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Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using autoencoder Models

APPLIED SCIENCES-BASEL 2025年第5期15卷 2662-2662页

作者： Melo, Renato Finotti, Rafaelle Guedes, Antonio Goncalves, Vitor Meixedo, Andreia Ribeiro, Diogo Barbosa, Flavio Cury, Alexandre Univ Fed Juiz de Fora Grad Program Civil Engn BR-36036900 Juiz De Fora Brazil Univ Porto Fac Engn CONSTRUCT LESE P-4200465 Porto Portugal Polytech Porto Sch Engn CONSTRUCT LESE P-4200465 Porto Portugal

This study presents a comparative analysis of three autoencoder (AE) models-Variational autoencoder (VAE), sparse autoencoder (SAE), and Convolutional autoencoder (CAE)-to detect and quantify structural anomalies in railway vehicle wheels, such as polygonization. Vertical acceleration data from a virtual wayside monitoring system serve as input for training the AE models, which are coupled with Hotelling's T2 Control Charts to differentiate normal and abnormal railway component behaviors. The results indicate that the SAE-T2 model outperforms its counterparts, achieving 16.67% higher accuracy than the CAE-T2 model in identifying distinct structural conditions, although with a 35.78% higher computational cost. Conversely, the VAE-T2 model is outperformed in 100% of the analyzed scenarios when compared to SAE-T2 in identifying distinct structural conditions while also exhibiting a 21.97% higher average computational cost. Across all scenarios, the SAE-T2 methodology consistently provided better classifications of wheel damage, showing its capability to extract relevant features from dynamic signals for Structural Health Monitoring (SHM) applications. These findings highlight SAE's potential as an interesting tool for predictive maintenance, offering improved efficiency and safety in railway operations.

关键词： structural health monitoring railways damage detection out of roundness sparse autoencoder convolutional autoencoder variational autoencoder

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A multilevel recovery diagnosis model for rolling bearing faults from imbalanced and partially missing monitoring data

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第3期20卷 5223-5242页

作者： Yang, Jing Xie, Guo Yang, Yanxi Li, Qijun Yang, Cheng Tianshui Normal Univ Sch Mechatron & Automot Engn Tianshui 741000 Peoples R China Xian Univ Technol Sch Automat & Informat Engn Xian 710048 Peoples R China

As an indispensable part of large Computer Numerical Control machine tool, rolling bearing faults diagnosis is particularly important. However, due to the imbalanced distribution and partially missing of collected monitoring data, such diagnostic issue generally emerging in manufacturing industry is still hardly to be solved. Thus, a multilevel recovery diagnosis model for rolling bearing faults from imbalanced and partially missing monitoring data is formulated in this paper. Firstly, a regulable resampling plan is designed to handle the imbalanced distribution of data. Secondly, a multilevel recovery scheme is formed to deal with partially missing. Thirdly, an improved sparse autoencoder based multilevel recovery diagnosis model is built to identify the health status of rolling bearings. Finally, the diagnostic performance of the designed model is verified by artificial faults and practical faults tests, respectively.

关键词： regulable resampling multilevel recovery sparse autoencoder rolling bearing fault diagnosis

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Learning-Based Cuckoo Search Algorithm to Schedule a Flexible Job Shop With Sequencing Flexibility

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第10期53卷 6663-6675页

作者： Lin, ChengRan Cao, ZhengCai Zhou, MengChu Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Zhejiang Gongshang Univ Sch Informat & Elect Engn Hangzhou 310018 Peoples R China New Jersey Inst Technol Helen & John C Hartmann Dept Elect & Comp Engn Newark NJ 07102 USA

