Research on GPS Receiver Data Processing Algorithm Based on Wavelet Analysis

Ershen Wang,Tao Pang,Yongming Yang 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.11

Reliability is an essential performance in GPS navigation system. Therefore, satellite fault detection is considered as one of the most important functions in GPS receivers. For the declining problem of the ability of fault detection for the traditional GPS satellite fault detection algorithm under the condition of small fault, a new GPS satellite fault detection algorithm based on wavelet analysis is proposed. The raw pseudorange measurements and the positioning data information are transformed by wavelet analysis and the data jumping point can be detected and identified through the different wavelet scales, and the satellite fault could be detected. Two kinds of GPS satellite fault detection methods are given in detail, and the advantages and disadvantages of them are compared. Validated by the real collected raw data from the GPS receiver, the results show that the wavelet analysis method can detect smaller mutations in a sequence of parameters, and the feasibility and effectiveness of applying the wavelet analysis algorithm in satellite fault detection for GPS receiver are verified.

Comparative Study of Three Fault Diagnostic Methods for Three Phase Inverter with Induction Motor

Furqan Asghar,Muhammad Talha,Sung Ho Kim 한국지능시스템학회 2017 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.17 No.4

In recent times, inverters are considered as the basic building block in an electrical drive system used widely in many industrial drive applications. However, the reliability of these inverters is mainly affected by the failure of power electronic switches. Various faults in inverter may influence the system operation by unexpected maintenance, which increases the cost factor and reduce overall efficiency. In this paper, comparative study of three different fault detection and diagnosis systems for three phase inverter is presented. The basic purpose of these fault detection and diagnosis systems is to detect single or multiple faults efficiently. These techniques rely on the neural network for fault detection and diagnosis by using Clarke transformed two-dimensional features extraction, three-dimensional features extraction and features extraction using discrete wavelet transform (DWT) with a different number of features in each technique. Several features are extracted using different mechanisms and used in the neural network as input for fault detection and diagnosis. Furthermore, a simulation study is carried out to analyze the fault detection and diagnosis response of these techniques. Also, a comparative study has been performed by considering fault detection time and accuracy. Comparison results prove the supremacy of three-dimensional feature extraction technique over other two techniques as it can detect and diagnose single, double and triple faults in a single cycle with high accuracy as compared to other two techniques multi-cycles detection.

Satellite Fault Detection and Isolation Scheme with Modified Adaptive Fading EKF

Jun Kyu Lim,Chan Gook Park 대한전기학회 2014 Journal of Electrical Engineering & Technology Vol.9 No.4

This paper presents a modified adaptive fading EKF (AFEKF) for sensor fault detection and isolation in the satellite. Also, the fault detection and isolation (FDI) scheme is developed in three phases. In the first phase, the AFEKF is modified to increase sensor fault detection performance. The sensor fault detection and sensor selection method are proposed. In the second phase, the IMM filer with scalar penalty is designed to detect wherever actuator faults occur. In the third phase of the FDI scheme, the sub-IMM filter is designed to identify the fault type which is either the total or partial fault. An important feature of the proposed FDI scheme can decrease the number of filters for detecting sensor fault. Also, the proposed scheme can classify fault detection and isolation as well as fault type identification.

저궤도 인공위성의 센서 및 구동기 통합 고장검출 및 분리 기법

임준규(Jun Kyu Lim),이준한(Junhan Lee),박찬국(Chan Gook Park) 제어로봇시스템학회 2011 제어·로봇·시스템학회 논문지 Vol.17 No.11

An integrated fault detection and isolation method is proposed in this paper. The main objective of this paper is development fault detection, isolation and diagnosis algorithm based on the DKF (Decentralized Kalman Filter) and the bank of IMM (Interacting Multiple Model) filters using penalty scalar for both partial and total faults and the outlier detection algorithm for preventing false alarm also included. The proposed FDI (Fault Detection and Isolation) scheme is developed in four phases. In the first phase, the outlier detection filter is designed to prevent false alarm as a pre-filter. In the second phases, two local filters and master filter are designed to detect sensor faults. In the third phases, the proposed FDI scheme checks sensor residual to isolate sensor faults and 11 EKFs actuator fault models are designed to detect wherever actuator faults occur. In the last phases, four filters are designed to identify the fault type which is either the total fault or partial fault. The developed scheme can deal with not only sensor and actuator faults, but also preventing false alarm. An important feature of the proposed FDI scheme can decreases fault isolation time and figure out not only fault detection and isolation but also fault type identification. To verify the proposed FDI algorithm performance, the Simulator is also developed under the Matlab/Simulink environment.

