhou Inst Technol Prov Key Lab Green Cutting Technol & Applicat Gan Lanzhou 730050 Peoples R China

Tool wear, and its online monitoring, plays an important role in increasing productivity and improving product quality. We describe an unsupervised method to monitor the wear state of milling cutter by tracking an error sequence generated by reconstructing monitoring signals from a sparse auto-encoder (SAE). The monitoring signals consist of the force and vibration signals collected during the cutting process. We establish a well-structured SAE model, which can adaptively extract the characteristics of the signal and complete the training of the model without supervision of the empirical label and investigate the reconstruction performance of the model for cutting signal. On this basis, an automatic online tool wear state identification strategy is designed to monitor the milling process. The mean reconstruction error (MRE) sequence associated with tool wear is recorded in real time by reconstructing the next signal segment from the SAE model, which is trained and updated using the current signal segment. Monitoring criteria and thresholds are recommended to automate the identification of tool wear conditions based on the filtered MRE curve. Five experiments with two different milling environments are run to confirm the feasibility of tool wear monitoring using this method, and the results show that the method can be used to monitor tool wear conditions online under different milling conditions without being supervised by any empirical labels.

关键词： Tool wear Online monitoring sparse auto-encoder Unsupervised Force signal Vibration signal

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A classification method for moving targets in the wild based on microphone array and linear sparse auto-encoder

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NEUROCOMPUTING 2017年 241卷 28-37页

作者： Guo, Feng Huang, Jingchang Zhang, Xin You, Xing Zu, Xingshui Zhao, Qin Ding, Yuanyuan Liu, Huawei Li, Baoqing Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Shanghai Peoples R China Univ Chinese Acad Sci Beijing Peoples R China IBM Res China Lab Beijing Peoples R China

Moving target classification is an important issue in wireless sensors. The wild environment makes it a difficult problem for the acoustic signals. In this paper, a new classification method for moving targets in the wild is proposed based on microphone array and linear sparse auto-encoder (LSAE). First, the acoustic signals of moving targets are enhanced by delay-and-sum (DS) beamformer in the narrowband way for the simplicity. The enhancing effects are given a detailed analysis. Then, a spatial feature named noise likelihood (NLH) is presented to further resist the interferences and noise widely existing in the wild. The NLH has a good ability to distinguish between the moving targets and noise. Moreover, to make full use of both the signals beamformed and the NLH, a classification network combining the LSAE layers to learn their representations by self-taught learning and the softmax layer for the classification is built. Experiments show that not only the representations learned by the LSAE layers are robust and much distinguishable but also the proposed method achieves a much better classification performance in comparison with the baseline classifiers for moving targets in the wild. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Microphone array Delay-and-sum Noise likelihood sparse auto-encoder Wireless sensors

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Optimization of stamping process parameters based on an improved particle swarm optimization-genetic algorithm and sparse auto-encoder-back-propagation neural network model

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ENGINEERING OPTIMIZATION 2024年第2期56卷 252-273页

作者： Xie, Yanmin Liu, Cheng Li, Wei Du, Meiyu Feng, Kai Southwest Jiaotong Univ Sch Mech Engn Chengdu Peoples R China

Stamping is a very important manufacturing process. To optimize the process parameters, a hybrid surrogate model based on the back-propagation neural network and sparse auto-encoder is proposed and compared with classical surrogate models to verify its reliability. Furthermore, the hybrid improved particle swarm optimization-genetic algorithm, based on chaos theory, is proposed and compared with other algorithms. A double-C part is used as an engineering example to verify the proposed method. The Latin hypercube sampling method is used for sampling and the response value is obtained by autoForm simulation software. On this basis, the hybrid surrogate model is used to establish the mapping relationship between the forming quality of the double-C part and the stamping process parameters. The optimal stamping process parameters are obtained through the improved hybrid algorithm. The results demonstrate that the wrinkling of the optimized double-C part is significantly reduced and the forming quality is improved.

关键词： Stamping sparse auto-encoder neural network hybrid algorithm forming quality

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Deep neural network for halftone image classification based on sparse auto-encoder

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2016年 50卷 245-255页

作者： Zhang, Yan Zhang, Erhu Chen, Wanjun Xian Univ Technol Fac Mech & Precis Instrumental Engn Xian 710048 Peoples R China Armed Police Acad Dept Border Control & Immigrat Langfang 065000 Peoples R China Xian Univ Technol Fac Printing Packing Engn & Digital Media Technol Xian 710048 Peoples R China

To restore high quality continuous tone images from each class of halftone images, halftone image fine classification is the key problem. In this paper, a novel feature learning method is proposed for classifying 14 kinds of halftone images produced by the most well-known halftoning algorithms. This study employs the stacked sparse auto-encoders (SAE) trained with unsupervised learning for extracting features of halftone images, and then uses softmax regression with supervised learning for fine-tuning the deep neural network and classifying halftone images. In order to reduce the run-time of deep neural network and improve the image correct classification rate, we propose an effective patch extraction method for testing halftone images by measuring the mean and variance of local entropy in a patch. Halftone image classification is determined by the classification results of all effective patches inside an image via majority voting (MV). The experimental results demonstrate that our proposed method achieves an average Correct classification rate (ACCR) of over 99.44% for 14 kinds of halftone images on two public image sets. Compared with state-of-the-art LMS-Bayes and M-10-ML methods, the proposed SAE-MV method can distinguish the most categories of halftone images and achieve competitive ACCR, meanwhile, demonstrate better generalization performance. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Halftone image classification sparse auto-encoder Effective patch extraction Majority voting

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