This work considers an extended version of flexible job-shop problem from a postprinting or semiconductor manufacturing environment, which needs a directed acyclic graph rather than a linear order to describe the precedences among operations. To obtain its reliable and high-quality schedule in a reasonable time, a learning-based cuckoo search (LCS) algorithm is presented. In it, cuckoo search is selected as an optimizer. To produce promising solutions in a high-dimensional solution space, a sparse autoencoder is introduced to compress a high-dimensional solution into an informative low-dimensional one. It extends the application area of autoencoder-embedded evolutionary optimization methods into combinational optimization by developing an improved one-hot encoding method. Then, in order to reveal the linkages among decision variables and enhance the explore ability of the proposed method, a factorization machine (FM) is used, for the first time, to capture the relevant and complementary features of population. Hence, a parallel framework involving three co-evolved subpopulations is constructed. The first one is an autoencoder embedded subpopulation, the second one is assisted by an FM, and the last one undergoes a regular iteration process. To balance the exploration and exploitation of the proposed framework and avoid unnecessary computation, a reinforcement learning algorithm is used to adaptively adjust the proportion of subpopulations and tune parameters of each subpopulation iteratively. Numerical simulations with benchmarks are performed to compare it with CPLEX, some classical heuristics, and several recently developed methods. The results shows that it well outperforms them.

关键词： Cuckoo search (CS) factorization machine (FM) job shop scheduling reinforcement learning (RL) sparse autoencoder

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An Effective sparse autoencoders based Deep Learning Framework for fMRI Scans Classification 22

An Effective Sparse Autoencoders based Deep Learning Framewo...

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22nd International Conference on Enterprise Information Systems (ICEIS)

作者： Mahmoud, Abeer M. Karamti, Hanen Alrowais, Fadwa Ain Shams Univ Fac Comp & Informat Sci Cairo Egypt Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Comp Sci Dept POB 84428 Riyadh Saudi Arabia Univ Sfax MIRACL Lab ISIMS BP 242 Sfax 3021 Tunisia

ISBN: (纸本)9789897584237

Deep Learning (DL) identifies features of medical scans automatically in a way very near to expert doctors and sometimes over beats in treatment procedures. In fact, it increases model generalization as it doesn't focus on low level features and reduces difficulties (eg: overfitting) of training high dimensional data. Therefore, DL becomes a prioritized choice in building most recent Computer-Aided Diagnosis (CAD) systems. From other prospective, Autism Spectrum Disorder (ASD) is a brain disorder characterized by social miscommunication and confusing repetitive behaviours. The accurate diagnosis of ASD through analysing brain scans of patients is considered a research challenge. Some appreciated efforts has been reported in literature, however the problem still needs enhancement and examination of different models. A multi-phase learning algorithm combining supervised and unsupervised approaches is proposed in this paper to classify brain scans of individuals as ASD or controlled patients (TC). First, unsupervised learning is adopted using two sparse autoencoders for feature extraction and refinement of optimal network weights using back-propagation error minimization. Then, third autoencoder act as a supervised classifier. The Autism Brain fMRI (ABIDE-I) dataset is used for evaluation and cross-validation is performed. The proposed model recorded effective and promising results compared to literatures.

关键词： sparse autoencoder Autism Medical Image Classification

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A random deep neural system for heartbeat classification

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EVOLVING SYSTEMS 2023年第1期14卷 37-48页

作者： Siouda, Roguia Nemissi, Mohamed Seridi, Hamid Univ 8 Mai 1945 Dept Comp Sci LabSTIC Lab PB 401 Guelma Algeria Univ 8 Mai 1945 Dept Elect & Telecommun LabSTIC Lab PB 401 Guelma Algeria

This paper introduces a heartbeat classification system that combines three types of neural networks: random neural networks, deep autoencoders and RBF neural networks. The aim is to make use of the advantages of these neural networks in order to introduce a model with simpler architecture than the state-of-the-art deep models. Indeed, the advantages of the three combined networks, briefly, are these: (i) autoencoders provide high level features without pre-processing;(ii) Random neural networks provide good generalisation and very fast training;(iii) RBF neural networks provide high coverage of the input space and allow using prior knowledge. On the other hand, two types of features are used: coded features (obtained from the autoencoder) and RR interval based-features. To evaluate the performance of the proposed system, we conduct experiments on the MIT-BIH arrhythmia dataset and we consider the recommendations of the association for the advancement of medical instrumentation, which defines five classes of interest. Furthermore, the experiments are based on an inter-patient paradigm and the obtained results are compared with some of the state-of-the-art methods.

