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Quantum stacked autoencoder fault diagnosis model for bearing faults

INSIGHT 2023年第11期65卷 631-638页

作者： Yu, Tianyi Li, Shunming Lu, Jiantao Nanjing Univ Aeronaut & Astronaut NUAA Coll Energy & Power Engn Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Nanjing Peoples R China Nantong Inst Technol Sch Automot Engn Nantong 226002 Peoples R China

The use of neural network models to monitor bearing vibration signals can easily be affected by noise, which leads to a decrease in the model test accuracy. Therefore, the existence of noise problems increases the requirements for non-linear mapping capability and robustness of deep neural network (DNN) models. In order to deal with the noise problem, the concept of qubit neurons is introduced into a deep learning stacked autoencoder (SAE) model, and a quantum stacked autoencoder (QSAE) model based on qubits and quantum gates is proposed. The properties of SAE layer-by-layer coding and the arithmetic of qubit neurons are combined in the QSAE. The quantum state signal is taken as the input signal to the encoder and the coding activation function and coding weight matrix are redefined by quantum-controlled non-gates and quantum revolving gates, so that the quantum state signal can be coded layer by layer. Experimental results show that the QSAE can train and diagnose noisy experimental data and maintain high test accuracy in an anti-attack test. This shows that the QSAE has non-linear mapping capability and robustness.

关键词： stacked autoencoder quantum neural network non-linear mapping robustness bearing fault

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A Data-Driven Health Monitoring Method Using Multiobjective Optimization and stacked autoencoder Based Health Indicator

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第9期17卷 6379-6389页

作者： Chen, Zhiwen Guo, Rongjie Lin, Zhi Peng, Tao Peng, Xia Cent South Univ Sch Automat Changsha 410083 Peoples R China Peng Cheng Lab Shenzhen State Key Lab High Performance Complex Mfg Changsha 410083 Peoples R China Hong Kong Univ Sci & Technol Sch Engn Hong Kong Peoples R China

This article proposes a new data-driven health monitoring method, which uses multiobjective optimization and stacked autoencoder based health indicator. Specifically, the proposed method proposes an improved nondominated sorting genetic algorithm-II (NSGA-II) to perform multiobjective optimization on a large number of candidate features extracted from the sensor measurements. Then, a stacked autoencoder model is used to construct health indicators from the selected features. In the improved NSGA-II algorithm, the optimization goals of feature selection are defined as the minimum gap of health indicators between different states and the number of features. Comparisons between the proposed method and the state-of-the-art methods on simulation experiments show that the proposed method can accurately identify the status of the equipment and effectively limit the complexity of the diagnostic model.

关键词： Optimization Feature extraction Indexes Monitoring Sorting Sociology Statistics Data driven health monitoring multiobjective optimization stacked autoencoder

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A hybrid network intrusion detection using darwinian particle swarm optimization and stacked autoencoder hoeffding tree

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2021年第6期18卷 8024-8044页

作者： Seraphim, B. Ida Poovammal, E. Ramana, Kadiyala Kryvinska, Natalia Penchalaiah, N. SRM Inst Sci & Technol Dept Comp Sci & Engn Chennai Tamil Nadu India Annamacharya Inst Technol & Sci Dept Artificial Intelligence & Data Sci Rajampet India Comenius Univ Fac Management Informat Syst Dept Odbojarov 10 Bratislava 82005 25 Slovakia Annamacharya Inst Technol & Sci Dept CSE Rajampet India

Cybersecurity experts estimate that cyber-attack damage cost will rise tremendously. The massive utilization of the web raises stress over how to pass on electronic information safely. Usually, intruders try different attacks for getting sensitive information. An Intrusion Detection System (IDS) plays a crucial role in identifying the data and user deviations in an organization. In this paper, stream data mining is incorporated with an IDS to do a specific task. The task is to distinguish the important, covered up information successfully in less amount of time. The experiment focuses on improving the effectiveness of an IDS using the proposed stacked autoencoder Hoeffding Tree approach (SAE-HT) using Darwinian Particle Swarm Optimization (DPSO) for feature selection. The experiment is performed in NSL_KDD dataset the important features are obtained using DPSO and the classification is performed using proposed SAE-HT technique. The proposed technique achieves a higher accuracy of 97.7% when compared with all the other state-of-art techniques. It is observed that the proposed technique increases the accuracy and detection rate thus reducing the false alarm rate.

