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Aircraft engines Remaining Useful Life prediction with an adaptive denoising online sequential Extreme Learning Machine

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年 96卷 103936-103936页

作者： Berghout, Tarek Mouss, Leila-Hayet Kadri, Ouahab Saidi, Lotfi Benbouzid, Mohamed Univ Batna 2 Lab Automat & Mfg Engn Batna 05000 Algeria Univ Batna 2 Dept Comp Sci Batna 05078 Algeria Univ Tunis SIME Lab LR 13E503 Tunis Tunisia Univ Brest UMR CNRS 6027 Inst Rech Dupuy Lome F-29238 Brest France Shanghai Maritime Univ Logist Engn Coll Shanghai 201306 Peoples R China

Remaining Useful Life (RUL) prediction for aircraft engines based on the available run-to-failure measurements of similar systems becomes more prevalent in Prognostic Health Management (PHM) thanks to the new advanced methods of estimation. However, feature extraction and RUL prediction are challenging tasks, especially for data-driven prognostics. The key issue is how to design a suitable feature extractor that is able to give a raw of time-varying sensors measurements more meaningful representation to enhance prediction accuracy with low computational costs. In this paper, a new denoising Online Sequential Extreme Learning Machine (DOS-ELM) with double dynamic forgetting factors (DDFF) and Updated Selection Strategy (USS) is proposed. First, depending on the characteristics of the training data that comes from aircraft sensors, robust feature extraction using a modified denoising autoencoder (DAE) is introduced to learn important patterns from data. Then, USS is integrated to ensure that only the useful data sequences pass through the training process. Finally, OS-ELM is used to fit the non-accumulative linear degradation function of the engine and to address dynamic programming by trucking the new coming data and forgetting gradually the old ones based on the proposed DDFF. The proposed DOS-ELM is tested on the public dataset of commercial modular aeropropulsion system simulation (C-MAPSS) of a turbofan engine and compared with OS-ELM trained with ordinary autoencoder (AE), basic OS-ELM and previous works from the literature. Comparison results prove the effectiveness of the new integrated robust feature extraction scheme by showing more stability of the network responses even under random solutions.

关键词： ELM OS-ELM Forgetting mechanism denoising autoencoder Updated selection strategy C-MAPPS Remaining Useful Life

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Integrated framework for SOH estimation of lithium-ion batteries using multiphysics features

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ENERGY 2022年第PartA期238卷 121712-121712页

作者： Son, Seho Jeong, Siheon Kwak, Eunji Kim, Jun-hyeong Oh, Ki-Yong Chung Ang Univ Dept Intelligent Energy & Ind 84 Heukseok Ro Seoul 06974 South Korea Hanyang Univ Dept Mech Convergence Engn 222 Wangsimni Ro Seoul 04763 South Korea

This study proposes a highly reliable, robust, and accurate integrated framework to estimate the state-of health (SOH) of lithium-ion batteries (LIBs), focusing on feature extraction and manipulation. This framework comprises three phases: feature extraction, feature manipulation, and SOH estimation. First, multiphysics features are extracted from mechanical and electrochemical evolutionary responses as distinct health indicators (HIs) to account for the multiphysics degradation mechanisms. Second, these features are manipulated to eliminate outliers and noises. This phase is especially effective for impedance HIs, considering the high sensitivity of these HIs to minor environmental perturbations. Third, a multivariate Gaussian distribution theory estimates the SOH combined with a nonlinear quadratic kernel to account for nonlinear characteristics in degradation modes of LIBs. The estimated results under various environments verify that the multiphysics feature primarily increases accuracy, whereas the feature manipulation ensures reliability and robustness. However, both phases are complementary in securing the accuracy, reliability, and robustness of the framework. Although the lifespan of LIBs is estimated using the training set in the 5 % SOH range, the estimation errors of the proposed framework are less than 2.5 % in all test sets. Thus, the proposed method ensures its potential applicability in practical implementations of onboard battery management systems. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Lithium-ion battery denoising autoencoder Gaussian process regression State-of-health Machine learning

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Dual attention interactive fine-grained classification network based on data augmentation

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2022年第0期88卷

作者： Zhu, Qiangxi Kuang, Wenlan Li, Zhixin Guangxi Normal Univ Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China

