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Remaining useful life prediction for space bearing with cage friction fault based on data pre-screening and gated recurrent unit

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MEASUREMENT SCIENCE AND TECHNOLOGY 2023年第12期34卷 125049-125049页

作者： Wang, Jianwen Pan, Qiang He, Tian Wang, Hong Qing, Tao Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China Beijing Inst Control Engn Beijing Key Lab Long life Technol Precise Rotat & Beijing 100194 Peoples R China

The remaining useful life (RUL) of bearings in space inertia actuators is crucial for performance maintenance requirements. But it is quite difficult to accurately predict the RUL of space bearings due to the significant intermittency and nonstationary properties caused by cage friction faults commonly occurring during the operation of the actuator. This paper proposes a data-driven method for RUL prediction of space bearings by incorporating the gated recurrent unit network with a novel data pre-screening approach. In the proposed method, a stacked autoencoder and clustering approach are introduced into the data pre-processing method, and a health index called Overrun-Distance is constructed for lifetime assessment. To verify the proposed method, a series of vibration tests on flywheels equipped with space bearings are conducted and used for RUL evaluation. The results show that the proposed RUL prediction method is applicable to space bearings for RUL prediction with high accuracy and effectiveness.

关键词： space bearing cage friction fault remaining useful life prediction stacked autoencoder gated recurrent unit

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Exploring Implicit and Explicit Geometrical Structure of Data for Deep Embedded Clustering

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NEURAL PROCESSING LETTERS 2021年第1期53卷 1-16页

作者： Zhu, Xiaofei Do, Khoi Duy Guo, Jiafeng Xu, Jun Dietze, Stefan Chongqing Univ Technol Coll Comp Sci & Engn Chongqing 400054 Peoples R China Leibniz Univ Hannover Res Ctr L3S D-30167 Hannover Germany Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China Renmin Univ China Sch Informat Beijing 100872 Peoples R China Leibniz Inst Social Sci Knowledge Technol Social Sci D-50667 Cologne Germany

Clustering is an essential data analysis technique and has been studied extensively over the last decades. Previous studies have shown that data representation and data structure information are two critical factors for improving clustering performance, and it forms two important lines of research. The first line of research attempts to learn representative features, especially utilizing the deep neural networks, for handling clustering problems. The second concerns exploiting the geometric structure information within data for clustering. Although both of them have achieved promising performance in lots of clustering tasks, few efforts have been dedicated to combine them in a unified deep clustering framework, which is the research gap we aim to bridge in this work. In this paper, we propose a novel approach, Manifold regularized Deep Embedded Clustering (MDEC), to deal with the aforementioned challenge. It simultaneously models data generating distribution, cluster assignment consistency, as well as geometric structure of data in a unified framework. The proposed method can be optimized by performing mini-batch stochastic gradient descent and back-propagation. We evaluate MDEC on three real-world datasets (USPS, REUTERS-10K, and MNIST), where experimental results demonstrate that our model outperforms baseline models and obtains the state-of-the-art performance.

关键词： Deep neural networks stacked autoencoder Manifold constraint Clustering

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An autoencoder-based spectral clustering algorithm

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SOFT COMPUTING 2020年第3期24卷 1661-1671页

作者： Li, Xinning Zhao, Xiaoxiao Chu, Derun Zhou, Zhiping Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Jiangsu Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China

Spectral clustering algorithm suffers from high computational complexity due to the eigen decomposition of Laplacian matrix and large similarity matrix for large-scale datasets. Some researches explore the possibility of deep learning in spectral clustering and propose to replace the eigen decomposition with autoencoder. K-means clustering is generally used to obtain clustering results on the embedding representation, which can improve efficiency but further increase memory consumption. An efficient spectral algorithm based on stacked autoencoder is proposed to solve this issue. In this paper, we select the representative data points as landmarks and use the similarity of landmarks with all data points as the input of autoencoder instead of similarity matrix of the whole datasets. To further refine clustering result, we combine learning the embedding representation and performing clustering. Clustering loss is used to update the parameters of autoencoder and cluster centers simultaneously. The reconstruction loss is also included to prevent the distortion of embedding space and preserve the local structure of data. Experiments on several large-scale datasets validate the effectiveness of the proposed method.