关键词： intrusion detection system (IDS) stream data mining stacked autoencoder DPSO Hoeffding tree feature selection

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Classification of data on stacked autoencoder using modified sigmoid activation function

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第1期44卷 1-18页

作者： Kumar, Arvind Sodhi, Sartaj Singh Comp Sci & Engn USICT GGSIPU Delhi India

A Neural Network is one of the techniques by which we classify data. In this paper, we have proposed an effectively stacked autoencoder with the help of a modified sigmoid activation function. We have made a two-layer stacked autoencoder with a modified sigmoid activation function. We have compared our autoencoder to the existing autoencoder technique. In the existing autoencoder technique, we generally use the logsigmoid activation function. But in multiple cases using this technique, we cannot achieve better results. In that case, we may use our technique for achieving better results. Our proposed autoencoder may achieve better results compared to this existing autoencoder technique. The reason behind this is that our modified sigmoid activation function gives more variations for different input values. We have tested our proposed autoencoder on the iris, glass, wine, ovarian, and digit image datasets for comparison propose. The existing autoencoder technique has achieved 96% accuracy on the iris, 91% accuracy on wine, 95.4% accuracy on ovarian, 96.3% accuracy on glass, and 98.7% accuracy on digit (image) dataset. Our proposed autoencoder has achieved 100% accuracy on the iris, wine, ovarian, and glass, and 99.4% accuracy on digit (image) datasets. For more verification of the effeteness of our proposed autoencoder, we have taken three more datasets. They are abalone, thyroid, and chemical datasets. Our proposed autoencoder has achieved 100% accuracy on the abalone and chemical, and 96% accuracy on thyroid datasets.

关键词： autoencoder sigmoid activation function logsigmoid neural network classification stacked autoencoder

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Distributed-ensemble stacked autoencoder model for nonlinear process monitoring

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INFORMATION SCIENCES 2021年 542卷 302-316页

作者： Li, Zhichao Tian, Li Jiang, Qingchao Yan, Xuefeng East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

Determining whether a fault occurs locally or globally is highly important for large-scale industrial processes involving multiple operating units. Moreover, the complex nonlinearity among process variables is a prominent feature of modern industries. This paper proposes a distributed-ensemble stacked autoencoder (DE-SAE) model based on deep learning technology for monitoring non-linear, large-scale, multi-unit processes. First, the deep features of the variables involved in each operating unit are extracted with the stacked autoencoder (SAE) to represent the essential structure of the unit. Two statistics are separately constructed using the deep features and the reconstruction error for detecting the faults in local units. Subsequently, the deep representations of the variables from each operating unit are modeled with the SAE to extract the global information for global monitoring. The proposed DE-SAE model uses deep learning techniques to solve the complex non-linear relationships in industrial processes, while considering their local and global information. Therefore, the method can explain the monitoring results better. Experimental results obtained from the numerical simulation and Tennessee-Eastman process confirm the feasibility and superiority of this method. (C) 2020 Elsevier Inc. All rights reserved.

关键词： stacked autoencoder Process monitoring Distributed ensemble Joint feature

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Optimized stacked autoencoder for IoT Enabled Financial Crisis Prediction Model

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Computers, Materials & Continua 2022年第4期71卷 1079-1094页

作者： Mesfer Al Duhayyim Hadeel Alsolai Fahd N.Al-Wesabi Nadhem Nemri Hany Mahgoub Anwer Mustafa Hilal Manar Ahmed Hamza Mohammed Rizwanullah Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recently,Financial Technology(FinTech)has received more attention among financial sectors and researchers to derive effective solutions for any financial institution or *** crisis prediction(FCP)is an essential topic in business sector that finds it useful to identify the financial condition of a financial *** the same time,the development of the internet of things(IoT)has altered the mode of human interaction with the physical *** IoT can be combined with the FCP model to examine the financial data from the users and perform decision making *** paper presents a novel multi-objective squirrel search optimization algorithm with stacked autoencoder(MOSSA-SAE)model for FCP in IoT *** MOSSA-SAE model encompasses different subprocesses namely preprocessing,class imbalance handling,parameter tuning,and ***,the MOSSA-SAE model allows the IoT devices such as smartphones,laptops,etc.,to collect the financial details of the users which are then transmitted to the cloud for further *** addition,SMOTE technique is employed to handle class imbalance *** goal of MOSSA in SMOTE is to determine the oversampling rate and area of nearest neighbors of ***,SAE model is utilized as a classification technique to determine the class label of the financial *** the same time,the MOSSA is applied to appropriately select the‘weights’and‘bias’values of the *** extensive experimental validation process is performed on the benchmark financial dataset and the results are examined under distinct *** experimental values ensured the superior performance of the MOSSA-SAE model on the applied dataset.

关键词： Financial data financial crisis prediction class imbalance problem internet of things stacked autoencoder

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A Novel Symmetric stacked autoencoder for Adversarial Domain Adaptation Under Variable Speed

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IEEE ACCESS 2022年 10卷 24678-24689页

作者： Li, Ranran Li, Shunming Xu, Kun Li, Xianglian Lu, Jiantao Zeng, Mengjie Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Nanjing 210016 Peoples R China

At present, most of the fault diagnosis methods with extensive research and good diagnostic effect are based on the premise that the sample distribution is consistent. However, in reality, the sample distribution of rotating machinery is inconsistent due to variable working conditions, and most of the fault diagnosis algorithms have poor diagnostic effects or even invalid. To dispose the above problems, a novel symmetric stacked autoencoder (NSSAE) for adversarial domain adaptation is proposed. Firstly, the symmetric stacked autoencoder network with shared weights is used as the feature extractor to extract features which can better express the original signal. Secondly, adding domain discriminator that constituting adversarial with feature extractor to enhance the ability of feature extractor to extract domain invariant features, thus confusing the domain discriminator and making it unable to correctly distinguish the features of the two domains. Finally, to assist the adversarial training, the maximum mean discrepancy (MMD) is added to the last layer of the feature extractor to align the features of the two domains in the high-dimensional space. The experimental results show that, under the condition of variable speed, the NSSAE model can extract domain invariant features to achieve the transfer between domains, and the transfer diagnosis accuracy is high and the stability is strong.