The key to fine-grained image classification is to find discriminative regions. Most existing methods only use simple baseline networks or low-recognition attention modules to discover object differences, which will limit the model to finding discriminative regions hidden in images. This article proposes an effective method to solve this problem. The first is a novel layered training method, which uses a new training method to enhance the feature extraction ability of the baseline model. The second step focuses on key regions of the image based on improved long short-term memory (LSTM) and multi-head attention. In the third step, based on the feature map obtained by the dual attention network, spatial mapping is performed by a multi-layer perceptron (MLP). Then the element-by-element mutual multiplication calculation of the channel is performed to obtain a feature map with finer granularity. Finally, the CUB-200-2011, FGVC Aircraft, Stanford Cars, and MedMNIST v2 datasets achieved good performance.

关键词： Data augmentation Hierarchical training denoising autoencoder Dual attention mechanism Interactive attention

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Lamb wave-based damage imaging of CFRP composite structures using autoencoder and delay-and-sum

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COMPOSITE STRUCTURES 2023年 303卷

作者： Yu, Yinghong Liu, Xiao Wang, Yihan Wang, Yishou Qing, Xinlin Xiamen Univ Sch Aerosp Engn Xiamen 361005 Fujian Peoples R China

Ultrasonic guided wave is a promising technique for structural health monitoring and nondestructive testing. However, due to the anisotropy and complexity of composite materials, the imaging performance of numerous signal processing methods deteriorates with significant artifacts and unsatisfactory accuracy. To obtain a better damage imaging performance of Lamb waves in noisy and noise-free conditions, a weighted delay-and-sum (DAS) imaging method based on denoising autoencoder (DAE) learning is developed for complex composite structures. The traditional DAS formulation is modified to be more compatible with anisotropic materials. The DAE with feature learning capability is then employed to extract potentially efficient features and remove noise from ultrasonic signals. Several verification experiments conducted on flat or curved and stiffened composite structures have confirmed the ability of the DAE-DAS method to suppress artificial noise and to intensify sin-gularities induced by the anomalies. By comparing with the unweighted DAS methods and the weighted DAS method without feature extraction, the proposed algorithm has satisfactory robustness to achieve higher local-ization accuracy and fewer artifacts.

关键词： Ultrasonic guided wave Composite structures Delay-and-sum denoising autoencoder Damage imaging

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Semi-supervised health assessment of pipeline systems based on optical fiber monitoring

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2023年 230卷 1页

作者： Jiang, Shengyu He, Rui Chen, Guoming Zhu, Yuan Shi, Jiaming Liu, Kang Chang, Yuanjiang China Univ Petr East China Ctr Offshore Engn & Safety Technol COEST 66 Changjiang West Rd Qingdao Peoples R China Univ Alberta Dept Mech Engn Edmonton AB Canada

Health assessment of pipeline systems is of deep significance for improving pipeline reliability and integrity. Traditional health assessment methods may be difficult or costly to perform on pipeline systems due to the long distances and environmental constraints of pipelines. This paper incorporates the distributed optical fiber sensor (DOFS) technique and the semi-supervised learning algorithm into the pipeline health assessment framework. Three critical problems that limit the application of DOFS in pipeline health assessments are addressed. First, an applicable damage monitoring experiment of a pipeline system is designed, which is effective in obtaining the necessary base data for data-driven modeling. Second, the correspondence between the pipeline health status and the monitored strain features is established. The experimental data are shared for public research, which is expected to solve the problem of the lack of benchmark research data in related fields. Third, considering the scarcity of labeled degradation data in pipelines, a semi-supervised denoising autoencoder model is proposed specifically for pipeline health assessment. The proposed method is demonstrated and validated using a comparative experimental case study.