关键词： Spectral clustering stacked autoencoder Sparse representation KL divergence

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Energy-and trust-aware secure routing algorithm for big data classification using MapReduce framework in IoT networks

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International Journal of Modeling, Simulation, and Scientific Computing 2021年第2期12卷 80-103页

作者： S.Md.Mujeeb R.Praveen Sam K.Madhavi Department of CSE Jawaharlal Nehru Technological University Anantapur(JNTUA)Ananthapuramu-515002 Andhra PradeshIndia Department of Computer Science and Engineering G.Pulla Reddy Engineering College(Autonomous)Nandyal Road Kurnool 518007Andhra PradeshIndia

Due to the open nature of wireless data transmission,routing and data security pose an important research challenge in the Internet of Things(IoT)-enabled ***,the characteristic features,like constrained resources,heterogeneity,uncontrolled environment,and scalability requirement,make the security issues even more ***,an effective and secure routing protocol named modified Energy Harvesting Trust-aware Routing Algorithm(mod-EHTARA)is proposed to increase the energy efficiency and the lifespan of the *** proposed mod-EHTARA is designed by adopting the Link Lifetime(LLT)model with the traditional *** optimal secure routing path is effectively selected by the proposed mod-EHTARA using the cost metric,which considers the factors like delay,LLT,energy,and *** big data classification process is carried out at the Base Station(BS)using the MapReduce ***,the big data classification is progressed using a stacked autoencoder,which is trained by the Adaptive E-Bat *** Adaptive E-Bat algorithm is developed by integrating the adaptive concept with the Bat Algorithm(BA)and Exponential Weighted Moving Average(EWMA).The proposed mod-EHTARA showed better performance by obtaining a maximal energy of 0.9855.

关键词： stacked autoencoder big data classification Internet of Things secure routing energy harvesting

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SN-SAE: a new damage diagnosis method for CFRP using Lamb wave

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MEASUREMENT SCIENCE AND TECHNOLOGY 2023年第9期34卷 095020-095020页

作者： Li, Zhiyong Wang, Zhiyong Li, Yong Han, Shanling Shandong Univ Sci & Technol Coll Mech & Elect Engn Qingdao Peoples R China Binzhou Univ Coll Mech & Elect Engn Binzhou Peoples R China

The damage diagnosis of carbon fiber reinforced polymer (CFRP) using Lamb wave has been widely developed, but it is still a challenging task to obtain reliable damage diagnosis results by analysis of Lamb wave, the emergence of deep learning models provides an effective solution for this work. However, the internal covariate shift and overfitting exist in traditional deep networks. The SN-SAE (stochastic normalization-stacked autoencoder) deep neural network model is proposed by introducing stochastic normalization (SN) into stacked autoencoder (SAE). The signals of 28 different damage locations in the CFRP plate provided by the open platform were processed by SN-SAE, and the damage diagnosis at different locations was achieved. The validity of SN-SAE was further verified by data obtained through building an experimental platform. The results demonstrated that the SN-SAE model can achieve high test accuracy with only 15% of the data samples as training with limited data sample, which provides a simple and effective solution for damage diagnosis of composite plates.

关键词： damage diagnosis carbon fiber reinforced polymer Lamb wave stochastic normalization stacked autoencoder

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On deep ensemble CNN-SAE based novel agro-market price forecasting

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EVOLUTIONARY INTELLIGENCE 2021年第2期14卷 851-862页

作者： Parida, Nirjharinee Mishra, Debahuti Das, Kaberi Rout, Narendra Kumar Panda, Ganapati Siksha O Anusandhan Deemed Univ Dept Comp Sci Engn Bhubaneswar Odisha India Natl Inst Technol Dept Comp Applicat GE Rd Chhatisgarh 492010 CG India CV Raman Global Univ Dept Elect & Telecommun Engn Bhubaneswar 752054 Odisha India

The prices of agro-commodities are highly volatile. Hence it is a challenge to the farmers to ensure fair and remunerative prices of these commodities. As a result, there is a need for prediction of agro market price appropriately. The closing price prediction of one soft commodity product, Cotton 29 mm and one agro-commodity product, Guar gum are chosen. The existing reported methods exhibit poor prediction performance. To alleviate this problem, the current investigation is undertaken for better prediction of closing prices. The deep ensemble approach using convolutional neural network (CNN) and stacked autoencoder (SAE) is employed to improve the prediction performance. For the ensemble strategy, the weights are optimized using three bio-inspired techniques such as genetic algorithm (GA), particle swarm optimization (PSO) and spider monkey optimization (SMO). Eighteen attributes relating to the closing price of each products are considered as input to the proposed models. The simulation based experimental results demonstrate the following contribution of the paper. Firstly, it is observed that CNN outperforms the SAE model in terms of short range prediction and vice versa for long range prediction. Secondly, the prediction performance of all the three ensemble models has been determined. Thirdly, out of three ensemble models, ensemble-SMO (ESMO) shows the best prediction performance in terms of mean square error and coefficient of multiple determination (R-2). It is then followed by ensemble-PSO and ensemble-GA respectively. The performance of proposed best ESMO is compared with the Grey wolf optimization based multiquadratic kernel KELM model (GWO-KELM) and it is observed that the proposed ESMO outperforms the GWO-KELM model.