关键词： Feature extraction Fault diagnosis Adaptation models Generators Data models Data mining Rolling bearings Artificial intelligence fault diagnosis stacked autoencoder domain adaptation

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A new deep model based on the stacked autoencoder with intensified iterative learning style for industrial fault detection

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2021年 153卷 47-59页

作者： Yu, Jianbo Yan, Xuefeng East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China

Deep learning-based process monitoring methods utilize the features extracted from deep neural net-works to perform fault detection and diagnosis. Traditional deep learning models are constructed in a fully connected manner and stacked by a sequential connection style. However, the feature information of the observed data would be discarded as the information is compressed and propagated layer-by-layer in hidden layers. Motivated by this, an intensified iterative learning (IIL) model which is developed from the stacked autoencoder is proposed in this study. In the process of feature extraction of IIL model, the traditional constraints of the hidden layer connection in deep neural networks are disregarded and the feature information of the current hidden layer comes from the information of all previous hidden layers to avoid the loss of information. In the process of real-time process monitoring, the features which are advantageous to accomplish fault detection would be intensified to obtain the most favorable features for monitoring. Finally, Euclidean distance and reconstruction error are employed to indicate and visualize the process status. The monitoring performance of the proposed IIL model is evaluated on three process tasks, and the results show it outperforms other deep learning methods on fault detection . (c) 2021 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Process monitoring stacked autoencoder Intensified iterative learning model Information loss Feature information intensify

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Deep stacked autoencoder-Based Long-Term Spectrum Prediction Using Real-World Data

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2023年第3期9卷 534-548页

作者： Pan, Guangliang Wu, Qihui Ding, Guoru Wang, Wei Li, Jie Xu, Fuyuan Zhou, Bo Nanjing Univ Aeronaut & Astronaut Key Lab Dynam Cognit Syst Electromagnet Spectrum S Minist Ind & Informat Technol Nanjing 211106 Peoples R China Army Engn Univ Coll Commun Engn Nanjing 210007 Peoples R China Nanjing Elect Equipment Res Inst Nanjing 210000 Peoples R China

Spectrum prediction is challenging due to its multi-dimension, complex inherent dependency, and heterogeneity among the spectrum data. In this paper, we first propose a stacked autoencoder (SAE) and bi-directional long short-term memory (Bi-LSTM) based spectrum prediction method (SAEL-SP). Specifically, a SAE is designed to extract the hidden features (semantic coding) of spectrum data in an unsupervised manner. Then, the output of SAE is connected to a predictor (Bi-LSTM), which is used for long-term prediction by learning hidden features. The main advantage of SAEL-SP is that the underlying features of spectrum data can be retained automatically, layer by layer, rather than designing them manually. To further improve the prediction accuracy of SAEL-SP and achieve a wider bandwidth prediction, we propose a SAE-based spectrum prediction method using temporal-spectral-spatial features of data (SAE-TSS). Different from SAEL-SP, the input of SAE-TSS is the image format. SAE-TSS achieves higher prediction accuracy than SAEL-SP using the features extracted from time, frequency, and space dimensions. We use a real-world spectrum dataset to validate the effectiveness of two prediction frameworks. Experiment results show that both SAEL-SP and SAE-TSS outperform existing spectrum prediction approaches.

关键词： Predictive models Feature extraction Hidden Markov models Time-frequency analysis Frequency measurement Autoregressive processes Data models Spectrum prediction temporal-spectral-spatial correlations deep learning stacked autoencoder

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A stacked autoencoder-Based miRNA Regulatory Module Detection Framework

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2019年第2期12卷 822-832页

作者： Yang, Yi Song, Yan Hunan Womens Univ Sch Informat Sci & Engn Zhongyi Rd 160 Changsha 410004 Hunan Peoples R China

MicroRNA regulatory module (MRM) plays an important role in the study of microRNA synergism. To detect MRMs, researchers have developed a number of related methods in the preceding decades. However, some existing methods are stochastic or specific to a certain situation. In this paper, we presented a novel deep ensemble framework called DeMosa to identify MRM for different cancers. In the proposed framework, we integrated stacked autoencoders and K-means method to detect MRMs in high-dimensional complex biological networks. We tested our method on synthetic data and three types of cancer data sets. In the synthetic data, we found DeMosa is superior to existing three methods SNMNMF, Mirsynergy, and bi-cliques merging (BCM) on clustering accuracy, stability, and module quality, while in the cancer datasets, DeMosa is more adaptable in different situations than the counterparts. In addition, we applied Kaplan-Meier survival analysis to predict several MRMs as potential prognostic biomarkers in cancers. (c) 2019 The Authors. Published by Atlantis Press SARL.

关键词： Module detection Intimacy K-means stacked autoencoder

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