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Intelligent fault diagnosis of automobile main reducer based onstacked convolutional auto-encoder and parallel attention-based convolutional blocks

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APPLIED INTELLIGENCE 2025年第1期55卷 1-20页

作者： Ye, Qing Liu, Changhua Yangtze Univ Sch Comp Sci Jingzhou 430023 Peoples R China Yangtze Univ Gen Off Jingzhou 430023 Peoples R China

Fault diagnosis is an important subfield of prognostic and health management (PHM). Intelligent fault diagnosis based on deep learning is the most popular data-driven method of the present. However, current researches are prone to ignoring the strong noisy backgroundin real working conditions and cannot achieve excellent performance in actual application. As we all know, noise reduction and feature extraction are two vital aspects in mechanicalfault diagnosis. In this article, an intelligent diagnostic model based onimproved stacked convolutional auto-encoder (ISCAE) and parallel attention-based convolutional blocks (PACB) is proposed. ISCAE-based module is constructed to reduce the noise of raw signals and then PACB-based module can synchronouslyextract local spatial feature and global feature *** equalize the role of above-mentioned two modules which are serial in the proposed model, two modules are trained and optimized synchronously to simultaneously adjust the neural network weights. The capability and effectiveness of the model are evaluated using a dataset collected from real operating environment of main reducer. The comparative analysisresults show that the ISCAE-PACB-based model can reach the accuracy of 98.95% and is superior to existing models.

关键词： convolutional auto-encoder Neural network Attention-based Fault diagnosis Noise reduction

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convolutional auto-encoder-based Packet-Loss-and-Error-Resilient Data Collection Method for Underwater Sensors 13

Convolutional Auto-encoder-based Packet-Loss-and-Error-Resil...

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13th IEEE International Conference on Communications, Circuits and Systems (ICCCAS)

作者： Yang, Fan Chen, Huifang Xie, Lei Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Prov Key Lab Informat Proc Commun & Netw Hangzhou 310027 Peoples R China Zhejiang Univ Zhoushan Ocean Res Ctr Zhoushan 316021 Peoples R China

ISBN: (纸本)9798350386288;9798350386271

In recent years, the underwater acoustic sensor network (UASN) is emerging as an effective means for marine data collection. However, due to the limited bandwidth of acoustic channel, the limited constrained energy supply of underwater sensors and data redundancy, it is impossible to deliver all the raw data generated by underwater sensors. Hence, to compress time-redundant data at a sensor node is of great significance for data collection from underwater sensors. Moreover, the underwater acoustic transmission link is unreliable, the issue of data collection with certain packet error resilience should be resolved. In this paper, a packet-loss-and-error-resilient data collection method based on convolutional auto-encoder (CAE) is proposed to collect the time-series data from underwater sensors, named as the PLER-CAE data collection method. In the proposed method, the auto-encoder is used to reduce data redundancy of the time-series data. The encoder deployed at the collection end compresses the data for transmission through the underwater channel, and the decoder deployed at the receiving end reconstructs the original data. Moreover, the proposed method complements the retransmission mechanism and the error correction technique in order to enhance data reconstruction quality. Numerical results show that the proposed PLER-CAE data collection method is effective.

关键词： convolutional auto-encoder data collection Packet-loss-and-error-resilient method underwater

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convolutional auto-encoder Based Sky Image Prediction Model for Minutely Solar PV Power Forecasting

Convolutional Auto-encoder Based Sky Image Prediction Model ...

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IEEE-Industry-Applications-Society Annual Meeting

作者： Chai, Hua Zhen, Zhao Li, Kangping Wang, Fei Dehghanian, Payman Shafie-khah, Miadreza Catalao, Joao P. S. North China Elect Power Univ Dept Elect Engn Baoding 071003 Peoples R China George Washington Univ Dept Elect & Comp Engn Washington DC USA Univ Vaasa Sch Technol & Innovat Vaasa 65200 Finland Univ Porto Fac Engn P-4200465 Porto Portugal INESC TEC P-4200465 Porto Portugal

ISBN: (纸本)9781728171920

The precise minute time scale forecasting of an individual Photovoltaic power station output relies on accurate sky image prediction. To avoid the two deficiencies of traditional digital image processing technology (DIPT) in predicting sky images: relatively limited input spatiotemporal information and linear extrapolation of images, convolutional auto-encoder (CAE) based sky image prediction models are proposed according to the spatiotemporal feature extraction ability of 2D and 3D convolutional layers. To verify the effectiveness of the proposed models, two typical DIPT methods, including particle image velocimetry (PIV) and Fourier phase correlation theory (FPCT) are introduced to build the benchmark models. The results show that the proposed models outperform the benchmark models under different scenarios.

