A Signal-Based Fault Detection and Tolerance Control Method of Current Sensor for PMSM Drive

Wu, Congcong; Guo, Chongshen; Xie, Zongwu; Ni, Fenglei; Liu, Hong

doi:10.1109/tie.2018.2813991

Cited by 104 publications

(50 citation statements)

References 19 publications

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“…Based on the simulation and analysis of the above three modes of failure, the effectiveness of the fault diagnosis and active fault-tolerant control strategy proposed in this paper is verified. According to the above waveforms and data, it is not difficult to find that from the perspective of control objective, both the composite sliding mode observer control system proposed in this paper and the multi-observer control system based on [10,12] can realize diagnosis and fault-tolerant control under current and speed sensor faults, as shown in Figure 9a,b and Figure 10a,b. From the perspective of control accuracy, the composite sliding mode observer control system has higher precision and lower probability of misdiagnosis or missed diagnosis, and thus reducing the chattering phenomenon of the system considerably, as shown in Figure 9c,d, Figure 10c,d, Figures 11a and 10d.…”

Section: Speed Sensor Constant Gain Faultmentioning

confidence: 97%

“…Though it is simple and satisfies the requirements of corresponding systems for stable operation under current sensor fault, it cannot be applied universally due to the great difference in the current reconstruction methods of different systems. (2) The Luenberger-based observer method [9][10][11]. In this method, the establishment of the Luenberger observer can solve the universality problem effectively, but the observer is highly susceptible to the influence of parameter changes and external disturbances, which may even cause misdiagnosis or missed diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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Sensors Fault Diagnosis and Active Fault-Tolerant Control for PMSM Drive Systems Based on a Composite Sliding Mode Observer

Zhu

Tan

et al. 2019

Energies

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Due to the use of multiple observers and controllers in multi-sensor fault-tolerant control of PMSM drive systems, the algorithm is complex and the system control performance is affected. In view of this, the paper studies multi-sensor fault diagnosis and active fault-tolerant control strategies based on a composite sliding mode observer. With the mathematical model of PMSM built, a design method of the composite sliding mode observer is proposed. A single observer is used to observe and estimate various state variables in the system in real time, which simplifies the implementation of observer-related algorithms. In order to improve the diagnostic accuracy of different types of sensors under different faults, a method for determining fault thresholds is proposed through global search for the maximum residual value. Based on this, a fault diagnosis and active fault-tolerant control strategy is proposed to realize fast switching and reconstruction of feedback signals of closed-loop control systems under different faults of multiple sensors, thus restoring the system performance. Finally, the effectiveness of the proposed algorithm and control strategy is verified by simulation experiments

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Section: Speed Sensor Constant Gain Faultmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Sensors Fault Diagnosis and Active Fault-Tolerant Control for PMSM Drive Systems Based on a Composite Sliding Mode Observer

Zhu

Tan

et al. 2019

Energies

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“…Sensor faults are investigated in [11] and exhibited as: (1) sensor gain drop Type a; (2) bias in sensor measurement Type b and (3) complete sensor outage Type c . Type b and Type a faults can be modeled as an addictive fault in sensor measurements y m (k) = y r (k) + f (k) (1) in which y m (k), y r (k) and f (k) denote the faulty measurements, nominal values and fault signals respectively. Type a fault is the sensor gain degradation and modeled as a multiplicative fault in [2] y m (k) = β (k) y r (k) .…”

