A fuzzy-based gas turbine fault detection and identification system for full and part-load performance deterioration

Mohammadi, Ehsan; Montazeri-Gh, Morteza

doi:10.1016/j.ast.2015.07.002

Cited by 75 publications

(33 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It also prevents the possible surge/stall, overpressure and over-temperature. Besides, reduction of fuel consumption [3][4][5], fault diagnosis [6,7], vibration control [8], and fault-tolerant control [9] are also significant for engine control systems of UAVs. Moreover, the engine control system should have strong adaptivity when the system parameters vary.…”

Section: Introductionmentioning

confidence: 99%

Single Neural Adaptive PID Control for Small UAV Micro-Turbojet Engine

Wei

Wang

et al. 2020

Sensors

View full text Add to dashboard Cite

The micro-turbojet engine (MTE) is especially suitable for unmanned aerial vehicles (UAVs). Because the rotor speed is proportional to the thrust force, the accurate speed tracking control is indispensable for MTE. Thanks to its simplicity, the proportional–integral–derivative (PID) controller is commonly used for rotor speed regulation. However, the PID controller cannot guarantee superior performance over the entire operation range due to the time-variance and strong nonlinearity of MTE. The gain scheduling approach using a family of linear controllers is recognized as an efficient alternative, but such a solution heavily relies on the model sets and pre-knowledge. To tackle such challenges, a single neural adaptive PID (SNA-PID) controller is proposed herein for rotor speed control. The new controller featuring with a single-neuron network is able to adaptively tune the gains (weights) online. The simple structure of the controller reduces the computational load and facilitates the algorithm implementation on low-cost hardware. Finally, the proposed controller is validated by numerical simulations and experiments on the MTE in laboratory conditions, and the results show that the proposed controller achieves remarkable effectiveness for speed tracking control. In comparison with the PID controller, the proposed controller yields 54% and 66% reductions on static tracking error under two typical cases.

show abstract

Section: Introductionmentioning

confidence: 99%

Single Neural Adaptive PID Control for Small UAV Micro-Turbojet Engine

Wei

Wang

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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...

show abstract

“…The components of monitoring and detection include data extraction, fault detection, fault location and prognosis [1][2][3][4]. Much of the current literature focuses on fault location algorithms which can be broken down into pattern recognition methods such as fuzzy logic [5,6], genetic algorithms [7], Bayesian belief networks [8][9][10], and neural networks [11][12][13][14] and model identification methods such as Kalman filtering [15] and weighted least squares [16][17][18]. But despite continuous developments in all of these physical and computational techniques, serious gas turbine problems can still quickly develop before corrective actions can take place.…”

Section: Introductionmentioning

confidence: 99%

Enhanced turbine monitoring using emissions measurements and data reconciliation

Syed

Dooley

Madron³

et al. 2016

Applied Energy

View full text Add to dashboard Cite

Standard monitoring within a gas-turbine based cogeneration system includes key flow rates, temperatures, pressures and turbine vibration. These standard measurements can be enhanced with continuous emissions monitoring to help pinpoint system problems. A combination of these measurements, a fast prediction model and data reconciliation constitute an improved monitoring and diagnostic tool that can quantitatively predict the existence of turbine problems (for example, damaged combustor nozzles) even when standard turbine monitoring indicates no problems exist.

show abstract

A fuzzy-based gas turbine fault detection and identification system for full and part-load performance deterioration

Cited by 75 publications

References 47 publications

Single Neural Adaptive PID Control for Small UAV Micro-Turbojet Engine

Single Neural Adaptive PID Control for Small UAV Micro-Turbojet Engine

Monitoring nonstationary and dynamic trends for practical process fault diagnosis

Enhanced turbine monitoring using emissions measurements and data reconciliation

Contact Info

Product

Resources

About