Fault Diagnosis of DC Microgrid Based on Residual Generator

This paper presents an approach for data-driven design of fault diagnosis system. The proposed fault diagnosis scheme consists of an adaptive residual generator and a bank of isolation observers, whose parameters are directly identified from the process data without identification of complete process model. To deal with normal variations in the process, the parameters of residual generator are online updated by standard adaptive technique to achieve reliable fault detection performance. After a fault is successfully detected, the isolation scheme will be activated, in which each isolation observer serves as an indicator corresponding to occurrence of a particular type of fault in the process. The thresholds can be determined analytically or through estimating the probability density function of related variables. To illustrate the performance of proposed fault diagnosis approach, a laboratory-scale three-tank system is finally utilized. It shows that the proposed data-driven scheme is efficient to deal with applications, whose analytical process models are unavailable. Especially, for the large-scale plants, whose physical models are generally difficult to be established, the proposed approach may offer an effective alternative solution for process monitoring.

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Fault Diagnosis With a Model-Based Recurrent Neural Network

10.1115/imece2000-2327 ◽

2000 ◽

Author(s):

Chengyu Gan ◽

Kourosh Danai

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Recurrent Neural Network ◽

State Space Model ◽

Black Box ◽

Fault Detection And Isolation ◽

Model Based ◽

Starting Point ◽

Residual Generator ◽

Structure Assignment

Abstract The utility of a model-based recurrent neural network (MBRNN) is demonstrated in fault diagnosis. The MBRNN can be formatted according to a state-space model. Therefore, it can use model-based fault detection and isolation (FDI) solutions as a starting point, and improve them via training by adapting them to plant nonlinearities. In this paper, the application of MBRNN to the IFAC Benchmark Problem is explored and its performance is compared with ‘black box’ neural network solutions. For this problem, the MBRNN is formulated according to the Eigen-Structure Assignment (ESA) residual generator developed by Jorgensen et al. [1]. The results indicate that the MBRNN provides better results than ‘black box’ neural networks, and that with training it can perform better than the ESA residual generator.

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LPV Model Based Sensor Fault Diagnosis and Isolation for Permanent Magnet Synchronous Generator in Wind Energy Conversion Systems

Applied Sciences ◽

10.3390/app8101816 ◽

2018 ◽

Vol 8 (10) ◽

pp. 1816 ◽

Cited By ~ 5

Author(s):

Zhimin Yang ◽

Yi Chai ◽

Hongpeng Yin ◽

Songbing Tao

Keyword(s):

Fault Diagnosis ◽

Permanent Magnet ◽

Synchronous Generator ◽

Fault Estimation ◽

Linear Matrix ◽

Sensor Faults ◽

Sensor Fault ◽

Permanent Magnet Synchronous Generator ◽

Multiple Sensor ◽

Residual Generator

This paper deals with the current sensor fault diagnosis and isolation (FDI) problem for a permanent magnet synchronous generator (PMSG) based wind system. An observer based scheme is presented to detect and isolate both additive and multiplicative faults in current sensors, under varying torque and speed. This scheme includes a robust residual generator and a fault estimation based isolator. First, the PMSG system model is reformulated as a linear parameter varying (LPV) model by incorporating the electromechanical dynamics into the current dynamics. Then, polytopic decomposition is introduced for H ∞ design of an LPV residual generator and fault estimator in the form of linear matrix inequalities (LMIs). The proposed gain-scheduled FDI is capable of online monitoring three-phase currents and isolating multiple sensor faults by comparing the diagnosis variables with the predefined thresholds. Finally, a MATLAB/SIMULINK model of wind conversion system is established to illustrate FDI performance of the proposed method. The results show that multiple sensor faults are isolated simultaneously with varying input torque and mechanical power.

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A Model-Based Sensor Fault Diagnosis Scheme for Batteries in Electric Vehicles

Energies ◽

10.3390/en14040829 ◽

2021 ◽

Vol 14 (4) ◽

pp. 829

Author(s):

Quanqing Yu ◽

Changjiang Wan ◽

Junfu Li ◽

Rui Xiong ◽

Zeyu Chen

Keyword(s):

