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deep auto-encoder Observer Multiple-model Fast Aircraft Actuator Fault Diagnosis Algorithm

INTERNATIONAL JOURNAL OF CONTROL autoMATION AND SYSTEMS 2017年第4期15卷 1641-1650页

作者： Ma, Jun Ni, Shihong Xie, Wujie Dong, Wenhan Air Force Engn Univ Engn Coll Sch Aeronaut & Astronaut Xian 710038 Shaanxi Peoples R China

In the extended multiple model adaptive estimation fault diagnosis algorithm, the extended Kalman filter has theoretical limitations, and the establishment of accurate aircraft mathematical model is almost impossible. Meanwhile, there is no automatic method to optimally select the node number of deep neural network hidden layer. In this paper, a deep auto-encoder observer multiple-model fault diagnosis algorithm for aircraft actuator fault is proposed. Based on the empirical formula of the basic auto-encoder hidden layer node number selection (three layered neural network), the recursive formula for deep auto-encoder hidden layer node number selection are proposed. The deep auto-encoder observers for no-fault and different actuator faults are trained to observe the system state. Combined with multiple model adaptive estimation, the deep auto-encoder observer overcomes the theoretical limitation of extended Kalman filter, and avoided the calculation of the nonlinear system Jacobian matrix. The simulation results show that hidden layer node number selection recursive formula is useful. The fault diagnosis algorithm is more efficient and has better performance compared to the standard methods.

关键词： deep auto-encoder fault detection and isolation hidden layer node number multiple model adaptive estimation

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deep auto-encoder based clustering

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INTELLIGENT DATA ANALYSIS 2014年第6_SUPPL期18卷 S65-S76页

作者： Song, Chunfeng Huang, Yongzhen Liu, Feng Wang, Zhenyu Wang, Liang North China Elect Power Univ Sch Control & Comp Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Southeast Univ Sch Automat Nanjing Jiangsu Peoples R China

For unsupervised problems like clustering, linear or non-linear data transformations are widely used techniques. Generally, they are beneficial to data representation. However, if data have a complicated structure, these techniques would be unsatisfying for clustering. In this paper, we propose a new clustering method based on the deep auto-encoder network, which can learn a highly non-linear mapping function. Via simultaneously considering data reconstruction and compactness, our method can obtain stable and effective clustering. Experimental results on four databases demonstrate that the proposed model can achieve promising performance in terms of normalized mutual information, cluster purity and accuracy.

关键词： Clustering deep auto-encoder non-linear transformation complicated data

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deep auto-encoder in Model Reduction of Lage-scale Spatiotemporal Dynamics

Deep Auto-encoder in Model Reduction of Lage-scale Spatiotem...

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International Joint Conference on Neural Networks (IJCNN)

作者： Wang, Mingliang Li, Han-Xiong Shen, Wenjing City Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Hong Kong Peoples R China Cent South Univ Sch Mech & Elect Engn State Key Lab High Performance Complex Mfg Changsha Hunan Peoples R China

ISBN: (纸本)9781509006199

This paper presents a deep auto-encoder based model reduction method for large scale spatiotemporal process. This method includes three phases in order to find the near-optimal parameters of the reduced order model. The sequence of the phases is allocated according to the idea of greedy training which approximately minimizes the modeling error. This method also avoids including the spatial dimensionality into the model which enables it to handle large-scale model reduction. Two case studies are carried out to demonstrate the effectiveness of the method.

关键词： deep auto-encoder model reduction Restricted Boltzmann Machine

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Intelligent fault diagnosis of rotating machinery using a new ensemble deep auto-encoder method

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MEASUREMENT 2020年 151卷

作者： Zhang, Yuyan Li, Xinyu Gao, Liang Chen, Wen Li, Peigen Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan Hubei Peoples R China

In traditional intelligent fault diagnosis methods of rotating machinery, features are designed manually by experts, which makes these methods less automatic. deep auto-encoder (DA) provides an effective way to learn discriminative features. However, individual DA is of low generalization and not robust. This work develops an ensemble DA(EDA) method for intelligent fault diagnosis. EDA is constructed by combining sparse DA, denoising DA and contractive DA. Thus, EDA can effectively handle redundant information, noisy corruption and signal perturbation. To ensure the feature learning performance of each DA model, a self-adaptive fine-tuning is designed to obtain stable convergence. To enhance the discriminative features, a dynamic weighted average method is designed to aggregate these learned features. EDA is verified on three public datasets, and achieves the testing accuracies of 100%, 99.69% and 99.92%. Comparisons with other methods, including both traditional methods and individual DA methods, demonstrate that EDA obtains higher diagnosis accuracy. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Intelligent fault diagnosis Rotating machinery Ensemble model deep auto-encoder