关键词： Pipeline system Health assessment Distributed optical fiber sensor Semi -supervised learning denoising autoencoder

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Enhancing deep learning nuclear quadrupole resonance detection using transfer learning and autoencoders

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 207卷

作者： Monea, Cristian Univ Politehn Bucuresti Doctoral Sch Elect Telecommun & Informat Technol 1-3 Iuliu Maniu Blvd Bucharest 061071 Romania Mira Technol Grp Res & Dev Dept 13 Nicolae Grigorescu St Otopeni 075100 Ilfov Romania

Nuclear quadrupole resonance is a highly specific spectroscopy technique for analyzing solid substances with applications ranging from laboratory analysis to security screening screening for prohibited substances. The technique has the drawback of a very low signal-to-noise ratio and multiple signal processing and analysis so-lutions have been proposed for noise rejection and detection. Among these, the deep learning approach using the AlexNet network was recently shown to outperform previous solutions. This paper proposes the enhancement of deep learning detection using transfer learning to extend the applicability of the detection algorithm to other spectrometers and denoising autoencoders to improve its performance at very low signal-to-noise ratios. The transfer learning technique is demonstrated by training the AlexNet network on a simulated data set and transferring the gained knowledge to a real data set. The resulting model achieves a detection accuracy of 98%, close to that obtained by the initial model trained on the real data. Two denoising architectures are proposed, such as deep neural network-based autoencoder and convolutional autoencoder. A comparative evaluation is performed at multiple signal-to-noise ratio conditions in the range [-30, 20] dB, and the convolutional autoencoder is shown to provide the best results, by significantly increasing the detection accuracy by approx. 20% at-30 dB.

关键词： Nuclear quadrupole resonance Deep learning Transfer learning denoising autoencoder Neural network Accuracy

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Growing Hierarchical Self-Organising Representation Map (GHSORM)

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INFORMATION SCIENCES 2023年第1期642卷

作者： Coulson, Matthew Ferles, Christos Winberg, Simon Naidoo, Kevin J. Univ Cape Town Fac Sci Sci Comp Res Unit ZA-7701 Rondebosch South Africa Univ Cape Town Fac Sci Dept Chem ZA-7701 Rondebosch South Africa Univ Cape Town Fac Engn & Built Environm Dept Elect Engn ZA-7701 Rondebosch South Africa

The Growing Hierarchical Self-Organising Representation Map (GHSORM) is a model fusing the denoising autoencoder, used to better represent a dataset, and the Growing Hierarchical Self -Organising Map, used for organizing and projecting the input data in clusters of varying detail. It is shown here that the GHSORM is instrumental in sub-grouping clusters that are not fully separable by a single SOM. This combined approach is first tested and illustrated on the problem of clustering handwritten digits where a modification of the Activation Maximisation method for use at the SOM output layer demonstrates the benefit of hierarchical growth in the GHSORM. In particular, the SOM Node Activation Maximisation method is used to visually represent the best approximation of each of the SOM nodes at the output layer. This demonstrates the improvement in representing difficult to separate digits in the hierarchical case. To test and measure the effi-cacy of the GHSORM hierarchical model in class and sub-class separation the method is applied to complex digital gene expression data. A cancer dataset, comprising of gene expression data that has samples of different classes and sub-classes, is used for this purpose. The GHSORM demon-strates robust capabilities to cluster and sub-cluster the different classes and subclasses of cancer, where the results are superior to both linear methods, currently in use, as well as the methods of its constituent algorithms.

关键词： Unsupervised learning Hierarchical Self -organizing map denoising autoencoder Cancer clustering Cancer sub -typing

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Damage detection for high-speed railway standard box girders based on time-frequency characteristics of train-induced strain

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2024年 218卷

作者： Wang, Xin Li, Shunlong Yang, Yaowen Zhuo, Yi Yang, Lei North Univ China Sch Environm & Safety Engn Taiyuan 030051 Peoples R China Harbin Inst Technol Sch Transportat Sci & Engn Harbin 150090 Peoples R China Nanyang Technol Univ Sch Civil & Environm Engn Singapore 639798 Singapore China Railway Design Corp Tianjin 300142 Peoples R China

This paper presents a novel approach for detecting damage in high-speed railway standard box girders by leveraging the time -frequency characteristics of train -induced strain. Based on the mechanical and deformation characteristics of high-speed railway box girders, the method involves segmenting the box girder into distinct components based on plate element analysis to identify their damage separately. It utilizes coefficients derived from wavelet transforms as indicators sensitive to damage, and employs the particle swarm optimization algorithm (PSO) to determine the ideal frequency intervals in terms of number and position. Within these optimal frequency intervals, the sum of wavelet coefficients is only sensitive to damage features while remaining unaffected by environmental and operational variations. A convolutional denoising autoencoder is employed to remove noise from the strain time -frequency image, improving the robustness of the proposed method. By utilizing the Gaussian inverse cumulative distribution function to estimate confidence boundary (CB) for damage features (DF), outliers are detected, allowing for precise damage localization and quantification. A case study for the high-speed railway box girder show that the proposed method can effectively identify, locate and quantify damage across all components in the critical key section by using data acquired from just 4 strain sensors. This method holds promise for facilitating the development of effective maintenance strategies for high-speed railway box girders.

