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A method for rolling bearing life state recognition by combining health indicator and anti-noise deep residual shrinkage network

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2023年第1期45卷 1-14页

作者： Pan, Xuejiao Dong, Shaojiang Pei, Xuewu Song, Kai Chongqing Jiaotong Univ Chongqing 400074 Peoples R China Chongqing Changan Auto Co Ltd Chongqing 401120 Peoples R China

Recognizing the life states of rolling bearing accurately is not only convenient for experts to make maintenance strategy in advance, but also to avoid the direct economic loss caused by equipment downtime and damage. Aiming at the difficulty in assessing bearings degradation performance accurately and the low accuracy of recognizing bearing life states, a new method for recognizing the life state of rolling bearing combining health indicator and anti-noise deep residual shrinkage network (ADRSN) was proposed. Firstly, the envelope spectrum data samples can clearly reveal the fault characteristic frequencies, so multipliers of the signal were input into deep convolutional autoencoder to realize the automatic extraction and expression of bearing life state features. Secondly, the bearing health indicator is constructed in the low-dimensional feature space based on multidimensional scaling and Euclidean distance, which is used to assess the bearing degradation performance. After that, the bearing life states were classified and the state samples were labeled according to the bearing health indicator curves. Subsequently, to further enhance the noise resistance of the recognition model, dropblock regularization and dropout regularization were introduced into the deep residual shrinkage network to construct the ADRSN model, and the labeled samples were used to train the ADRSN to obtain the bearing life state recognition model. Finally, the proposed bearing health indicator and ADRSN model were applied to the bearing full-life experimental data. The results proved that the proposed health assessment method has more advantages than other conventional method. In addition, the ADRSN improves recognition accuracy by about 20% in noisy environment, which shows that ADRSN has more resistance and generalization in noisy environment.

关键词： convolutional autoencoder Health indicator Anti-noise deep residual shrinkage network Life state recognition

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Damage identification of seismic-isolated structure based on CAE network using vibration monitoring data

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ENGINEERING STRUCTURES 2023年第1期283卷

作者： Zhang, Minte Guo, Tong Zhu, Ruizhao Zong, Yueran Liu, Zhongxiang Xu, Weijie Southeast Univ Sch Civil Engn Nanjing 210096 Peoples R China

Seismic-isolated systems have been investigated and practiced globally to prevent the destruction of existing structures, and vibration-based structural health monitoring is important for maintaining the functionality of the isolation systems. Given the recent boom in computational science and machine learning algorithms, this paper deployed a structural health monitoring system for a rubber bearing-isolated gymnasium in an area with high seismic fortification intensity and introduced an unsupervised deep learning network named convolutional autoencoder (CAE) to identify damage from vibrations of the isolation layer. The CAE network is first trained by operational monitoring vibrations and tested by new data. The vibration assessment results indicate that the CAE network can accurately reconstruct daily data and effectively detect unexpected ground motion. Thereafter, to further test the network's damage identification and localization performance, an analytical finite element model of the gymnasium structure is established, and the undamaged and damaged datasets are generated for network verification and updating. The verification results demonstrate that the CAE network leads to reliable feature extraction power in global and local damage detection of the isolation layer. The proposed vibration assessment method is beneficial to building administrators in making accurate and timely decisions with the assistance of the score result provided by the CAE network.

关键词： Seismic-isolated structure Vibration assessment Structural health monitoring Damage identification convolutional autoencoder

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Cross-domain learning for pulmonary nodule detection using Gestalt principle of similarity

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SOFT COMPUTING 2023年 1-12页

作者： Susan, Seba Sethi, Dhaarna Arora, Kriti Delhi Technol Univ Dept Informat Technol Delhi 110042 India