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Turn-to-turn short circuit of motor stator fault diagnosis in continuous state based on deep auto-encoder

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IET ELECTRIC POWER APPLICATIONS 2019年第10期13卷 1598-1606页

作者： Wang, Botao Shen, Chuanwen Xu, Kexing Zheng, Tingting Xi An Jiao Tong Univ Sch Elect Engn Xianning West Rd Xian Shaanxi Peoples R China

In this study, a turn-to-turn short circuit of motor stator fault diagnosis system based on deep auto-encoder and soft-max classifier is proposed. It is also considered in the proposed system that an unbalanced power supply has similar current characteristics and is possible to be misidentified. The determination of neural network parameters and their effect in training are given systematically, which improves the performance compared with traditional auto-encoder. The proposed fault diagnosis system can map the preprocessed three-phase motor current signals to a two-dimensional vector, corresponding to certain area of the feature plane to identify different fault types. The proposed system is verified via simulation experiment using MATLAB and physical experiment using a motor in laboratory. The conclusion illustrates that when signals originating from discrete motor speed, load, and fault severity are used in training, the fault diagnosis is still effective in continuous state. The accuracy is 89% for speed verification and 95% for load verification in simulation and is above 99% in the physical experiment.

关键词： fault diagnosis stators neural nets learning (artificial intelligence) induction motors turn-to-turn short circuit continuous state deep auto-encoder motor stator fault diagnosis system soft-max classifier similar current characteristics traditional auto-encoder three-phase motor current signals different fault types discrete motor speed fault severity

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A robust and accurate indoor localization system using deep auto-encoder combined with multi-feature fusion

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2020年 1-11页

作者： Wang, Qinghu Northeast Univ Coll Comp Sci & Engn Shenyang 110169 Peoples R China Inner Mongolia Univ Nationalities Coll Comp Sci & Technol Tongliao 028000 Peoples R China

Many existing indoor localization systems use RSS as fingerprints, but RSS is a coarse-grained data, which not only fluctuates over time but also is not unique for a specific location due to rich multi-path effects and shadow fading in indoor environments. In order to further improve the localization accuracy, a robust and accurate indoor localization algorithm based on deep auto-encoder network combine with multi-feature fusion. To extract deep features hidden in CSI data and reduce the computational complexity of localization, an improved method is designed to achieve dimension reduction and feature extraction, which can avoid explicit information extraction of available CSI characteristics on the basis of effective representation of CSI fingerprint difference between different locations. Then, the fingerprint database is constructed by combination of RSS and CSI coding. Moreover, the Naive Bayer Classifier is adopted to improve the localization accuracy and stability. In the simulation experiment, the positioning effect of the proposed algorithm under different fingerprint libraries and the positioning effect under different positioning methods are mainly carried out. Experimental results show that the proposed method can effectively perform indoor positioning and has good practicability in the actual production environment.

关键词： Indoor localization deep learning Dimension reduction Multi-feature fusion deep auto-encoder Naive bayer classifier

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A deep auto-encoder satellite anomaly advance warning framework

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AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY 2021年第6期93卷 1085-1096页

作者： Chen, Junfu Zhao, Xiaodong Pi, Dechang Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China

Purpose The purpose of this paper is to ensure the stable operation of satellites in orbit and to assist ground personnel in continuously monitoring the satellite telemetry data and finding anomalies in advance, which can improve the reliability of satellite operation and prevent catastrophic losses. Design/methodology/approach This paper proposes a deep auto-encoder (DAE) satellite anomaly advance warning framework for satellite telemetry data. Firstly, this study performs grey correlation analysis, extracts important feature attributes to construct feature vectors and builds the variational auto-encoder with bidirectional long short-term memory generative adversarial network discriminator (VAE/BLGAN). Then, the Mahalanobis distance is used to measure the reconstruction score of input and output. According to the periodic characteristic of satellite operation, a dynamic threshold method based on periodic time window is proposed. Satellite health monitoring and advance warning are achieved using reconstruction scores and dynamic thresholds. Findings Experiment results indicate DAE methods can probe that satellite telemetry data appear abnormal, trigger a warning before the anomaly occurring and thus allow enough time for troubleshooting. This paper further verifies that the proposed VAE/BLGAN model has stronger data learning ability than other two auto-encoder models and is sensitive to satellite monitoring data. Originality/value This paper provides a DAE framework to apply in the field of satellite health monitoring and anomaly advance warning. To the best of the authors' knowledge, this is the first paper to combine DAE methods with satellite anomaly detection, which can promote the application of artificial intelligence in spacecraft health monitoring.