关键词： Deep learning Convolutional neural network stacked autoencoder Ensemble learning Spider monkey optimization Genetic algorithm Particle swarm optimization R-2(coefficient of multiple determination)

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Soft sensor method of multimode BOF steelmaking endpoint carbon content and temperature based on vMF-WSAE dynamic deep learning

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HIGH TEMPERATURE MATERIALS AND PROCESSES 2023年第1期42卷

作者： Yang, Lu Liu, Hui Chen, Fugang Kunming Univ Sci & Technol Fac Informat Engn & Automation Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Artificial Intelligence Kunming 650500 Yunnan Peoples R China Yunnan Kungang Elect & Informat Sci Ltd Kunming 650302 Yunnan Peoples R China

The difficulty of endpoint determination in basic oxygen furnace (BOF) steelmaking lies in achieving accurate real-time measurements of carbon content and temperature. For the characteristics of serious nonlinearity between process data, deep learning can perform excellent nonlinear feature representation for complex structural data. However, there is a process drift phenomenon in BOF steelmaking, and the existing deep learning-based soft sensor models cannot adapt to changes in the characteristics of samples, which may lead to their performance degradation. To deal with this problem, considering the characteristics of multimode distribution of process data, an adaptive updating deep learning model based on von-Mises Fisher (vMF) mixture model and weighted stacked autoencoder is proposed. First, the stacked autoencoder (SAE) and vMF mixture model are constructed for complex structural data, which can initially establish nonlinear mapping relationships and division of different distributions. Second, for each query sample, the basic SAE network will perform online adaptive fine-tuning according to its data with the same distribution to achieve dynamic updating. Moreover, each sample is assigned a weight according to its similarity with the query sample. Through the designed weighted loss function, the updated deep network will better match the working conditions of the query sample. Experimental studies with numerical examples and actual BOF steelmaking process data are provided to demonstrate the effectiveness of the proposed method.

关键词： BOF steelmaking endpoint stacked autoencoder vMF mixture model similarity measure adaptive weighted updating

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Robust classification and biomarker discovery of inherited metabolic diseases using GC-MS urinary metabolomics analysis combined with chemometrics

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MICROCHEMICAL JOURNAL 2023年 190卷

作者： Chen, Nan Chen, Si Zhang, Qi Wang, Si-Rui Tang, Li-Juan Jiang, Jian-Hui Yu, Ru-Qin Zhou, Yan-Ping Hunan Univ Coll Chem & Chem Engn State Key Lab Chemo Biosensing & Chemometr Changsha 410082 Peoples R China Cent China Normal Univ Coll Chem Key Lab Pesticide & Chem Biol Minist Educ Wuhan 430079 Peoples R China Wuhan Univ Res Ctr Strateg Emerging Ind Wuhan 430071 Peoples R China

Early diagnosis and treatment of inherited metabolic diseases (IMDs) is crucial for reducing neonatal mortality rate and improving quality of life in children. The discovery of disease-related biomarkers that can objectively measure the potential pathophysiological changes is vital to improve the prognosis of IMDs. In this study, we collected 90 clinic urine samples of newborns, including two types of IMDs and healthy samples, glutaric aciduria type I (GA I) and propionic acidemia (PA). And 132 metabolites were identified using gas chromatography-mass spectrometry (GC-MS). Then we proposed an integrated chemometrics strategy of assembling discrete particle swarm optimization (DPSO) into stacked autoencoder (SAE) to form a framework called DPSO-SAE for the study of GC-MS metabolomics data. SAE was known for its excellent non-linear feature learning ability. The intro-duction of DPSO afforded SAE the possibility of biomarker discovery and improving performance on classifi-cation via enabling synergetic optimization of variable combinations and the parameter of neuron numbers for SAE modeling. We then invoked DPSO-SAE for the data analysis as compared with random forest (RF), partial least squares discriminant analysis (PLSDA) and conventional SAE. Superior performance was obtained by DPSO-SAE with high accuracy and good generalization ability on classification. We further demonstrated the robust-ness of DPSO-SAE in variable selection and proofed the statistical significance of identified marker metabolites that account for IMD classification. Six potential biomarkers were proofed, including 3-methylglutaconic, 3-OH-propionic, Methylcitric, Methylmalonic and Uric for PA and Glutaric for GA I. All results indicated that the proposed strategy of DPSO-SAE was feasible for robust classification and biomarker discovery of IMDs. And it may provide a valuable modeling algorithm for metabolomics studies.

