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Semisupervised fault diagnosis of aeroengine based on denoising autoencoder and deep belief network

AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY 2022年第10期94卷 1772-1779页

作者： Lv, Defeng Wang, Huawei Che, Changchang Nanjing Univ Aeronaut & Astronaut Sch Civil Aviat Nanjing Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing Peoples R China Nanjing Forestry Univ Coll Automobile & Traff Engn Nanjing Peoples R China

Purpose The purpose of this study is to analyze the intelligent semisupervised fault diagnosis method of aeroengine. Design/methodology/approach A semisupervised fault diagnosis method based on denoising autoencoder (DAE) and deep belief network (DBN) is proposed for aeroengine. Multiple state parameters of aeroengine with long time series are processed to form high-dimensional fault samples and corresponding fault types are taken as sample labels. DAE is applied for unsupervised learning of fault samples, so as to achieve denoised dimension-reduction features. Subsequently, the extracted features and sample labels are put into DBN for supervised learning. Thus, the semisupervised fault diagnosis of aeroengine can be achieved by the combination of unsupervised learning and supervised learning. Findings The JT9D aeroengine data set and simulated aeroengine data set are applied to test the effectiveness of the proposed method. The result shows that the semisupervised fault diagnosis method of aeroengine based on DAE and DBN has great robustness and can maintain high accuracy of fault diagnosis under noise interference. Compared with other traditional models and separate deep learning model, the proposed method also has lower error and higher accuracy of fault diagnosis. Originality/value Multiple state parameters with long time series are processed to form high-dimensional fault samples. As a typical unsupervised learning, DAE is used to denoise the fault samples and extract dimension-reduction features for future deep learning. Based on supervised learning, DBN is applied to process the extracted features and fault diagnosis of aeroengine with multiple state parameters can be achieved through the pretraining and reverse fine-tuning of restricted Boltzmann machines.

关键词： Aeroengine Semisupervised fault diagnosis denoising autoencoder Deep belief network Unsupervised learning Supervised learning

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Adaptive denoising autoencoder for robust fault detection

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2024年 188卷 860-876页

作者： Li, Zixuan Zhao, Haitao East China Univ Sci & Technol Sch Informat Sci & Engn Automat Dept Shanghai 200237 Peoples R China

In real chemical processes, the collected data is often subject to interference from ambient environmental noise, resulting in a decline in the detection performance. Although denoising autoencoder can realize robust fault detection to a certain extent, its performance is limited by the addition of artificial noise, which refers to Gaussian noise or the dropout noise. Moreover, the artificial noise added is often far from the distribution of real data, making it difficult to suppress the noise in real data. To address this issue, an adaptive denoising autoencoder (ADAE) based on the diffusion model is proposed. ADAE adopts a chain -like structure, concatenating traditional DAEs by replacing the manually injected noise in each DAE block with noise generated by diffusion model at various diffusion steps. In order to better capture the key features of the original process data, a novel noise prediction network is proposed within the diffusion model, utilizing dilated and gated convolutions to capture the dynamic information of the process data. Ultimately, adaptation is achieved through a chain of DAEs, suppressing noise similar to the distribution of the original data. The superiority of ADAE in noise suppression is then verified through numerical examples and the Tennessee Eastman process (TEP), comparing it with other methods.

关键词： Process monitoring Robust fault detection denoising autoencoder Diffusion model

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Towards the Analysis of Regularized denoising autoencoder for Biosignal Processing: Lasso Versus Ridge Norms

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WIRELESS PERSONAL COMMUNICATIONS 2024年第1期134卷 319-338页

作者： Dasan, Evangelin Jeyabalan, Nelson Samuel Jebastin Unnamalai Inst Technol Dept Comp Sci & Engn Kovilpatti 628502 Tamil Nadu India Annamalai Univ Dept Zool Chidambaram 608002 Tamil Nadu India