Fault-weighted quantification method of fault detection coverage through fault mode and effect analysis in digital I&C systems

Cho, J.,Lee, S.J.,Jung, W. North-Holland Pub. Co 2017 Nuclear engineering and design Vol.316 No.-

<P>The one of the most outstanding features of a digital I&C system is the use of a fault-tolerant technique. With an awareness regarding the importance of thequantification of fault detection coverage of fault-tolerant techniques, several researches related to the fault injection method were developed and employed to quantify a fault detection coverage. In the fault injection method, each injected fault has a different importance because the frequency of realization of every injected fault is different. However, there have been no previous studies addressing the importance and weighting factor of each injected fault. In this work, a new method for allocating the weighting to each injected fault using the failure mode and effect analysis data was proposed. For application, the fault-weighted quantification method has also been applied to specific digital reactor protection system to quantify the fault detection coverage. One of the major findings in an application was that we may estimate the unavailability of the specific module in digital I&C systems about 20-times smaller than real value when we use a traditional method. The other finding was that we can also classify the importance of the experimental case. Therefore, this method is expected to not only suggest an accurate quantification procedure of fault-detection coverage by weighting the injected faults, but to also contribute to an effective fault injection experiment by sorting the importance of the failure categories. (C) 2017 Elsevier B.V. All rights reserved.</P>

Fault Detection and Classification with Optimization Techniques for a Three-Phase Single-Inverter Circuit

Gomathy, V.,Selvaperumal, S. The Korean Institute of Power Electronics 2016 JOURNAL OF POWER ELECTRONICS Vol.16 No.3

Fault detection and isolation are related to system monitoring, identifying when a fault has occurred, and determining the type of fault and its location. Fault detection is utilized to determine whether a problem has occurred within a certain channel or area of operation. Fault detection and diagnosis have become increasingly important for many technical processes in the development of safe and efficient advanced systems for supervision. This paper presents an integrated technique for fault diagnosis and classification for open- and short-circuit faults in three-phase inverter circuits. Discrete wavelet transform and principal component analysis are utilized to detect the discontinuity in currents caused by a fault. The features of fault diagnosis are then extracted. A fault dictionary is used to acquire details about transistor faults and the corresponding fault identification. Fault classification is performed with a fuzzy logic system and relevance vector machine (RVM). The proposed model is incorporated with a set of optimization techniques, namely, evolutionary particle swarm optimization (EPSO) and cuckoo search optimization (CSO), to improve fault detection. The combination of optimization techniques with classification techniques is analyzed. Experimental results confirm that the combination of CSO with RVM yields better results than the combinations of CSO with fuzzy logic system, EPSO with RVM, and EPSO with fuzzy logic system.

Fault Detection and Classification with Optimization Techniques for a Three-Phase Single-Inverter Circuit

V. Gomathy,S. Selvaperumal 전력전자학회 2016 JOURNAL OF POWER ELECTRONICS Vol.16 No.3

Fault detection and isolation are related to system monitoring, identifying when a fault has occurred, and determining the type of fault and its location. Fault detection is utilized to determine whether a problem has occurred within a certain channel or area of operation. Fault detection and diagnosis have become increasingly important for many technical processes in the development of safe and efficient advanced systems for supervision. This paper presents an integrated technique for fault diagnosis and classification for open- and short-circuit faults in three-phase inverter circuits. Discrete wavelet transform and principal component analysis are utilized to detect the discontinuity in currents caused by a fault. The features of fault diagnosis are then extracted. A fault dictionary is used to acquire details about transistor faults and the corresponding fault identification. Fault classification is performed with a fuzzy logic system and relevance vector machine (RVM). The proposed model is incorporated with a set of optimization techniques, namely, evolutionary particle swarm optimization (EPSO) and cuckoo search optimization (CSO), to improve fault detection. The combination of optimization techniques with classification techniques is analyzed. Experimental results confirm that the combination of CSO with RVM yields better results than the combinations of CSO with fuzzy logic system, EPSO with RVM, and EPSO with fuzzy logic system.