关键词： ECG arrhythmia Radial basis function neural network Random vector functional link neural network sparse autoencoder Multi-class classification

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Heart disease risk prediction using deep learning techniques with feature augmentation

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第20期82卷 31759-31773页

作者： Teresa Garcia-Ordas, Maria Bayon-Gutierrez, Martin Benavides, Carmen Aveleira-Mata, Jose Alberto Benitez-Andrades, Jose Univ Leon Escuela Ingn Ind & Informat SECOMUCI Res Grp Campus Vegazana s-n Leon 24071 Leon Spain Univ Leon Dept Elect Syst & Automat Engn SALBIS Res Grp Campus Vegazana s-n Leon 24071 Leon Spain

Cardiovascular diseases state as one of the greatest risks of death for the general population. Late detection in heart diseases highly conditions the chances of survival for patients. Age, sex, cholesterol level, sugar level, heart rate, among other factors, are known to have an influence on life-threatening heart problems, but, due to the high amount of variables, it is often difficult for an expert to evaluate each patient taking this information into account. In this manuscript, the authors propose using deep learning methods, combined with feature augmentation techniques for evaluating whether patients are at risk of suffering cardiovascular disease. The results of the proposed methods outperform other state of the art methods by 4.4%, leading to a precision of a 90%, which presents a significant improvement, even more so when it comes to an affliction that affects a large population.

关键词： Deep learning sparse autoencoder Convolutional neural network Heart disease

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A novel adaptive fault diagnosis algorithm for multi-machine equipment: application in bearing and diesel engine

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2023年第3期22卷 1677-1707页

作者： Liu, Yangshuo Kang, Jianshe Bai, Yunjie Guo, Chiming Army Engn Univ PLA Shijiazhuang 050003 Hebei Peoples R China Chinese Peoples Liberat Army Unit 66029 Xilinguolemeng Peoples R China

This paper proposes an adaptive fault diagnosis algorithm based on vibration signals for fault diagnosis of bearings and diesel engines. First, the improved nonlinear gray wolf optimization algorithm (NGWO) is adopted to optimize the key parameter for variational mode decomposition (VMD) with the power spectral entropy as the fitness value. Meanwhile, adaptive noise reduction of the signal is realized. Then, sensitive fault features of bearings and diesel engines are selected through a feature sensitivity analysis on the vibration signals. Also, a single-layer sparse autoencoder is used to align the feature dimensions of each type of data to construct feature matrix samples. Subsequently, a deep neural network (DNN) consisting of a two-layer stacked sparse autoencoder (SSAE) and a Softmax classification layer is constructed to realize failure mode recognition. During the training process of DNN, a surrogate model formed by NGWO and a back propagation neural network is employed to optimize the hyperparameters of SSAE. Finally, to verify the effectiveness of the proposed fault diagnosis algorithm, fault diagnosis experiments are conducted on the fault data set of bearings and diesel engines. The diagnosis results show that the proposed method achieves high-precision fault diagnosis for bearings and diesel engines and performs stably for small samples.

关键词： Fault diagnosis condition monitoring model adaptability adaptive noise reduction feature sensitivity analysis feature alignment sparse autoencoder surrogate model hyperparameter optimization

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STACKED sparse autoencoder (SSAE) BASED FRAMEWORK FOR NUCLEI PATCH CLASSIFICATION ON BREAST CANCER HISTOPATHOLOGY 11

STACKED SPARSE AUTOENCODER (SSAE) BASED FRAMEWORK FOR NUCLEI...

