Energy‐efficient distributed approach for clustering‐based fault detection and diagnosis in image sensor networks

Nowadays wireless technology is playing vital role in the communication system. Wireless Mesh Network (WMN) gives a less expensive alternative to backhauls that can be utilized to give minimal effort get to administrations. Contrasted with regular remote LANs, the advantages of a WMN incorporate more prominent range in light of bundle transferring and higher throughput as a result of shorter bounces. A WMN, be that as it may, might be liable to an assortment of shortcomings that are difficult to analyze physically. This paper presents a survey of the various algorithms used for fault detection, fault diagnosis and fault management in WMNs.

SummaryA fault diagnosis and compensation problem for two‐dimensional discrete time systems with time‐varying state delays is studied in this paper. The concerned two‐dimensional systems are described by the Fornasisi–Marchesini second model and are subject to unknown disturbances. First, a fault detection and diagnosis module is designed to obtain the information on sensor faults; a new fault detection and diagnosis integrated design, using the observer based on descriptor system approach, is proposed to detect and estimate the sensor faults. The integrated design can maximize the fault detection rate for a given false alarm rate. Sufficient conditions for the existence of the integrated fault detection and diagnosis design are derived in the context of norm evaluation and provided in terms of matrix inequalities. Second, a fault‐tolerant control module is proposed upon an existing output feedback controller. When the sensor fault occurs, the faulty measurement can be identified and corrected by the proposed fault detection and diagnosis module. In this case, the feedback controller can guarantee the performance of the closed‐loop system even when encountering sensor faults. Finally, the proposed method is applied to a thermal process to illustrate its effectiveness. Copyright © 2017 John Wiley & Sons, Ltd.

The electrical system fault testing and diagnosis system based on I2C-bus and embedded equipment is proposed. This system is accomplished with the integration of PXI-based computer, field fault testing platform, dedicated interface adapter unit and accessories with software program of virtual instrument and database management and fault guidance system. It can obtain various state parameters of the heavy mechanized bridge by interface adapter unit and corresponding sensors. The dedicated interface adapter unit, that is the core of the hardware platform is composed of chassis, embedded microcomputer controller, I2C-bus data acquisition modules, VPC connection package and military aviation sockets. Because of its flexibility and modular design, it can support different data conversion, for example, analog, digital, and serial, while functioning as an intelligent conversion bridge between field fault testing platform or PXI computer and electrical system of heavy mechanized bridge electrical system. The fault diagnosis software is of hierarchical modular structure with integration of system management control, fault detection and circuit hardware driver modules together with fault repair and diagnosis database. Therefore it provides functions of human-machine interaction, equipment fault detection, fault diagnosis and analysis, as well as repair and maintenance guidance The fault testing and diagnosis system in this work is also featured with handy structure, lightweight, low consumption, large storage capacity, excellent expandability and intelligent inference capability. The system can be used in the online fault testing and diagnosis of similar engineering equipments. So it has important engineering and military application value to the rapid fault testing and fault diagnosis of engineering equipments.

비점오염원은 세정에 의해 하수관 내로 유입되어 수계까지 흘러 수계의 오염부하를 가중시킬 뿐만 아니라, 관의 상태에 따라 누수 및 월류를 야기하여 오염을 일으킨다. 이에 따라 하수관거의 유지관리 및 환경오염 방지를 위한 관거내 하수의 유량, 수질, 불명수 및 월류 등을 효율적으로 관리하기 위한 모니터링의 필요성이 증가하고 있다. 그러나 하수관거의 경우, 지하에 설계되며 그 구조 및 연결이 복잡한 특성으로 인해 실제 하수관거에 대한 모니터링은 쉽지 않다. 본 연구에서는 시스템해석이론에 기초한 하수관망 오염제거 해석과 관망 모니터링 및 이상진단방법을 제시하였다. 먼저 하수관망 공정모사 프로그램인 Stormwater & Wastewater Management Model for expert (XP-SWMM)을 이용하여 관망 내의 오염물질의 거동패턴을 해석하였다. 둘째, 다변량 통계 모니터링을 이용하여 하수관망 내의 수질 모니터링 및 하수관망 유출을 탐지하는 관망 이상 진단을 수행하였다. 정적/동적 상태 시스템에 기초한 하수관망 오염 매카니즘 해석결과, 강우시 총질소와 총인 부하량이 비강우시보다 급격하게 증가함을 확인하였으며, 이는 수계의 오염부하를 가중시킬 것으로 판단되다. 이에 따라 하수관망 내 유출은 강우로 생긴 유량 및 유입오염물질의 농도 증가로 인한 관망이상으로 사료된다. 제안된 하수관망 모니터링 및 이상진단 기법은 도시 유역에서의 비점오염원 관리와 지속적인 모니터링에 있어 효과적으로 사용될 수 있을 것으로 예상된다. Nonpoint source pollution causes leaks and overtopping, depending on the state of the sewer network as well as aggravates the pollution load of the aqueous water system as it is introduced into the sewer by wash-off. According, the need for efficient sewer monitoring system which can manage the sewage flowrate, water quality, inflow/infiltration and overflow has increased for sewer maintenance and the prevention of environmental pollution. However, the sewer monitoring is not easy since the sewer network is built in underground with the complex nature of its structure and connections. Sewer decontamination mechanism as well as pipe network monitoring and fault diagnosis of water network system on system analysis proposed in this study. First, the pollution removal pattern and behavior of contaminants in the sewer pipe network is analyzed by using sewer process simulation program, stormwater & wastewater management model for expert (XP-SWMM). Second, the sewer network fault diagnosis was performed using the multivariate statistical monitoring to monitor water quality in the sewer and detect the sewer leakage and burst. Sewer decontamination mechanism analysis with static and dynamic state system results showed that loads of total nitrogen (TN) and total phosphorous (TP) during rainfall are greatly increased than non-rainfall, which will aggravate the pollution load of the water system. Accordingly, the sewer outflow in pipe network is analyzed due to the increased flow and inflow of pollutant concentration caused by rainfall. The proposed sewer network monitoring and fault diagnosis technique can be used effectively for the nonpoint source pollution management of the urban watershed as well as continuous monitoring system.