Fault Diagnosis ◽

Open Circuit Voltage ◽

Fault Tolerant ◽

Fault Tolerant Control ◽

Open Circuit ◽

Sensor Data ◽

Battery Management System ◽

Sensor Fault ◽

Model Based ◽

Residual Generator

The implementation of each function of a battery management system (BMS) depends on sensor data. Efficient sensor fault diagnosis is essential to the durability and safety of battery systems. In this paper, a model-based sensor fault diagnosis scheme and fault-tolerant control strategy for a voltage sensor and a current sensor are proposed with recursive least-square (RLS) and unscented Kalman filter (UKF) algorithms. The fault diagnosis scheme uses an open-circuit voltage residual generator and a capacity residual generator to generate multiple residuals. In view of the different applicable state of charge (SOC) intervals of each residual, different residuals need to be selected according to the different SOC intervals to evaluate whether a sensor fault occurs during residual evaluation. The fault values of the voltage and current sensors are derived in detail based on the open-circuit voltage residual and the capacity residual, respectively, and applied to the fault-tolerant control of battery parameters and state estimations. The performance of the proposed approaches is demonstrated and evaluated by simulations with MATLAB and experimental studies with a commercial lithium-ion battery cell.

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Residual generator fuzzy identification for automotive diesel engine fault diagnosis

International Journal of Applied Mathematics and Computer Science ◽

10.2478/amcs-2013-0032 ◽

2013 ◽

Vol 23 (2) ◽

pp. 419-438 ◽

Cited By ~ 9

Author(s):

Silvio Simani

Keyword(s):

Fault Diagnosis ◽

Diesel Engine ◽

Control Strategies ◽

Direct Consequence ◽

Fault Detection And Isolation ◽

Fuzzy Identification ◽

Overall Performance ◽

Residual Generator ◽

Residual Generation

Safety in dynamic processes is a concern of rising importance, especially if people would be endangered by serious system failure. Moreover, as the control devices which are now exploited to improve the overall performance of processes include both sophisticated control strategies and complex hardware (input-output sensors, actuators, components and processing units), there is an increased probability of faults. As a direct consequence of this, automatic supervision systems should be taken into account to diagnose malfunctions as early as possible. One of the most promising methods for solving this problem relies on the analytical redundancy approach, in which residual signals are generated. If a fault occurs, these residual signals are used to diagnose the malfunction. This paper is focused on fuzzy identification oriented to the design of a bank of fuzzy estimators for fault detection and isolation. The problem is treated in its different aspects covering the model structure, the parameter identification method, the residual generation technique, and the fault diagnosis strategy. The case study of a real diesel engine is considered in order to demonstrate the effectiveness the proposed methodology.

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Fault Diagnosis Based Approach to Protecting DC Microgrid Using Machine Learning Technique

Procedia Computer Science ◽

10.1016/j.procs.2017.09.019 ◽

2017 ◽

Vol 114 ◽

pp. 449-456 ◽

Cited By ~ 6

Author(s):

Ibrahim Almutairy ◽

Marwan Alluhaidan

Keyword(s):

Machine Learning ◽

Fault Diagnosis ◽

Machine Learning Technique ◽

Dc Microgrid ◽

Learning Technique

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Event-based weighted residual generator design via non-PDC scheme for fault diagnosis in T–S fuzzy systems

Journal of the Franklin Institute ◽

10.1016/j.jfranklin.2018.02.011 ◽

2018 ◽

Vol 355 (6) ◽

pp. 3145-3167 ◽

Cited By ~ 3

Author(s):

Xiao-Lei Wang ◽

Guang-Hong Yang

Keyword(s):

Fault Diagnosis ◽

Fuzzy Systems ◽

Residual Generator ◽

Event Based ◽

Generator Design

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Fuzzy Logic Based Modeling in the Complex System Fault Diagnosis

Knowledge-Based Intelligent System Advancements ◽

10.4018/978-1-61692-811-7.ch006 ◽

2011 ◽

pp. 108-128

Author(s):

Miroslav Pokorný ◽

Pavel Fojtík

Keyword(s):

Complex System ◽

Decision Making ◽

Fault Diagnosis ◽

Kalman Filters ◽

Fuzzy Model ◽

Ratio Test ◽

Linear Dynamic ◽

Linear Subsystem ◽

Residual Generator ◽

Takagi Sugeno

This chapter deals with the model-based fault diagnosis approaches that exploit the fuzzy modeling approximation abilities to obtain the appropriate model of the monitored system. This technique makes use of the Takagi-Sugeno fuzzy model to describe the non-linear dynamic system by its decomposition onto number of linear submodels, so that it is possible to overcome difficulties in conventional methods for dealing with nonlinearity. A linear residual generator formed by Kalman filters which are designed for the each of the linear subsystem is then proposed to generate diagnostic signals - residuals. Since the task is formulated on a statistical basis, the generalized likelihood ratio test is chosen as a decision-making algorithm. Finally, two practical examples are presented to demonstrate the applicability of the proposed approach.

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