Transfer learning is a trending concept in computer vision that is based on the transfer of knowledge between the source and target domains. In the distant domain problem addressed in this paper, the source and target domains are totally unrelated but have similar visual structures, thereby infusing explainability in transfer learning. We specifically focus on the pulmonary nodule detection problem in which the task is to distinguish the image patches that contain lung nodules from those that do not. The Gestalt principle of similarity states that the human mind tends to club visually similar structures together based on some object attributes such as shape and color. Though some structural differences may exist, these are imperceptible to the human eye since it focusses on "what it wants to see." This is the central idea behind our work that trains a deep convolutional neural network on commonly found natural scene images containing visual structures similar in appearance to cropped images of pulmonary nodules from computed tomography (CT) scans for the purpose of cancer diagnosis. Our transfer learning module comprises a deep convolutional autoencoder (CAE) that is pre-trained on a source domain comprising of a small and selective subset of only two objects: flowers and rivers that are selected by voting by human annotators to visually correlate with images of lung nodules and non-nodules, respectively. Our work thus presents a mechanism to make the learning process both human-interactive and explainable. Deep tuning our network on images from the benchmark Lung Image Database Consortium and Infectious Disease Research Institute (LIDC/IDRI) database yields higher classification scores as compared to the state-of-the-art. Cost-sensitive learning or data augmentation can be additionally used to further improve the performance of the proposed model.

关键词： Gestalt principle of similarity Lung nodule detection Deep learning Transfer learning convolutional autoencoder Medical image analysis

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Wafer map defect patterns classification based on a lightweight network and data augmentation

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 1029-1042页

作者： Naigong Yu Huaisheng Chen Qiao Xu Mohammad Mehedi Hasan Ouattara Sie Faculty of Information Technology Beijing University of TechnologyBeijingChina Beijing Key Laboratory of Computational Intelligence and Intelligent System BeijingChina Engineering Research Center of Digital Community Ministry of EducationBeijingChina College of Robotic UniversitéFélix Houphouët‐BoignyAbidjanCôte d'Ivoire

Accurately identifying defect patterns in wafer maps can help engineers find abnormal failure factors in production *** the wafer testing stage,deep learning methods are widely used in wafer defect detection due to their powerful feature extraction ***,most of the current wafer defect patterns classification models have high complexity and slow detection speed,which are difficult to apply in the actual wafer production *** addition,there is a data imbalance in the wafer dataset that seriously affects the training results of the *** reduce the complexity of the deep model without affecting the wafer feature expression,this paper adjusts the structure of the dense block in the PeleeNet network and proposes a lightweight network WM‐PeleeNet based on the PeleeNet *** addition,to reduce the impact of data imbalance on model training,this paper proposes a wafer data augmentation method based on a convolutional autoencoder by adding random Gaussian noise to the hidden *** method proposed in this paper has an average accuracy of 95.4%on the WM‐811K wafer dataset with only 173.643 KB of the parameters and 316.194 M of FLOPs,and takes only 22.99 s to detect 1000 wafer *** with the original PeleeNet network without optimization,the number of parameters and FLOPs are reduced by 92.68%and 58.85%,*** augmentation on the minority class wafer map improves the average classification accuracy by 1.8%on the WM‐811K *** the same time,the recognition accuracy of minority classes such as Scratch pattern and Donut pattern are significantly improved.

关键词： convolutional autoencoder lightweight network wafer defect detection

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CystNet: An AI driven model for PCOS detection using multilevel thresholding of ultrasound images

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SCIENTIFIC REPORTS 2024年第1期14卷 1-19页

作者： Moral, Poonam Mustafi, Debjani Mustafi, Abhijit Sahana, Sudip Kumar Birla Inst Technol Dept Comp Sci & Engn Ranchi 835215 India

Polycystic Ovary Syndrome (PCOS) is a widespread endocrinological dysfunction impacting women of reproductive age, categorized by excess androgens and a variety of associated syndromes, consisting of acne, alopecia, and hirsutism. It involves the presence of multiple immature follicles in the ovaries, which can disrupt normal ovulation and lead to hormonal imbalances and associated health complications. Routine diagnostic methods rely on manual interpretation of ultrasound (US) images and clinical assessments, which are time-consuming and prone to errors. Therefore, implementing an automated system is essential for streamlining the diagnostic process and enhancing accuracy. By automatically analyzing follicle characteristics and other relevant features, this research aims to facilitate timely intervention and reduce the burden on healthcare professionals. The present study proposes an advanced automated system for detecting and classifying PCOS from ultrasound images. Leveraging Artificial Intelligence (AI) based techniques, the system examines affected and unaffected cases to enhance diagnostic accuracy. The pre-processing of input images incorporates techniques such as image resizing, normalization, augmentation, Watershed technique, multilevel thresholding, etc. approaches for precise image segmentation. Feature extraction is facilitated by the proposed CystNet technique, followed by PCOS classification utilizing both fully connected layers with 5-fold cross-validation and traditional machine learning classifiers. The performance of the model is rigorously evaluated using a comprehensive range of metrics, incorporating AUC score, accuracy, specificity, precision, F1-score, recall, and loss, along with a detailed confusion matrix analysis. The model demonstrated a commendable accuracy of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \

关键词： Polycystic ovary syndrome Follicles Image classification convolutional autoencoder EfficientNet Image segmentation

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Compact representation and identification of important regions of metal microstructures using complex-step convolutional autoencoders

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MATERIALS & DESIGN 2022年 223卷

作者： Arumugam, Dharanidharan Kiran, Ravi North Dakota State Univ Dept Civil Construct & Environm Engn Fargo ND 58105 USA

In this study, we propose a complex-step convolutional autoencoder to identify the regions that are important in a metal microstructure for compact representation and secure sharing. Firstly, the architecture of a convolutional autoencoder is designed for the compact representation of microstructural images. The designed autoencoder achieved a high image compression ratio of 32 without loss of important information. Secondly, an in-home developed model agnostic sensitivity analysis using complex step derivative approximation is implemented on convolutional autoencoders to identify regions of the microstructure that are important for reconstruction. Finally, saliency maps that highlight the importance of pixels for reconstruction are generated for three grades of dual-phase structural steels. The saliency maps indicated secondary phase regions and grain boundaries are important for microstructure image reconstruction. The proposed approach produces more tenable saliency explanations compared to guided backpropagation and layer wise relevance propagation methods. The decoder part of the convolutional autoencoder can be used as a key that could be used to reconstruct the actual microstructure from encoded image information contributing to secure and efficient sharing of microstructure data. The proposed framework is generic and can be extended to identify important microstructural regions for other metals, composites, biomaterials, and material systems. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Interpretable AI ASTM A992 Saliency maps Pixel relevance convolutional autoencoder

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Unsupervised annual change detection from optical-SAR fused satellite image time-series using 3D-CAE

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2023年第5期44卷 1628-1642页

作者： Paul, Subir la Rosa, Laura Elena Cue Ghamisi, Pedram Gloaguen, Richard Helmholtz Inst Freiberg Resource Technol H Helmholtz Zent Dresden Rossendorf HZDR Freiberg Germany Inst Adv Res Artificial Intelligence IARAI Vienna Austria Helmholtz Inst Freiberg Resource Technol H Helmholtz Zent Dresden Rossendorf HZDR D-09599 Freiberg Germany

Satellite images are widely used for change detection in various applications, like, agricultural and forest cover area monitoring and management, urban planning, disaster management etc. which require prior information about the timeline of changes. However, it is very challenging to identify precise timeline of changes and to acquire corresponding labelled reference of changes. Considering this, convolutional Auto-Encoder (CAE)-based approaches has been proposed for unsupervised change detection from bi-temporal remote sensing images. However, these type of approaches require prior knowledge about the exact moment the change occurs in order to employ the bi-temporal images, one pre-change and one post-change. This work introduces the utilization of satellite image time-series (SITS) data in a Joint 3D CAE-based unsupervised approach to detect annual changes. Our study focuses on a mining area where the changes mostly occurs due to tailings, waste and ore deposit, mining pit, mining residue, and so on. Here, we have explored Synthetic Aperture Radar (SAR) (i.e. Sentinel-1) data along with optical (i.e. Sentinel-2) data, since optical data alone was not able to detect all the changes. The recall of detected change areas during 2016-2018 has improved by about 16% with the use of combination of Sentinel-1&2 data as compared to utilization of only Sentinel-2 images.