关键词： stacked autoencoder Discrete particle swarm optimization GC-MS urinary metabolomics Classification Biomarker discovery Inherited metabolic diseases

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Identifying typical pre-crash scenarios based on in-depth crash data with deep embedded clustering for autonomous vehicle safety testing

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ACCIDENT ANALYSIS AND PREVENTION 2023年第1期191卷 107218页

作者： Zhou, Rui Huang, Helai Lee, Jaeyoung Huang, Xiangzhi Chen, Jiguang Zhou, Hanchu Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Peoples R China Univ Cent Florida Dept Civil Environm & Construction Engn Orlando FL 32816 USA Newhood Technol Co Ltd Changsha 410075 Peoples R China City Univ Hong Kong Sch Data Sci Hong Kong 99907 Peoples R China

Choosing appropriate scenarios is critical for autonomous vehicles (AVs) safety testing. Real-world crash scenarios can be used as critical scenarios to test the safety performance of AVs. As one of the dominant types of traffic crashes, the car to powered-two-wheelers (PTWs) crash results in a higher possibility of fatality than ordinary car-to-car crashes. Generally, typical testing scenarios are chosen according to the subjective understanding of the safety experts with limited static features of crashes (e.g., geometric features, weather). This study introduced a novel method to identify typical car-to-PTWs crash scenarios based on real-world crashes with dynamic pre-crash features investigated from the China In-depth Mobility Safety Study-Traffic Accident (CIMSSTA) database. First, we present crash data collection and construction methods of the CIMSS-TA database to construct testing scenarios. Second, the stacked autoencoder methods are used to learn and obtain embedded features from the high-dimensional data. Third, the extracted features are clustered using k-means clustering algorithm, and then the clustering results are interpreted. Six typical car-to-PTWs scenarios are obtained with the proposed processes. This study introduces a typical high-risk scenario construction method based on deep embedded clustering. Unlike existing researches, the proposed method eliminates the negative impacts of manually selecting clustering variables and provides a more detailed scenario description. As a result, the typical scenarios obtained from AV testing are more robust.

关键词： Testing scenarios Clustering analysis Autonomous vehicles stacked autoencoder

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Identification of multiple power quality disturbances in hybrid microgrid using deep stacked auto-encoder based bi-directional LSTM classifier

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2025年 11卷

作者： Jalli, Ravi Kumar Priyadarshini, Lipsa Dash, P.K. Bisoi, Ranjeeta Siksha 'O' Anusandhan Deemed to be University Odisha Bhubaneswar India Department of EEE GMR Institute of Technology Andhra Pradesh Rajam India

In recent years microgrid technology has created widespread interest for the integration of renewable energy sources into main utility grid to supply clean energy to the end users. However, the use of power electronic equipments, electronic controllers, and uncertain nature of the renewable energy sources, in the microgrid network power quality disturbances (PQD) are becoming quite complex and challenging task. Thus to design an effective PQD recognition system, this paper proposes a novel time-frequency analysis method based on adaptively fast complementary ensemble local mean decomposition (AFCELMD) technique that decomposes the multicomponent PQD signal into a series of demodulated product functions (PFs). Out of the several PFs the most sensitive one is selected adaptively and used for feature extraction and classification through a deep stacked auto-encoder (dSAE) hybridized with a time-recursive bi-directional long short term memory (BiLSTM) network classifier. The proposed BILSTM classifier captures the temporal features and their long term dependencies from the processed PF data samples and detects single and simultaneously occurring twenty complex power quality disturbances in the grid connected mode and five PQDs during uncertain PV insolence variation and load and capacitor switching during islanded mode of microgrid operation with significant accuracy of 99.90%. © 2025 The Author(s)

关键词： Bidirectional LSTM Microgrid Multiple power quality events Signal decomposition stacked autoencoder

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