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Based on Soft Competition ART Neural Network Ensemble and Its Application to the Fault Diagnosis of Bearing

MATHEMATICAL PROBLEMS IN ENGINEERING 2017年第1期2017卷 1-11页

作者： Yang, Dan Mu, Hailin Xu, Zengbing Wang, Zhigang Yi, Cancan Liu, Changming Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Wuhan Univ Sci & Technol State Key Lab Refractories & Met Wuhan 430081 Peoples R China

This paper presents a novel method for fault diagnosis based on an improved adaptive resonance theory (ART) neural network and ensemble technique. The method consists of three stages. Firstly, the improved ART neural network is comprised of the soft competition technique based on fuzzy competitive learning (FCL) and ART based on Yu's norm, the neural nodes in the competition layer are trained according to the degree of membership between the mode node and the input, and then fault samples are classified in turn. Secondly, with the distance evaluation technique, the optimal features are obtained from the statistical characteristics of original signals and wavelet coefficients. Finally, the optimal features are input into the neural network ensemble (NNE) based on voting method to identify the different fault categories. The proposed method is applied to the fault diagnosis of rolling element bearings, and testing results show that the neural network ensemble can reliably classify different fault categories and the degree of faults, which has a better classification performance compared with the single neural network.

关键词： NEURAL networks (computer science) debugging in computer science BEARINGS (Machinery) WAVELETS (Mathematics) PERFORMANCE evaluation

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Bearing Fault Detection by One-Dimensional Convolutional Neural Networks

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MATHEMATICAL PROBLEMS IN ENGINEERING 2017年第1期2017卷 1-9页

作者： Eren, Levent Izmir Univ Econ Dept Elect & Elect Engn Sakarya Cad 156 TR-35330 Izmir Turkey

Bearing faults are the biggest single source of motor failures. Artificial Neural Networks (ANNs) and other decision support systems are widely used for early detection of bearing faults. The typical decision support systems require feature extraction and classification as two distinct phases. Extracting fixed features each time may require a significant computational cost preventing their use in real-time applications. Furthermore, the selected features for the classification phase may not represent the most optimal choice. In this paper, the use of 1D Convolutional Neural Networks (CNNs) is proposed for a fast and accurate bearing fault detection system. The feature extraction and classification phases of the bearing fault detection are combined into a single learning body with the implementation of 1D CNN. The raw vibration data (signal) is fed into the proposed system as input eliminating the need for running a separate feature extraction algorithm each time vibration data is analyzed for classification. Implementation of 1D CNNs results in more efficient systems in terms of computational complexity. The classification performance of the proposed system with real bearing data demonstrates that the reduced computational complexity is achieved without a compromise in fault detection accuracy.

关键词： NEURAL networks (computer science) debugging in computer science MOTOR failure DECISION support systems FEATURE extraction (Data processing)

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Fault Detection Modelling and Analysis in Wireless Sensor Network

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JOURNAL OF SENSORS 2018年第10-17期2018卷 1-9页

作者： Jia, Shuang Ma, Lin Qin, Danyang Harbin Inst Technol Sch Elect & Informat Engn Harbin 150080 Heilongjiang Peoples R China Heilongjiang Univ Dept Commun Engn Harbin 150080 Heilongjiang Peoples R China

For the serious impacts of network failure caused by the unbalanced energy consumption of sensor nodes, hardware failure, and attacker intrusion on data transmission, a low-energy-consumption distributed fault detection mechanism in a wireless sensor network (LEFD) is proposed in this paper. The time correlation information of nodes is used to detect fault nodes in LEFD firstly, and then the spatial correlation information is adopted to detect the remaining fault nodes, so as to check the states of nodes comprehensively and improve the efficiency of data transmission. In addition, the nodes do not need to exchange information with their neighbor nodes in the detection process since LEFD uses the data sensed by the node itself to detect some types of faults, thus reducing the energy consumption of nodes effectively. Performance analysis and simulation results show that the proposed detection mechanism can improve the transmission performance and reduce the energy consumption of the network effectively.

关键词： debugging in computer science Wireless sensor networks Energy consumption Correlation (Statistics) Data transmission systems

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Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis

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JOURNAL OF CONTROL science AND ENGINEERING 2017年第2-20期2017卷 1-8页

作者： Xu, Xiaoming Wen, Chenglin Hangzhou Dianzi Univ Sch Automat Hangzhou 310018 Zhejiang Peoples R China

In traditional principle component analysis (PCA), because of the neglect of the dimensions influence between different variables in the system, the selected principal components (PCs) often fail to be representative. While the relative transformation PCA is able to solve the above problem, it is not easy to calculate the weight for each characteristic variable. In order to solve it, this paper proposes a kind of fault diagnosis method based on information entropy and Relative Principle Component Analysis. Firstly, the algorithm calculates the information entropy for each characteristic variable in the original dataset based on the information gain algorithm. Secondly, it standardizes every variable's dimension in the dataset. And, then, according to the information entropy, it allocates the weight for each standardized characteristic variable. Finally, it utilizes the relative-principal-components model established for fault diagnosis. Furthermore, the simulation experiments based on Tennessee Eastman process and Wine datasets demonstrate the feasibility and effectiveness of the new method.