Section: Problem Statementmentioning

confidence: 99%

“…Due to the high power density and efficiency, permanent magnet synchronous generator based wind turbines are promising in wind conversion systems (WECSs) with variable speed operation and full-scale power delivery [1,2]. To fulfill control demands for maximum power point tracking (MPPT) and grid codes, closed-loop feedback control is designed, relying on the mechanical, current and voltage measurements.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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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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“…The development of instrumentation and automation for modern industrial processes in the chemical and general manufacturing industries allows large quantities of data to be utilized for assessing current operating conditions (Kruger & Xie, 2012;Severson, Chaiwatanodom & Braatz, 2016). Traditional approaches to monitor general processes include model-based (Ding, 2013;Zhong, Xue & Ding, 2018;Liu, Luo, Yang & Wu, 2016;Li, Gao, Shi & Lam, 2016;Zhao, Yang, Ding & Li, 2018), signal-based (Lei, Lin, He & Zuo, 2013;Yan, Gao & Chen, 2014;Fan, Cai, Zhu, Shen, Huang & Shang, 2015;Wu, Guo, Xie, Ni & Liu, 2018), and knowledge-based (Gao, Cecati & Ding, 2015;Mohammadi & Montazeri-Gh, 2015;Chiremsel,  Corresponding Authors: +86-25-8489-3221, q.chen@nuaa.edu.cn (Qian Chen); +1-518-276-4818, krugeu@rpi.edu (Uwe Kruger) Said & Chiremsel, 2016;Davies, Jackson & Dunnett, 2017) techniques. Based on their conceptual simplicity, techniques that relate to multivariate statistical process control (MSPC) (Kruger & Xie, 2012;Qin, 2012;Ge, Song & Gao, 2013;Yin, Li, Gao & Kaynak, 2015) have also gained attention over the past few decades, particularly for applications to industrial processes that produce larger variable sets.…”

Section: Introductionmentioning

confidence: 99%

Monitoring nonstationary and dynamic trends for practical process fault diagnosis

Lin

Krüger

et al. 2019

Control Engineering Practice

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This article introduces a revised common trend framework to monitor nonstationary and dynamic trends in industrial processes and shows needs for each improvement on the basis of three application studies. These improvements relate to (i) the extension of the common trend framework to include sets that contain stationary and nonstationary variables, (ii) handling cases where residuals are not drawn from multivariate normal distributions and (iii) the application of the framework to larger variable sets. Existing work does not adequately address these practically important issues. Industrial application studies highlight the needs for (i) the extended framework to model data sets containing stationary and nonstationary variables, (ii) handling statistics that are not based on normally distributed residuals and (iii) the use of Chigira procedure to robustly extract common trends. The extended framework is compared to traditional approaches. Keywords common trends models; Kasa decomposition; cointegration; latent stationary and nonstationary factors; generalized data structure; forecast recovery; multivariate normal distribution processes has been presented through the application to a glass melter process (Lin, Kruger & Chen, 2017), the following practically relevant issues have not been considered: the application studies presented herein contain recorded variable sets that are both, nonstationary and stationary, whereas Lin, Kruger & Chen (2017) addressed the problem of variable sets that are nonstationary only;  the model residuals may not be drawn from multivariate normal distributions, in contrast to the work in Lin, Kruger & Chen (2017) where this assumption is made; and  the conventional cointegration testing procedure by Johansen (1995) may run into difficulties if the number of variables is large, which is typically the case for complex industrial processes.This article is divided into the following sections. Section 2 gives a detailed description of the processes, including the FCCU simulator, the polymerization and the distillation process, used in this article. Section 3 then summarizes the monitoring framework and introduces an improved common trends framework. Sections 4 to 6 summarize the applications to the three processes. A concluding summary is given in Section 7. Process DescriptionsThis section provides a detailed description of the three processes that are used to demonstrate the working of the improved cointegration framework. Subsection 2.1 gives a description of the simulated fluid catalytic cracking unit. Subsections 2.2 and 2.3 then provide details of the polymerization and distillation processes, respectively. Fluid Catalytic Cracking UnitA fluid catalytic cracking unit (FCCU) is an important part of a refinery. An FCCU receives several different heavy feed stocks from other refinery units and cracks these into more valuable components that are usually blended into gasoline and other products. The main feed stream to an FCCU is gas oil, but heavier diesel and wash oil streams also cont...

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A Signal-Based Fault Detection and Tolerance Control Method of Current Sensor for PMSM Drive

Cited by 104 publications

References 19 publications

Sensors Fault Diagnosis and Active Fault-Tolerant Control for PMSM Drive Systems Based on a Composite Sliding Mode Observer

Sensors Fault Diagnosis and Active Fault-Tolerant Control for PMSM Drive Systems Based on a Composite Sliding Mode Observer

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

Monitoring nonstationary and dynamic trends for practical process fault diagnosis

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