In order to meet the requirements of multi-parameter and real-time fault diagnosis of China VI vehicle emission standards diesel engine air system, this paper focuses on the research of the diesel engine air system fault detection and diagnosis method based on deep learning to improve the operational safety and fault diagnosis efficiency. In this paper, the air system fault detection and diagnosis are completed based on the AE model and the CNN model, combined with the actual operation of the diesel engine. Among them, the AE model successfully detected all real-time operation faults, and the false detection rate on the health data was 0.1162%; the CNN model obtained a 90.77% fault diagnosis accuracy rate on the test set of the real-time operation data set. The results show that the model has high accuracy in the diagnosis of diesel engine faults, which is of great significance to the application of deep learning based on big data processing.

Accurate aircraft engine fault detection and diagnosis is vitally important reducing operating costs and improving safety. Various data and knowledge could be collected from manufacture, test bed measurement systems, on-board measurement systems, maintenance history and experts' experience. Integrating and fusing these data and information to provide intelligent fault diagnosis and maintenance schedules are essentially to both civil and military engines. Information fusion strategies and architectures have been developed over the last several years for improving upon the accuracy, robustness and overall effectiveness of diagnostic and prognostic technologies. Fusion of relevant sensor data, maintenance database information, and various diagnostic and prognostic technologies has been proven effective in reducing false alarm rates, increasing confidence levels in early fault detection, and predicting time to failure or degraded condition requiring maintenance action. In this paper, we researched on the architectures of fusion systems. A four-level model was presented to fit the usage in aircraft engines fault detection and diagnosis. To generate the deviations for gas path fault detection, baseline values should be chosen firstly. We analyzed choosing baseline values and generating deviations in detail. Then, the fuzzy set was introduced to descript the degree of symptoms belonging to fault patterns. A method based on fuzzy set to isolate fault and quantify the deterioration of performance of components was presented. We also demonstrated the method with an example. For this method, it is not necessary to have more measurements than fault patterns. The fault diagnosis system based on it is very easy to construct and extend too

The problem of the wind turbine gearbox fault diagnosis was investigated by employing the support vector machine (SVM), which is optimized by the improved fruit fly intelligent algorithm with decreasing steps. First of all, the fault characteristic value extracted by Hilbert transform envelope is presented. Secondly, the general SVM solution to the wind turbine gearbox fault diagnosis problem is presented, where the improved fruit fly intelligent algorithm is adopted to optimize the performance of the model of the fault detection and diagnosis. Thirdly, the effectiveness of three fault diagnosis models are compared, including the fault detection and diagnosis model by the traditional SVM, its improved model optimized by the partical swarm optimization algorithm, and improved model by the SVM optimized by the proposed approach. By using the proposed optimization algorithm, the accuracy of gearbox fault diagnosis is much better than other two models, which are validated by the extensive simulation results based on practical historical operation data.

To solve the problem of fault prediction and diagnosis of household air conditioning, an improved Bayesian network (BN) fault diagnosis model is proposed. Firstly, the orthogonal defect classification (ODC) is used to make statistics and analysis of air conditioning fault data, and the structure of BN fault diagnosis model is built based on the analysis results. Then, genetic algorithm (GA) is used to optimize the conditional probability of network nodes and determine the network parameters. Finally, the cooling and heating failure data of household air conditioning were taken as an example to diagnose. Compared with the traditional BN model, the accuracy of fault diagnosis increases from 81.13% to 92.83%, which verifies the effectiveness of the model.

Applications of Artificial Immune System (AIS) has been widely applied in various engineering, including network security, pattern recognition, combinational optimization, machine learning and fault diagnosis, etc. Fault diagnosis is another AIS application field directly mapped from the theory of immunity after information security and has made certain achievements in research. Real-valued Negative selection algorithms (RNSA) of AIS generate their detector sets based on the points of self data. Self data is regarded as the normal pattern of behavior of the monitored system. This paper provide a new fault detection method based on RNSA of artificial immunity. It can effectively overcome the deficiency of the various fault detection methods of today that cannot implement fault detections because there are only normal samples and not enough fault samples and short of the function of continuous learning. The test result shows that, by increasing a certain number of training samples, the accuracy of fault diagnosis has made great changes. This way has obvious advantage in robustness and accuracy in detection and shows a favorable prospect of application.