Performance Comparison of GPS Fault Detection and Isolation via Pseudorange Prediction Model based Test Statistics

Jangsik Yoo,Jongsun Ahn,Young Jae Lee,Sangkyung Sung 대한전기학회 2012 Journal of Electrical Engineering & Technology Vol.7 No.5

Fault detection and isolation (FDI) algorithms provide fault monitoring methods in GPS measurement to isolate abnormal signals from the GPS satellites or the acquired signal in receiver. In order to monitor the occurred faults, FDI generates test statistics and decides the case that is beyond a designed threshold as a fault. For such problem of fault detection and isolation, this paper presents and evaluates position domain integrity monitoring methods by formulating various pseudorange prediction methods and investigating the resulting test statistics. In particular, precise measurements like carrier phase and Doppler rate are employed under the assumption of fault free carrier signal. The presented position domain algorithm contains the following process; first a common pseudorange prediction formula is defined with the proposed variations in pseudorange differential update. Next, a threshold computation is proposed with the test statistics distribution considering the elevation angle. Then, by examining the test statistics, fault detection and isolation is done for each satellite channel. To verify the performance, simulations using the presented fault detection methods are done for an ideal and real fault case, respectively.

Fault Detection and Diagnosis of Winding Short in BLDC Motors Based on Fuzzy Similarity

Hyeon Bae,Sungshin Kim,George Vachtsevanos 한국지능시스템학회 2009 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.9 No.2

The tum-to-tum short is one major fault of the motor faults of BLDC motors and can appear frequently. When the fault happens, the motor can be operated without breakdown, but it is necessary to maintain the motor for continuous working. In past research, several methods have been applied to detect winding faults. The representative approaches have been focusing on current signals, which can give important information to extract features and to detect faults. In this study, current sensors were installed to measure signals for fault detection of BLDC motors. In this study, the Park's vector method was used to extract the features and to isolate the faults from the current measured by sensors. Because this method can consider the three-phase current values, it is useful to detect features from one-phase and three-phase faults. After extracting two-dimensional features, the final feature was generated by using the two-dimensional values using the distance equation. The values were used in fuzzy similarity to isolate the faults. Fuzzy similarity is an available tool to diagnose the fault without model generation and the fault was converted to the percentage value that can be considered as possibility of the fault.

Fault detection and diagnosis algorithms for an open-cycle liquid propellant rocket engine using the Kalman filter and fault factor methods

Lee, Kyelim,Cha, Jihyoung,Ko, Sangho,Park, Soon-Young,Jung, Eunhwan Elsevier 2018 Acta astronautica Vol.150 No.-

<P><B>Abstract</B></P> <P>This paper deals with applications of fault detection and diagnosis algorithms based on the Kalman filter and fault factor method for an open-cycle liquid propellant rocket engine in a steady state. In order to develop the algorithms, we designed the Kalman filter with a linearized model of a liquid propellant rocket engine and energy balance equations with fault factors. To confirm the fault detection and diagnosis algorithms, we developed a mathematical model of an open-cycle liquid propellant rocket engine and artificially injected various faults, such as decreasing turbine efficiency and a sensor fault. We then executed the fault detection and diagnosis algorithms and analyzed the results of each algorithm. These processes are numerically demonstrated for the open-cycle liquid propellant rocket engine at full thrust level by using the simulated measurement data of the liquid propellant rocket engine mathematical model.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A mathematical model of an open-cycle liquid propellant rocket engine (OCLPRE) is developed. </LI> <LI> Kalman filter and fault factor methods are used for fault detection-diagnosis of the OCLPRE. </LI> <LI> The pros and cons of each fault detection-diagnosis algorithms are analyzed. </LI> <LI> The performances of the algorithms are demonstrated by numerical simulations. </LI> </UL> </P>