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11th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Xu, Jun Xiang, Lei Hang, Renlong Wu, Jianzhong Nanjing Univ Informat Sci & Technol Nanjing 210044 Jiangsu Peoples R China Jiangsu Canc Hosp Nanjing 210000 Jiangsu Peoples R China

ISBN: (纸本)9781467319614

In this paper, a Stacked sparse autoencoder (SSAE) based framework is presented for nuclei classification on breast cancer histopathology. SSAE works very well in learning useful high-level feature for better representation of input raw data. To show the effectiveness of proposed framework, SSAE+Softmax is compared with conventional Softmax classifier, PCA+Softmax, and single layer sparse autoencoder (SAE)+Softmax in classifying the nuclei and non-nuclei patches extracted from breast cancer histopathology. The SSAE+Softmax for nuclei patch classification yields an accuracy of 83.7%, F1 score of 82%, and AUC of 0.8992, which outperform Softmax classifier, PCA+Softmax, and SAE+Softmax.

关键词： Deep learning sparse autoencoder Breast Cancer Histopathology

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SV-SAE: Layer-Wise Pruning for autoencoder Based on Link Contributions

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IEEE ACCESS 2025年 13卷 75666-75678页

作者： Rheey, Joohong Park, Hyunggon Ewha Womans Univ Dept Elect & Elect Engn Seoul 03760 South Korea

autoencoders are a type of deep neural network and are widely used for unsupervised learning, particularly in tasks that require feature extraction and dimensionality reduction. While most research focuses on compressing input data, less attention has been given to reducing the size and complexity of the autoencoder model itself, which is crucial for deployment on resource-constrained edge devices. This paper introduces a layer-wise pruning algorithm specifically for multilayer perceptron-based autoencoders. The resulting pruned model is referred to as a Shapley Value-based sparse autoencoder (SV-SAE). Using cooperative game theory, the proposed algorithm models the autoencoder as a coalition of interconnected units and links, where the Shapley value quantifies their individual contributions to overall performance. This enables the selective removal of less important components, achieving an optimal balance between sparsity and accuracy. Experimental results confirm that the SV-SAE reaches an accuracy of 99.25%, utilizing only 10% of the original links. Notably, the SV-SAE remains robust under high sparsity levels with minimal performance degradation, whereas other algorithms experience sharp declines as the pruning ratio increases. Designed for edge environments, the SV-SAE offers an interpretable framework for controlling layer-wise sparsity while preserving essential features in latent representations. The results highlight its potential for efficient deployment in resource-constrained scenarios, where model size and inference speed are critical factors.

关键词： autoencoders Computational modeling Accuracy Neurons Image coding Deep learning Inference algorithms Feature extraction Dimensionality reduction Convolutional neural networks Edge computing feature importance latent representation layer-wise pruning lightweight model Shapley value sparse autoencoder unstructured pruning

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sparse auto encoder driven support vector regression based deep learning model for predicting network intrusions

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2021年第4期14卷 2419-2429页

作者： Preethi, D. Khare, Neelu Vellore Inst Technol Sch Informat Technol & Engn Vellore Tamil Nadu India

The Network Intrusion Detection System (NIDS) assumes a prominent aspect in ensuring network security. It serves better than traditional network security mechanisms, such as firewall systems. The result of the NIDS indicates the enhanced and efficient performance of the algorithms. It is utilized to predict intrusions, and it also has better training times for the algorithms. In this paper, a capable deep learning model using sparse Auto Encoder (SAE) is proposed. It is a self-taught learning framework. Such a model is a competent unsupervised learning algorithm in reconstructing new feature representation;thus, it diminishes the dimensionality. The SAE requires minimum training time substantially and efficiently enhances the prediction accuracy of Support Vector Regression (SVR) related to attacks. The experiments are administered using the standard intrusion detection dataset NSL-KDD, and therefore, the implementations are performed using python and tensor flow. The proposed model's effectiveness is estimated with other models viz., the PCA-SVR and SVR models applying prediction metrics such as R2 score, Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), accuracy and also training time. Results validate that the proposed SAE-SVR model has accelerated the training time of SVR and has the edge over the other models weighed in terms of prediction metrics. The model improves the rate of prediction by bringing down the error rates and yields a pioneering research mechanism for predicting the intrusions.

关键词： Network intrusion detection Network security Deep learning sparse autoencoder Support vector regression NSL-KDD Tensor flow

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