关键词： PRINCIPAL components analysis ENTROPY (Information theory) debugging in computer science ALGORITHMS VARIABLES (Logic)

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Study and Verification of a Physical Simulation System for Formation Pressure Testing while Drilling

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GEOFLUIDS 2018年第1期2018卷 18-18页

作者： Ma, Tianshou Peng, Nian Chen, Ping Yang, Chunhe Wang, Xingming Han, Xiong Southwest Petr Univ State Key Lab Oil & Gas Reservoir Geol & Exploita Chengdu 610500 Sichuan Peoples R China Chinese Acad Sci Inst Rock & Soil Mech State Key Lab Geomech & Geotech Engn Wuhan 430071 Hubei Peoples R China Drilling & Prod Engn Technol Res Inst CCDC Guanghan 618300 Sichuan Peoples R China

Based on a brief overview on the determination methods of formation pressure and their features, the major principle of formation pressure testing while drilling (FPTWD) and existing physical simulation systems was introduced, and the deficiency of the existing physical simulation systems was also discussed. A laboratory high-precision physical simulation system was therefore developed to simulate the downhole testing environment and testing process of FPTWD. The present experimental system was designed to endure pressures up to 20,000 psi, and the relative control accuracy of pressure is approximately 0.02% FS. Two kinds of man-made specimens with the permeability of 10-110mD were used to test the pressure response and to verify the present physical simulation system. The debugging results indicated that the variation amplitude under the stable condition is approximately 0.07 psi, 0.08 psi, 0.11 psi, and 0.11 kN for the annular pressure, pore pressure, confining pressure, and thrust force, respectively. Thus, the high control accuracy is approximately +/- 1.0 psi under the stable conditions. The experimental results indicated that the pressure drawdown declines rapidly in the stage of withdrawing formation fluids and then recovers slowly. The pressure drop amplitude also decreases with permeability, while the pressure buildup amplitude increases with permeability. The tendency of pressure change is nearly the same for both the present and the previous systems, and the pressure curve of the present system is much smoother and better than that of the previous system. The relative error of explaining formation pressure is less than 1% and 4% for the present and the previous systems, respectively. In addition, this physical simulation system has important applications to verify the interpretation model, to analyze pressure response recorded by FPTWD tools, to test the capability and design of FPTWD tools, and to calibrate the formation pressure, formation parameters, a

关键词： RESERVOIR oil pressure DRILLING & boring machinery debugging in computer science computer simulation PROSPECTING

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Architecture Level Safety Analyses for Safety-Critical Systems

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INTERNATIONAL JOURNAL OF AEROSPACE ENGINEERING 2017年第1期2017卷 6143727-6143727页

作者： Kushal, K. S. Nanda, Manju Jayanthi, J. CSIR Natl Aerosp Labs Aerosp Elect & Syst Div Bangalore Karnataka India

The dependency of complex embedded Safety-Critical Systems across Avionics and Aerospace domains on their underlying software and hardware components has gradually increased with progression in time. Such application domain systems are developed based on a complex integrated architecture, which is modular in nature. Engineering practices assured with system safety standards to manage the failure, faulty, and unsafe operational conditions are very much necessary. System safety analyses involve the analysis of complex software architecture of the system, a major aspect in leading to fatal consequences in the behaviour of Safety-Critical Systems, and provide high reliability and dependability factors during their development. In this paper, we propose an architecture fault modeling and the safety analyses approach that will aid in identifying and eliminating the design flaws. The formal foundations of SAE Architecture Analysis & Design Language (AADL) augmented with the Error Model Annex (EMV) are discussed. The fault propagation, failure behaviour, and the composite behaviour of the design flaws/failures are considered for architecture safety analysis. The illustration of the proposed approach is validated by implementing the Speed Control Unit of Power-Boat Autopilot (PBA) system. The Error Model Annex (EMV) is guided with the pattern of consideration and inclusion of probable failure scenarios and propagation of fault conditions in the Speed Control Unit of Power-Boat Autopilot (PBA). This helps in validating the system architecture with the detection of the error event in the model and its impact in the operational environment. This also provides an insight of the certification impact that these exceptional conditions pose at various criticality levels and design assurance levels and its implications in verifying and validating the designs.

关键词： AVIONICS AERONAUTICS software RELIABILITY (Engineering) HARDWARE debugging in computer science

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Rotating Machinery Fault Diagnosis for Imbalanced Data Based on Fast Clustering Algorithm and Support Vector Machine

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JOURNAL OF SENSORS 2017年第10-22期2017卷 8092691-8092691页

作者： Zhang, Xiaochen Jiang, Dongxiang Han, Te Wang, Nanfei Yang, Wenguang Yang, Yizhou Tsinghua Univ Dept Thermal Engn State Key Lab Power Syst Beijing 100084 Peoples R China

To diagnose rotating machinery fault for imbalanced data, a method based on fast clustering algorithm (FCA) and support vector machine (SVM) was proposed. Combined with variationalmode decomposition (VMD) and principal component analysis (PCA), sensitive features of the rotating machinery fault were obtained and constituted the imbalanced fault sample set. Next, a fast clustering algorithm was adopted to reduce the number of the majority data from the imbalanced fault sample set. Consequently, the balanced fault sample set consisted of the clustered data and the minority data from the imbalanced fault sample set. After that, SVM was trained with the balanced fault sample set and tested with the imbalanced fault sample set so the fault diagnosis model of the rotating machinery could be obtained. Finally, the gearbox fault data set and the rolling bearing fault data set were adopted to test the fault diagnosis model. The experimental results showed that the fault diagnosis model could effectively diagnose the rotating machinery fault for imbalanced data.