The use of Internet of Things (IoT) that integrate smart bio sensor devices to the internet and shows individual health in real time. Healthcare organizations gain measurable insights into their most demanding problems like chronic diseases which demands long term monitoring. Various types of nano-sensors like Ingestible embedded in pills, Blood sampling sensor and tissue sensors are used in Healthcare IoT. Such implantable device collects, process and sends the vital signs called biosignals from particular organ of the human body where it has been implanted or fixed throughout the day to remote clinician. Such prolonged monitoring may weaken the battery power of nano-sensors. Since nano-sensors are miniaturized in nature and completely relies on its battery, energy awareness is incorporated in this paradigm that can help to avoid unnecessary energy consumption. This is achieved by data compression scheme. As the nano-sensors are light weight devices the designed algorithm should be low complex as well as efficient. As well, the signal acquired through this wireless sensor device are prone to be contaminated with noises because of the wearer's movements. In this study, regularized denoising autoencoder (DAE) has been employed to compress and recover the signal from its noisy version. Lasso (Least Absolute Shrinkage and Selection Operator) and Ridge regularization concepts are used and contrasted in this article. The cost function now includes these penalty clauses to address the overfitting problem. The experimental findings demonstrate that LASSO Norm has outperformed over RIDGE in 18% for ECG, 57% for EMG & 31% for EEG signal with respect to Quality Score. The datasets used in this investigation were taken from a database that was open to the public for testing.

关键词： Internet of Things Wireless nano-sensor device denoising autoencoder Biosignals Lasso Ridge

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End-to-end recurrent denoising autoencoder embeddings for speaker identification

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NEURAL COMPUTING & APPLICATIONS 2021年第21期33卷 14429-14439页

作者： Rituerto-Gonzalez, Esther Pelaez-Moreno, Carmen Univ Carlos III Madrid Dept Signal Theory & Commun Grp Multimedia Proc Ave Univ 30 Madrid 28911 Spain

Speech 'in-the-wild' is a handicap for speaker recognition systems due to the variability induced by real-life conditions, such as environmental noise and the emotional state of the speaker. Taking advantage of the principles of representation learning, we aim to design a recurrent denoising autoencoder that extracts robust speaker embeddings from noisy spectrograms to perform speaker identification. The end-to-end proposed architecture uses a feedback loop to encode information regarding the speaker into low-dimensional representations extracted by a spectrogram denoising autoencoder. We employ data augmentation techniques by additively corrupting clean speech with real-life environmental noise in a database containing real stressed speech. Our study presents that the joint optimization of both the denoiser and speaker identification modules outperforms independent optimization of both components under stress and noise distortions as well as handcrafted features.

关键词： denoising autoencoder Speaker embeddings Noisy conditions Stress End-to-end model Speaker identification

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Single-channel Dereverberation for Distant-Talking Speech Recognition by Combining denoising autoencoder and Temporal Structure Normalization

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JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY 2016年第2期82卷 151-161页

作者： Ueda, Yuma Wang, Longbiao Kai, Atsuhiko Xiao, Xiong Chng, Eng Siong Li, Haizhou Shizuoka Univ Grad Sch Engn Hamamatsu Shizuoka 4328561 Japan Nagaoka Univ Technol Nagaoka Niigata 9402188 Japan Nanyang Technol Univ Temasek Labs NTU Singapore 138632 Singapore Nanyang Technol Univ Sch Comp Engn Singapore 138632 Singapore ASTAR Inst Infocomm Res Human Language Technol Singapore 138632 Singapore

In this paper, we propose a robust distant-talking speech recognition by combining cepstral domain denoising autoencoder (DAE) and temporal structure normalization (TSN) filter. As DAE has a deep structure and nonlinear processing steps, it is flexible enough to model highly nonlinear mapping between input and output space. In this we train a DAE to map reverberant and noisy speech features to the underlying clean speech features in the cepstral domain. For the proposed method, after applying a DAE in the cepstral domain of speech to suppress reverberation, we apply a post-processing technology based on temporal structure normalization (TSN) filter to reduce the noise and reverberation effects by normalizing the modulation spectra to reference spectra of clean speech. The proposed method was evaluated using speech in simulated and real reverberant environments. By combining a cepstral-domain DAE and TSN, the average Word Error Rate (WER) was reduced from 25.2 % of the baseline system to 21.2 % in simulated environments and from 47.5 % to 41.3 % in real environments, respectively.