关键词： convolutional autoencoder optical-SAR fusion satellite image time-series unsupervised change detection

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Improving extreme learning machine model using deep learning feature extraction and grey wolf optimizer: Application to image classification

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2024年第1期18卷 457-483页

作者： Ali, Selma Kali Boughaci, Dalila Univ Sci & Technol Houari Boumediene USTHB Dept Artificial Intelligence Algiers Algeria

The Extreme Learning Machine (ELM) is a highly efficient model for real-time network retraining due to its fast learning speed, unlike traditional machine learning methods. However, the performance of ELM can be negatively impacted by the random initialization of weights and biases. Moreover, poor input feature quality can further degrade performance, particularly with complex visual data. To overcome these issues, this paper proposes optimizing the input features as well as the initial weights and biases. We combine both convolutional Neural Network (CNN) and convolutional autoencoder (CAE) extracted features to optimize the quality of the input features. And we use our hybrid Grey Wolf Optimizer-Multi-Verse Optimizer (GWO-MVO) metaheuristic for initializing weights and biases by applying four fitness functions based on: the norm of the output weights, the error rate on the training set, and the error rate on the validation set. Our method is evaluated on image classification tasks using two benchmark datasets: CIFAR-10 and CIFAR-100. Since image quality may vary in real-world applications, we trained and tested our models on the dataset's original and noisy versions. The results demonstrate that our method provides a robust and efficient alternative for image classification tasks, offering improved accuracy and reduced overfitting.

关键词： Extreme learning machine grey wolf optimizer feature extraction convolutional neural network convolutional autoencoder image classification

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Time-staged photoelastic image prediction using multi-stage convolutional autoencoders

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年 116卷

作者： Lee, Hyunsoo An, Heungjo Lee, Dong-Wook Kumoh Natl Inst Technol Sch Ind Engn Gumi South Korea Technol Innovat Inst Adv Mat Res Ctr Abu Dhabi U Arab Emirates

Photoelasticity has been widely utilized for the analysis of changes in the properties of micro-sized objects under variational forces. In machine-learning-based predictive maintenance, failure prediction is realized using sensor data and system signals. However, predictive maintenance using photoelasticity and machine learning is yet to be reported because of the difficulty in obtaining time-staged photoelastic images. The prediction of a future photoelastic image using past photoelastic images may facilitate near-future failure identification so that timely preventive measures can be implemented for mechanical or structural components. Such prediction is a spatiotemporal problem, which requires considerable recurrent data for temporal relations and image-based handling of spatial information. In this study, we first generated datasets of synthetic time-staged photoelastic images. Next, several combinations of deep learning models, including an autoencoder, a recurrent neural network, and a convolutional neural network, were examined. Finally, this study proposes a novel multi-stage convolutional autoencoder model for failure prediction. The effectiveness of the proposed model in terms of loss and accuracy was verified through numerical tests. The results revealed that the proposed model is an effective prediction framework for image-based future prediction of micro/nanosized materials. Furthermore, the proposed model can be extended to other applications that require image-based spatiotemporal data.

关键词： Photoelasticity Time-staged image prediction Hybrid deep learning convolutional autoencoder

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A deep learning framework for realistic robot motion generation

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NEURAL COMPUTING & APPLICATIONS 2023年第32期35卷 23343-23356页

作者： Dong, Ran Chang, Qiong Ikuno, Soichiro Tokyo Univ Technol Sch Comp Sci Tokyo 1920982 Japan Tokyo Inst Technol Sch Comp Tokyo 1528550 Japan

Humanoid robots are being developed to play the role of personal assistants. With the development of artificial intelligence technology, humanoid robots are expected to perform many human tasks, such as housework, human care, and even medical treatment. However, robots cannot currently move flexibly like humans, which affects their fine motor skill performance. This is primarily because traditional robot control methods use manipulators that are difficult to articulate well. To solve this problem, we propose a nonlinear realistic robot motion generation method based on deep learning. Our method benefits from decomposing human motions into basic motions and realistic motions using the multivariate empirical mode decomposition and learning the biomechanical relationships between them by using an autoencoder generation network. The experimental results show that realistic motion features can be learned by the generation network and motion realism can be increased by adding the learned motions to the robots.

关键词： Realistic motion generation convolutional autoencoder Multivariate empirical mode decomposition Human in the loop

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