关键词： Solar PV power forecasting minute time scale sky image convolutional auto-encoder spatiotemporal feature

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Multimodal Emotion Recognition Method Based on convolutional auto-encoder

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2019年第1期12卷 351-358页

作者： Zhou, Jian Wei, Xianwei Cheng, Chunling Yang, Qidong Li, Qun Nanjing Univ Posts & Telecommun Coll Comp 66 Xinmofan Rd Nanjing 210003 Jiangsu Peoples R China Jiangsu High Technol Res Key Lab Wireless Sensor 66 Xinmofan Rd Nanjing 210003 Jiangsu Peoples R China

Emotion recognition is of great significance to computational intelligence systems. In order to improve the accuracy of emotion recognition, electroencephalogram (EEG) signals and external physiological (EP) signals are adopted due to their perfect performance in reflecting the slight variations of emotions, wherein EEG signals consist of multiple channels signals and EP signals consist of multiple types of signals. In this paper, a multimodal emotion recognition method based on convolutional auto-encoder (CAE) is proposed. Firstly, a CAE is designed to obtain the fusion features of multichannel EEG signals and multitype EP signals. Secondly, a fully connected neural network classifier is constructed to achieve emotion recognition. Finally, experiment results show that the proposed method can improve the accuracy of emotion recognition obviously compared with other similar methods. (c) 2019 The Authors. Published by Atlantis Press SARL.

关键词： Emotion recognition convolutional auto-encoder Fully connected neural network EEG signals EP signals

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Stacked convolutional auto-encoder representations with spatial attention for efficient diabetic retinopathy diagnosis

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第22期81卷 32033-32056页

作者： Bodapati, Jyostna Devi Vignans Fdn Sci Technol & Res Amaravati 522213 Andhra Pradesh India Indian Inst Technol IIT Madras Chennai 600036 Tamil Nadu India

Recently, the attention mechanism has been effectively implemented in convolutional neural networks to boost performance of several computer vision tasks. Recognizing the potential of the attention mechanism in medical imaging, we present an end-to-end-trainable spatial Attention based convolutional neural network architecture for recognizing diabetic retinopathy severity level. Initially spatial representations of the fundus images are projected to reduced space using a stacked convolutional auto-encoder. In order to enhance discrimination in reduced space, the auto-encoder is jointly trained with the classifier in an end-to-end manner. Attention mechanism introduced in the classification module ensures high emphasis on lesion regions compared to the non-lesion regions. The proposed model is evaluated on two benchmark datasets, and the experimental outcomes indicate that joint training favors stability and complements the learned representations when used along with attention. The proposed approach outperforms several existing models by achieving an accuracy of 84.17%, 63.24% respectively on Kaggle APTOS19 and IDRiD datasets. In addition, ablation studies validate our contributions and the behavior of the proposed model on both the datasets.

关键词： Diabetic retinopathy convolutional neural networks Spatial attention convolutional auto-encoder Neural induced support vector machine Attention Deep features

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One-dimensional convolutional auto-encoder-based feature learning for fault diagnosis of multivariate processes

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JOURNAL OF PROCESS CONTROL 2020年 87卷 54-67页

作者： Chen, Shumei Yu, Jianbo Wang, Shijin Tongji Univ Sch Mech Engn Shanghai 201804 Peoples R China Tongji Univ Sch Econ & Management Shanghai 200082 Peoples R China

Noise and high-dimension of process signals decrease effectiveness of those regular fault detection and diagnosis models in multivariate processes. Deep learning technique shows very excellent performance in high-level feature learning from image and visual data. However, the large labeled data are required for deep neural networks (DNNs) with supervised learning like convolutional neural network (CNN), which increases the time cost of model construction significantly. A new DNN model, one-dimensional convolutional auto-encoder (1D-CAE) is proposed for fault detection and diagnosis of multivariate processes in this paper. 1D-CAE is utilized to learn hierarchical feature representations through noise reduction of high-dimensional process signals. auto-encoder integrated with convolutional kernels and pooling units allows feature extraction to be particularly effective, which is of great importance for fault detection and diagnosis in multivariate processes. The comparison between 1D-CAE and other typical DNNs illustrates effectiveness of 1D-CAE for fault detection and diagnosis on Tennessee Eastman Process and Fed-batch fermentation penicillin process. The proposed method provides an effective platform for deep-learningbased process fault detection and diagnosis of multivariate processes. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Multivariate process Fault diagnosis convolutional auto-encoder Feature learning Tennessee Eastman Process Fed-batch fermentation penicillin process

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Memristive Circuit Design of Quantized convolutional auto-encoder

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2023年第4期7卷 1301-1313页

作者： Zhang, Yaozhong Wang, Xiaoping Yang, Chao Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automation Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Guangxi Univ Sci & Technol Sch Comp Sci & Technol Liuzhou 545006 Peoples R China

This work proposes a memristor-based quantized convolutional auto-encoder (MQCAE) and applies it in an image denoising application. The pulse width or amplitude is gradually tuned by incremental steps in current memristive programming methods, which can be extremely time-consuming. In this work, a multi-level programming method without incremental steps is proposed. MQCAE is composed of five kinds of network layers: convolution layers, deconvolution layers, activation function layers, batch normalization layers and max-pooling layers. We design a memristive circuit for realizing convolution and deconvolution with different kernel parameters. The proposed circuit generates one output feature row every cycle. Analog data buffers are designed to store the intermediate data among network layers. In addition, a reconfigurable analog circuit for realizing activation functions and batch normalization is presented. By using analog temp modules, convolution layers and max-pooling layers are able to compute simultaneously. We construct the MQCAE and introduce the pipeline technique among network layers based on the circuit modules. As a result, MQCAE is able to process one FashionMNIST image in 36 cycles with clock frequency 20 kHz. Finally, we verify the effectiveness of MQCAE in an image denoising task. The results show that the denoising performance of proposed scheme is close to software model while the processing speed is faster.