关键词： Speech recognition Dereverberation denoising autoencoder Environment adaptation Distant-talking speech

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Gap imputation in related multivariate time series through recurrent neural network-based denoising autoencoder

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INTEGRATED COMPUTER-AIDED ENGINEERING 2024年第2期31卷 157-172页

作者： Alonso, Serafin Moran, Antonio Perez, Daniel Prada, Miguel A. Fuertes, Juan J. Dominguez, Manuel Univ Leon Grp Invest Supervis Control & Automatizac Proc In Esc Ingn Ind Informat & Aeroespacial Campus Vegazana S-N Leon 24007 Spain

Technological advances in industry have made it possible to install many connected sensors, generating a great amount of observations at high rate. The advent of Industry 4.0 requires analysis capabilities of heterogeneous data in form of related multivariate time series. However, missing data can degrade processing and lead to bias and misunderstandings or even wrong decision-making. In this paper, a recurrent neural network-based denoising autoencoder is proposed for gap imputation in related multivariate time series, i.e., series that exhibit spatio-temporal correlations. The denoising autoencoder (DAE) is able to reproduce input missing data by learning to remove intentionally added gaps, while the recurrent neural network (RNN) captures temporal patterns and relationships among variables. For that reason, different unidirectional (simple RNN, GRU, LSTM) and bidirectional (BiSRNN, BiGRU, BiLSTM) architectures are compared with each other and to state-of-the-art methods using three different datasets in the experiments. The implementation with BiGRU layers outperforms the others, effectively filling gaps with a low reconstruction error. The use of this approach is appropriate for complex scenarios where several variables contain long gaps. However, extreme scenarios with very short gaps in one variable or no available data should be avoided.

关键词： Sensor observations missing data gap imputation multivariate time series denoising autoencoder recurrent neural network

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A channel estimation method using denoising autoencoder for large-scale asymmetric backscatter systems

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ICT EXPRESS 2024年第2期10卷 400-405页

作者： Jung, Chae Yoon Kang, Jae-Mo Kim, Dong In Sungkyunkwan Univ SKKU Dept Superintelligence Engn Suwon South Korea Kyungpook Natl Univ Dept Artificial Intelligence Daegu South Korea Sungkyunkwan Univ SKKU Dept Elect & Comp Engn Suwon South Korea

A novel channel estimation method based on deep learning algorithm is proposed for large-scale IoT networks. We consider asymmetric backscatter communication system to maintain low-power at sensor nodes. In order to obtain channel data, we design denoising autoencoder which consists of encoder with Feedforward Neural Network (FNN) and decoder with Convolutional Neural Network (CNN). Finally, the channel estimation error is minimized, while the pilots are optimized. Especially, we adopt beamforming technique that relies only on cascaded channel data to reduce complexity in multi-sensor system. It is shown that the accuracy is slightly degraded while the complexity is greatly reduced. (c) 2023 The Author(s). Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Backscatter communication Beamforming Channel estimation Deep learning denoising autoencoder

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Concept Drift Detection with denoising autoencoder in Incomplete Data 18th

Concept Drift Detection with Denoising Autoencoder in Incomp...