关键词： Satellite Health monitoring Anomaly advance warning deep auto-encoder Dynamic threshold

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Integrated deep auto-encoder and Q-Learning-Based Scheme to Detect Anomalies and Supporting Forensics in Cloud Computing Environments

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WIRELESS PERSONAL COMMUNICATIONS 2022年第3期127卷 2247-2265页

作者： Savaridassan, P. Maragatham, G. SRM Inst Sci & Technol Sch Comp Dept Informat Technol Chennai Tamil Nadu India

The cloud computing environment when deployed correctly is responsible for delivering scalability, cost efficiency, reliability, security and interoperability to the end users. Log analysis is considered to be an indispensable component of security regulations and framework, since these computer-generated records help the organizations, businesses and networks to respond to different kinds of risks that are possible to cloud environment in a reactive and proactive manner. In this paper, an Integrated deep auto-encoder and Q-learning-based deep Learning (IDEA-QLDL) Scheme is proposed for attaining maximum prediction accuracy during the process of exploring log data and classifying them into genuine and anomalous. It initiates the process of acceptance or denial based on the continuous investigation of behavioral patterns that are highly applicable for classification. The results of the proposed IDEA-QLDL Scheme confirmed its predominance in improving the classification accuracy, precision, recall and detection time compared to the benchmarked schemes considered for investigation.

关键词： Bio-inspired algorithm Q-Learning deep auto-encoder Anomaly detection

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A Wasserstein gradient-penalty generative adversarial network with deep auto-encoder for bearing intelligent fault diagnosis

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MEASUREMENT SCIENCE AND TECHNOLOGY 2020年第4期31卷

作者： Xiong, Xiong Jiang, Hongkai Li, Xingqiu Niu, Maogui Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China

It is a great challenge to manipulate unbalanced fault data in the field of rolling bearings intelligent fault diagnosis. In this paper, a novel intelligent fault diagnosis method called the Wasserstein gradient-penalty generative adversarial network with deep auto-encoder is proposed for intelligent fault diagnosis of rolling bearings. Firstly, the gradient penalty term is added to the Wasserstein generative adversarial network to enhance the stability and convergence of the network. Secondly, a deep auto-encoder network comprised of multiple auto-encoders is regarded as the discriminator. Finally, the sparse auto-encoder is placed at the end of the proposed method as the classifier to classify synthetic bearing faults. The results show that the proposed method has a better performance than traditional methods and the Wasserstein generative adversarial network.

关键词： rolling bearing intelligent fault diagnosis Wasserstein gradient-penalty generative adversarial network deep auto-encoder unsupervised learning

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Driving Maneuver Anomaly Detection Based on deep auto-encoder and Geographical Partitioning

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ACM TRANSACTIONS ON SENSOR NETWORKS 2023年第2期19卷 1-22页

作者： Liu, Miaomiao Yang, Kang Fu, Yanjie Wu, Dapeng Du, Wan Univ Calif Merced Dept Comp Sci & Engn Merced CA 95343 USA Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

This paper presents GeoDMA, which processes the GPS data from multiple vehicles to detect anomalous driving maneuvers, such as rapid acceleration, sudden braking, and rapid swerving. First, an unsupervised deep auto-encoder is designed to learn a set of unique features from the normal historical GPS data of all drivers. We consider the temporal dependency of the driving data for individual drivers and the spatial correlation among different drivers. Second, to incorporate the peer dependency of drivers in local regions, we develop a geographical partitioning algorithm to partition a city into several sub-regions to do the driving anomaly detection. Specifically, we extend the vehicle-vehicle dependency to road-road dependency and formulate the geographical partitioning problem into an optimization problem. The objective of the optimization problem is to maximize the dependency of roads within each sub-region and minimize the dependency of roads between any two different sub-regions. Finally, we train a specific driving anomaly detection model for each sub-region and perform in-situ updating of these models by incremental training. We implement GeoDMA in Pytorch and evaluate its performance using a large real-world GPS trajectories. The experiment results demonstrate that GeoDMA achieves up to 8.5% higher detection accuracy than the baseline methods.

关键词： Anomaly detection deep auto-encoder peer dependency geographical partitioning

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