关键词： Rotating machinery debugging in computer science Machine learning Support vector machines Classification algorithms Mechanical engineering

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Testing noninterference, quickly

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JOURNAL OF FUNCTIONAL PROGRAMMING 2016年第0期26卷 e4-000页

作者： Hritcu, Catalin Lampropoulos, Leonidas Spector-Zabusky, Antal De Amorim, Arthur Azevedo Denes, Maxime Hughes, John Pierce, Benjamin C. Vytiniotis, Dimitrios Inria Paris Prosecco Team Paris France Univ Penn Dept Comp & Informat Sci 200 S 33Rd St Philadelphia PA 19104 USA Inria Paris Gallium Team Paris France Chalmers Univ Comp Sci & Engn Gothenburg Sweden Microsoft Res Programming Principles & Tools Grp Cambridge England

Information-flow control mechanisms are difficult both to design and to prove correct. To reduce the time wasted on doomed proof attempts due to broken definitions, we advocate modern random-testing techniques for finding counterexamples during the design process. We show how to use QuickCheck, a property-based random-testing tool, to guide the design of increasingly complex information-flow abstract machines, leading up to a sophisticated register machine with a novel and highly permissive flow-sensitive dynamic enforcement mechanism that is sound in the presence of first-class public labels. We find that both sophisticated strategies for generating well-distributed random programs and readily falsifiable formulations of noninterference properties are critically important for efficient testing. We propose several approaches and evaluate their effectiveness on a collection of injected bugs of varying subtlety. We also present an effective technique for shrinking large counterexamples to minimal, easily comprehensible ones. Taken together, our best methods enable us to quickly and automatically generate simple counterexamples for more than 45 bugs. Moreover, we show how testing guides the discovery of the sophisticated invariants needed for the noninterference proof of our most complex machine.

关键词： INFORMATION theory FLOW control (Data transmission systems) MACHINE theory debugging in computer science DISTRIBUTED computing

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Aero Engine Component Fault Diagnosis Using Multi-Hidden-Layer Extreme Learning Machine with Optimized Structure

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INTERNATIONAL JOURNAL OF AEROSPACE ENGINEERING 2016年第1期2016卷 1-11页

作者： Pang, Shan Yang, Xinyi Zhang, Xiaofeng Ludong Univ Coll Informat & Elect Engn Yantai 264025 Peoples R China Naval Aeronaut & Astronaut Univ Dept Aircraft Engn Yantai 264001 Peoples R China

A new aero gas turbine engine gas path component fault diagnosis method based on multi-hidden-layer extreme learning machine with optimized structure (OM-ELM) was proposed. OM-ELM employs quantum-behaved particle swarm optimization to automatically obtain the optimal network structure according to both the root mean square error on training data set and the norm of output weights. The proposed method is applied to handwritten recognition data set and a gas turbine engine diagnostic application and is compared with basic ELM, multi-hidden-layer ELM, and two state-of-the-art deep learning algorithms: deep belief network and the stacked denoising autoencoder. Results show that, with optimized network structure, OM-ELM obtains better test accuracy in both applications and is more robust to sensor noise. Meanwhile it controls the model complexity and needs far less hidden nodes than multi-hidden-layer ELM, thus saving computer memory and making it more efficient to implement. All these advantages make our method an effective and reliable tool for engine component fault diagnosis tool.

关键词： DEEP learning (Machine learning) GAS turbines SIGNAL denoising debugging in computer science MATHEMATICAL optimization

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Fault detection filter design for switched systems with quantization effects

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2016年第11期353卷 2431-2450页

作者： Li, Jian Park, Ju H. Northeast Dianli Univ Sch Automat Engn Jilin 132012 Jilin Peoples R China Yeungnam Univ Dept Elect Engn Nonlinear Dynam Grp 280 Daehak Ro Kyongsan 38541 South Korea

The problem of fault detection for switched systems with quantization effects is investigated in this paper. The dynamic quantizer introduced here is composed of a dynamic scaling and a static quantizer. Subsequently, a novel fault detection scheme, which fully considers the static quantizer range and quantizer error, is proposed. Furthermore, sufficient conditions for fault detection filter are given in the framework of linear matrix inequality, and the filter gains and the static quantizer range are obtained by a convex optimized problem. Finally, the presented technique is validated by two examples, and simulation results indicate that the proposed method can effectively detect the fault. (C) 2016 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

关键词： debugging in computer science computer simulation LINEAR matrix inequalities QUANTIZATION (Signal procesing) CONVEX functions

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