Model-free fault detection and isolation of a benchmark process control system based on multiple classifiers techniques—A comparative study

Nozari, Hasan Abbasi; Nazeri, Sina; Banadaki, Hamed Dehghan; Castaldi, Paolo

doi:10.1016/j.conengprac.2018.01.007

Cited by 12 publications

(8 citation statements)

References 35 publications

(42 reference statements)

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“…The original hypothesis H 0 is for the fault-free condition, while the alternative hypothesis H 1 is for the fault of the system [31]. The false alarm rate and the missed detection rate of a faulty system can be expressed as…”

Section: False Alarm Rate and Missed Detection Ratementioning

confidence: 99%

“…The original hypothesis H 0 is for the fault‐free condition, while the alternative hypothesis H 1 is for the fault of the system [31]. The false alarm rate and the missed detection rate of a faulty system can be expressed as

P_{FA} = P ()(, min_{j = 1, 2, \dots, K} KLD false(p false(θ_{M} false), p false(θ_{j}^{NF} false) false) > J | H_{0})

P_{MA} = P ()(, min_{j = 1, 2, \dots, K} KLD false(p false(θ_{M} false), p false(θ_{j}^{NF} false) false) \leq J | H_{1})

For convenience in calculation,

min_{j = 1, 2, \dots, K} KLD false(p false(θ_{M} false), p false(θ_{j}^{NF} false) false)

is assumed to obey normal distribution.…”

Section: Robust Fault‐detection Design Of Wind Turbinesmentioning

confidence: 99%

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Robust fault‐detection based on residual K–L divergence for wind turbines

Zhang

Wang

Xia

et al. 2019

IET Renewable Power Generation

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A robust fault-detection design based on residual Kullback-Leibler (K-L) divergence, which is applied to a 5 MW offshore wind turbine (WT) benchmark, is presented. The main challenges of the wind turbine fault detection lie in its complex operation conditions and disturbances as well as measurement noise. For overcoming these difficulties, the measured data are divided on the basis of the operation conditions of WT. The robust residual generator based on parity vector is adopted to calculate the residual under different operation conditions. The K-L divergence based on probability density function is employed to measure the residual. Then, the threshold for the fault detection is determined in line with both false alarm rate and missed detection rate. The simulation results show that the performance and effectiveness of the proposed robust fault detection are better than compared with other data-driven fault-detection approaches.

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Section: False Alarm Rate and Missed Detection Ratementioning

confidence: 99%

P_{FA} = P ()(, min_{j = 1, 2, \dots, K} KLD false(p false(θ_{M} false), p false(θ_{j}^{NF} false) false) > J | H_{0})

P_{MA} = P ()(, min_{j = 1, 2, \dots, K} KLD false(p false(θ_{M} false), p false(θ_{j}^{NF} false) false) \leq J | H_{1})

For convenience in calculation,

min_{j = 1, 2, \dots, K} KLD false(p false(θ_{M} false), p false(θ_{j}^{NF} false) false)

is assumed to obey normal distribution.…”

Section: Robust Fault‐detection Design Of Wind Turbinesmentioning

confidence: 99%

Robust fault‐detection based on residual K–L divergence for wind turbines

Zhang

Wang

Xia

et al. 2019

IET Renewable Power Generation

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“…In this section fault detection and isolation techniques that have been implemented across various processes are reported here. Nozari et al, [17] have put forward a data-driven method for fault detection and isolation using ensemble classification methods. A comparison of all classification methods individually and ensemble results were highlighted.…”

Section: Introductionmentioning

confidence: 99%

Is Fault Detection and Diagnosis in Pneumatic Actuator A Topic of Concern?

Navada

Santhosh

2020

ARFMTS

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The present civilization highly depends on industrial products and hence there is an increased demand for the same. Therefore, each industry is trying to increase its production output without hindering the quality. Maintenance of plant health is essential to improve the production rate without any loss. Industrial processes require monitoring of every element as their consistent behavior is a fundamental concern. Any deviation in the working of these components may alter the quality of the end product, causing a huge loss for the industry. Therefore, monitoring and finding the root cause for irregular behavior of industrial processes is a requisite for avoiding any future loss. In this paper, an attempt is made to present types of faults, types of pneumatic actuator faults, and different techniques used for the detection and isolation of faults. Simulation work is carried out to generate stiction behavior in the control valve using the Choudhury stiction model. Valve stiction behavior for different values of stick band and jump values are discussed in this paper. A comparison of several techniques used for the detection of faults based on two performance indices namely true detection rate and false alarm rate has been given at the end of this paper. From these techniques, it is observed that these indices are interdependent, such that an increase in the detection rate increases the false detection rate and increases detection time.

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“…Reference [20] reported a signature analysis technique based on the hysteresis property of an actuator for fault isolation in pneumatic actuators used for aviation applications. Several pneumatic faults and their fault detection techniques are reviewed in [21]. Both model-driven and data-driven techniques are discussed.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection of a Flow Control Valve Using Vibration Analysis and Support Vector Machine

Venkata

Rao

2019

Electronics

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A control valve plays a very significant role in the stable and efficient working of a control loop for any process. In a fluid flow process, the probability of failure of a control valve may increase for many reasons pertaining to a flow process such as high pressures at the inlet, different properties of the liquid flowing through the pipe, mechanical issue related to a control valve, ageing, etc. A method to detect faults in the valve can lead to better stability of the control loop. In the proposed work, a technique is developed to determine the fault in a pneumatic control valve by analyzing the vibration data at the outlet of the valve. The fault diagnosis of the valve is carried out by analyzing the change in vibration of the pipe due to the change in flow pattern induced by the control valve. The faults being considered are inflow and insufficient supply pressure faults. Vibration data obtained is processed using a signal processing technique like amplification, Fourier transform, etc. The support vector machine (SVM) algorithm is used to classify the vibration data into two classes, one normal and the other faulty. The designed algorithm is trained to identify faults and subjected to test with a practical setup; test results show an accuracy of 97%.

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Model-free fault detection and isolation of a benchmark process control system based on multiple classifiers techniques—A comparative study

Cited by 12 publications

References 35 publications

Robust fault‐detection based on residual K–L divergence for wind turbines

Robust fault‐detection based on residual K–L divergence for wind turbines

Is Fault Detection and Diagnosis in Pneumatic Actuator A Topic of Concern?

Fault Detection of a Flow Control Valve Using Vibration Analysis and Support Vector Machine

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