关键词： Acceleration memristor crossbar array convolutional auto-encoder image denoising quantization

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Adaptive multiscale and dual subnet convolutional auto-encoder for intermittent fault detection of analog circuits in noise environment

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ISA TRANSACTIONS 2023年 136卷 428-441页

作者： Fang, Xiaoyu Qu, Jianfeng Chai, Yi Liu, Bowen Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Sec Chongqing 400044 Peoples R China Chongqing Univ Coll Automat Chongqing 400044 Peoples R China

In avionics , industrial electronic systems, analog circuits are one of the most commonly used components. Intermittent faults (IFs) are a no fault found (NFF) state in analog circuits that are difficult to detect. In addition, the presence of noise may obscure critical information about the state of the circuit. Considering these challenges, this paper proposes an adaptive multiscale and dual subnet convolutional auto-encoder (AMDSCAE) to detect IFs. The proposed method can adaptively assign different attention to each scale and then fuse the multiscale information, which has better noise robustness. Then, the fault reconstruction error is amplified by the dual subnet structure to enhance the IF detection ability and find weaker faults. Considering the difficulty of obtaining fault sample labels, the proposed model requires only fault-free samples in the training process. In three typical analog filter circuit experiments, AMDSCAE has better noise immunity and can detect weaker IFs.(c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： convolutional auto-encoder Adaptive multiscale learning Dual subnet structure Intermittent fault detection Analog circuits

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Steady-State Process Fault Detection for Liquid Rocket Engines Based on convolutional auto-encoder and One-Class Support Vector Machine

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IEEE ACCESS 2020年 8卷 3144-3158页

作者： Zhu, Xiaobin Cheng, Yuqiang Wu, Jianjun Hu, Runsheng Cui, Xing Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Peoples R China

Liquid rocket engines (LREs) are the main propulsive devices of launch vehicles. Due to the complex structures and extreme working environments, LREs are also the components prone to failure. It is of great engineering significance to develop fault detection technologies which can detect fault symptoms in time and provide criteria for further fault diagnosis and control measures to avoid serious consequences during both the ground tests and flight missions. This paper presents a novel fault detection method based on convolutional auto-encoder (CAE) and one-class support vector machine (OCSVM) for the steady-state process of LREs. We train the CAEs by normal ground hot-fire test data of a certain type of large LRE for automatic feature extraction. Then the obtained features are used to train the OCSVMs to accomplish the fault detection task. The results demonstrate that the proposed method outperforms traditional redline system (RS), adaptive threshold algorithm (ATA), and back-propagation neural network (BPNN). We also study the effect of sample sizes and domain knowledge on the performance of the proposed method. The results suggest that appropriate measures that enrich the effective information content in the training data, such as increasing sample size and introducing domain knowledge, can further improve the performance of the proposed fault detection method.

关键词： convolutional auto-encoder one-class support vector machine liquid rocket engine steady-state process fault detection

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FRWCAE: joint faster-RCNN and Wasserstein convolutional auto-encoder for instance retrieval

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APPLIED INTELLIGENCE 2020年第7期50卷 2208-2221页

作者： Zhang, Yi-yang Feng, Yong Liu, Da-jiang Shang, Jia-xing Qiang, Bao-hua Chongqing Univ Coll Comp Sci Chongqing 400030 Peoples R China Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing 400030 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin 541004 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Optoelect Informat Proc Guilin 541004 Peoples R China

Based on the powerful feature extraction capability of deep convolutional neural networks, image-level retrieval methods have achieved superior performance compared to the hand-crafted features and indexing algorithms. However, people tend to focus on foreground objects of interest in images. Locating objects accurately and using object-level features for retrieval become the essential tasks of instance search. In this work, we propose a novel instance retrieval method FRWACE, which combines the Faster R-CNN framework for object-level feature extraction with a brand-new Wasserstein convolutional auto-encoder for dimensionality reduction. In addition, we propose a considerate category-first spatial re-rank strategy to improve instance-level retrieval accuracy. Extensive experiments on four large datasets Oxford 5K, Paris 6K, Oxford 105K and Paris 106K show that our approach has achieved significant performance compared to the state-of-the-arts.

关键词： Instance-level retrieval convolutional auto-encoder Wasserstein distance Dimensionality reduction

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