关键词： Damage detection Train -induced response Time -frequency characteristics denoising autoencoder High-speed railway box girder

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Noise power spectral density scaled SNR response estimation with restricted range search for sound source localisation using unmanned aerial vehicles

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EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING 2020年第1期2020卷 13-13页

作者： Yen, Benjamin Hioka, Yusuke Univ Auckland Dept Mech Engn Acoust Res Ctr 20 Symonds St Auckland 1010 New Zealand

A method to locate sound sources using an audio recording system mounted on an unmanned aerial vehicle (UAV) is proposed. The method introduces extension algorithms to apply on top of a baseline approach, which performs localisation by estimating the peak signal-to-noise ratio (SNR) response in the time-frequency and angular spectra with the time difference of arrival information. The proposed extensions include a noise reduction and a post-processing algorithm to address the challenges in a UAV setting. The noise reduction algorithm reduces influences of UAV rotor noise on localisation performance, by scaling the SNR response using power spectral density of the UAV rotor noise, estimated using a denoising autoencoder. For the source tracking problem, an angular spectral range restricted peak search and link post-processing algorithm is also proposed to filter out incorrect location estimates along the localisation path. Experimental results show the proposed extensions yielded improvements in locating the target sound source correctly, with a 0.0064-0.175 decrease in mean haversine distance error across various UAV operating scenarios. The proposed method also shows a reduction in unexpected location estimations, with a 0.0037-0.185 decrease in the 0.75 quartile haversine distance error.

关键词： Microphone array Unmanned aerial vehicle Rotor noise Source localisation denoising autoencoder Power spectral density Restricted peak search

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A novel lidar signal noise reduction algorithm based on improved deep belief network

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PHYSICA SCRIPTA 2023年第12期98卷 126004-126004页

作者： Li, Shun Mao, Jiandong Gong, Xin Li, Zhiyuan North Minzu Univ Sch Elect & Informat Engn North Wenchang Rd Yinchuan 750021 Peoples R China Key Lab Atmospher Environm Remote Sensing Ningxia North Wenchang Rd Yinchuan 750021 Peoples R China

To reduce noise in the lidar return signal, an improved deep belief network (DBN) denoising algorithm is proposed in this study. In the traditional implementation process of DBN, a multi-layer fully connected network is realized by stacking restricted Boltzmann machines (RBMs). However, the RBM is an undirected graph model, and there is no clear causal relationship between random variable nodes. The denoising autoencoder (DAE) can avoid this problem and produce field generalization performance by adding random contamination during training and stacking, thereby achieving better performance than the traditional DBN. In this study, a new multi-layer DBN called DADBN is implemented by stacking DAE and RBM. First, the multi-layer DAE is placed in the beginning layer of the network as the primary filter of the signal to provide data dimensionality reduction and feature extraction. Then, the RBM is used as the lower layer, the hidden layer is calculated according to the initial value of the visible layer by the contrast of the divergence algorithm, and the visible layer is reconstructed from the samples of the hidden layer. And the sparse representation penalty is added to the RBM model to solve the assimilation phenomenon of hidden layer nodes in the RBM model. It not only optimizes the log-likelihood function when training data, but also makes the probability of each hidden layer node being activated tend to a minimum value, so as to sparsely activate the sparse hidden layer nodes. The weight matrix W is obtained by computing the results of the hidden layer twice. Finally, the original input signal was reconstructed by Gibbs sampling layer and the reconstructed signal was decoded by the decoder to achieve the purpose of noise reduction. To verify its effectiveness, this method is compared with four other denoising methods: wavelet packet algorithm, complete ensemble empirical modal decomposition (CEEMDAN), wavelet transform and empirical mode decomposition (WT-EMD), and

关键词： lidar deep learning deep belief network denoising autoencoder noise reduction

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