The purpose of fault diagnosis in mobile ad hoc network is to have each fault-free node to determine the state of all nodes in the system. This paper proposes a fault diagnosis algorithm based on the approach proposed in for diagnosing nodes in mobile ad hoc network. The proposed diagnosis algorithm is linearly scalable under the assumption that the mobiles may be: (i) crash faulty due to out of range or physical damage and (ii) value faulty due to sending erroneous messages while operating in the field. The parameters such as diagnostic latency and message complexity are used for evaluating the proposed diagnosis algorithm. The result shows that diagnosis latency and message complexity is reduced as compared to cluster based comparison diagnosis algorithm CBCD proposed in.

Angular velocity sensor detection and diagnosis become increasingly essential for the improvement of reliability, safety, and efficiency of the control system on aircraft. The classical methods for fault detection and diagnosis are limit or trend checking of some measurable output variables. Due to they do not give a deeper insight and usually do not allow a fault diagnosis, model-based methods of fault detection and diagnosis were developed by using input and output signals and applying dynamic process models. These approaches are based on parameter estimation, parity equations, or state observers. This paper presents an improvement method to build algorithm fault diagnosis for angular velocity sensors on aircraft. Based on proposed method, results of paper can be used in designed intelligent systems that can automatically fault detection on aircraft.

The present paper proposes a novel data-driven Fault Detection and Diagnosis algorithm for induction motors based on Motor Current Signature Analysis. Principal Component Analysis is used to reduce the three-phase currents space in two dimensions. Then, Kernel Density Estimation is adopted to estimate the Probability Density Function of healthy and of each faulty motors, which will give typical patterns that can be used to identify each fault. Kullback-Leibler divergence is used as an index to identify the dissimilarity between two determined probability distributions, that allows the automatic identification of distinct fault types. Several simulations and experimental results are carried out using two benchmarks in order to verify the effectiveness of the proposed methodology: the first is used to prove appropriateness of the method for air gap eccentricity fault diagnosis and the second is used to prove suitability of the method for rotor broken bars and connectors fault diagnosis. Simulations and classification results prove that the proposed Fault Detection and Diagnosis procedure is able to detect and diagnose different induction motor fault types.

<abstract xmlns="http://www.w3.org/1999/xhtml"> An integrated approach to the fault-tolerant control (FTC) of a quadcopter unmanned aerial vehicle (UAV) with incipient actuator faults is presented. The framework is comprised of a radial basis function neural network (RBFNN) fault detection and diagnosis (FDD) module and a reconfigurable flight controller (RFC) based on the extremum seeking control approach. The dynamics of a quadcopter subject to incipient actuator faults are estimated using a nonlinear identification method comprising a continuous forward algorithm (CFA) and a modified golden section search (GSS) one. A time-difference-of-arrival (TDOA) method and the post-fault system estimates are used within the FDD module to compute the fault location and fault magnitude. The impact of bi-directional uncertainty and FDD detection time on the overall FTC performance and system recovery is assessed by simulating a quadcopter UAV during a trajectory tracking mission and is found to be robust against incipient actuator faults during straight and level flight and tight turns. </abstract>

With the development of big data, artificial intelligence and other technologies, data-driven aviation equipment fault diagnosis and prediction technology has gradually become a research hotspot in the aviation field. Many typical intelligent algorithm models have been applied to this field. However, limited by the airborne embedded computing environment, there are still some problems in the deployment of intelligent prediction models represented by deep neural networks on aircraft. This paper summarizes and analyzes the research and application of typical deep neural networks such as convolutional neural networks in the field of aircraft fault diagnosis and prediction. Facing the airborne embedded environment, the current difficulties in deploying the deep neural network algorithm model in the airborne environment are analyzed. The development direction of the application of fault prediction and diagnosis algorithms represented by neural networks in the future is discussed.

In order to meet the new requirements of fault diagnosis response and intelligent degree in the current computer network, a fault detection of computer communication network based on an improved neural network algorithm is proposed. First, from the perspective of deep learning, based on the KDD99 data set, the network fault diagnosis method based on the convolutional neural network model is studied, and the data conversion operation of grayscale matrixed raw data is proposed. And experiments are carried out, the convolutional neural network structure is designed according to the scale of data features, a series of optimization studies including discarding learning, gradient optimization algorithm, and data enhancement based on this is carried out, and the establishment of the entire fault diagnosis model is completed. The experimental results show that, in the diagnostic model designed in this paper, the Tanh activation function is used in the first fully connected layer to achieve the best convergence speed. During the training process, it can start to converge after about 24 iterations, and the accuracy rate of the model training process can reach 98.1%, verifying the correctness and superiority of the algorithm and model.