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18th European-Alliance-for-Innovation (EAI) International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous)

作者： Murao, Jun Yonekawa, Kei Kurokawa, Mori Amagata, Daichi Maekawa, Takuya Hara, Takahiro Osaka Univ Osaka Japan KDDI Res Inc Saitama Japan

ISBN: (纸本)9783030948221;9783030948214

Recent e-commerce and location-based services provide personalized recommendations based on machine-learning models that take into account purchase and visiting histories. Because machine-learning models assume the same distributions between training and test data, they cannot catch up with concept drifts, i.e., changes of behavioral patterns over time. To keep recommendation accurate, it is important to detect concept drifts. Generally, to achieve this, we need complete data (i.e., data without missing values). In real-world datasets, however, there are many incomplete data, and existing concept drift detection techniques do not deal with incomplete data. To address this issue, we investigate how a deep learning technique (denoising autoencoder), which complements missing values, contributes to detecting concept drifts in incomplete data. We conduct experiments on synthetic and real datasets to evaluate the robustness of this technique, and our results show its advantages.

关键词： Concept drift Incomplete data denoising autoencoder

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A clinical trial termination prediction model based on denoising autoencoder and deep survival regression

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Quantitative Biology 2024年第2期12卷 205-214页

作者： Huamei Qi Wenhui Yang Wenqin Zou Yuxuan Hu School of Electronic Information Central South UniversityChangshaHunanChina School of Computer Science and Engineering Central South UniversityChangshaHunanChina

Effective clinical trials are necessary for understanding medical advances but early termination of trials can result in unnecessary waste of *** models can be used to predict survival probabilities in such ***,survival data from clinical trials are sparse,and DeepSurv cannot accurately capture their effective features,making the models weak in generalization and decreasing their prediction *** this paper,we propose a survival prediction model for clinical trial completion based on the combination of denoising autoencoder(DAE)and DeepSurv *** DAE is used to obtain a robust representation of features by breaking the loop of raw features after autoencoder training,and then the robust features are provided to DeepSurv as input for *** clinical trial dataset for training the model was obtained from the *** dataset.A study of clinical trial completion in pregnant women was conducted in response to the fact that many current clinical trials exclude pregnant *** experimental results showed that the denoising autoencoder and deep survival regression(DAE-DSR)model was able to extract meaningful and robust features for survival analysis;the C-index of the training and test datasets were 0.74 and 0.75 *** with the Cox proportional hazards model and DeepSurv model,the survival analysis curves obtained by using DAE-DSR model had more prominent features,and the model was more robust and performed better in actual prediction.

关键词： clinical trials denoising autoencoder DeepSurv experimental termination survival analysis

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Automated Computer-aided Design of Cranial Implants Using a Deep Volumetric Convolutional denoising autoencoder

Automated Computer-aided Design of Cranial Implants Using a ...

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World Conference on Information Systems and Technologies (WorldCIST)

作者： Morais, Ana Egger, Jan Alves, Victor Univ Minho Sch Engn Dept Informat Braga Portugal Graz Univ Technol Inst Comp Graph & Vis Graz Austria Univ Minho Algoritmi Ctr Braga Portugal Comp Algorithms Med Lab Graz Austria

ISBN: (纸本)9783030161866;9783030161873

Computer-aided Design (CAD) software enables the design of patient-specific cranial implants, but it often requires of a lot of manual user-interactions. This paper proposes a Deep Learning (DL) approach towards the automated CAD of cranial implants, allowing the design process to be less user-dependent and even less time-consuming. The problem of reconstructing a cranial defect, which is essentially filling in a region in a skull, was posed as a 3D shape completion task and, to solve it, a Volumetric Convolutional denoising autoencoder was implemented using the open-source DL framework PyTorch. The autoencoder was trained on 3D skull models obtained by processing an open-access dataset of Magnetic Resonance Imaging brain scans. The 3D skull models were represented as binary voxel occupancy grids and experiments were carried out for different voxel resolutions. For each experiment, the autoencoder was evaluated in terms of quantitative and qualitative 3D shape completion performance. The obtained results showed that the implemented Deep Neural Network is able to perform shape completion on 3D models of defected skulls, allowing for an efficient and automatic reconstruction of cranial defects.

关键词： Cranial implant Medical imaging Computer-aided Design (CAD) Deep Learning 3D shape completion denoising autoencoder

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