Engineering systems fault detection methods

2022 ◽  
pp. 119-164
Author(s):  
Gilberto Francisco Martha de Souza ◽  
Adherbal Caminada Netto ◽  
Arthur Henrique de Andrade Melani ◽  
Miguel Angelo de Carvalho Michalski ◽  
Renan Favarão da Silva
Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 389
Author(s):  
Jinfu Liu ◽  
Zhenhua Long ◽  
Mingliang Bai ◽  
Linhai Zhu ◽  
Daren Yu

As one of the core components of gas turbines, the combustion system operates in a high-temperature and high-pressure adverse environment, which makes it extremely prone to faults and catastrophic accidents. Therefore, it is necessary to monitor the combustion system to detect in a timely way whether its performance has deteriorated, to improve the safety and economy of gas turbine operation. However, the combustor outlet temperature is so high that conventional sensors cannot work in such a harsh environment for a long time. In practical application, temperature thermocouples distributed at the turbine outlet are used to monitor the exhaust gas temperature (EGT) to indirectly monitor the performance of the combustion system, but, the EGT is not only affected by faults but also influenced by many interference factors, such as ambient conditions, operating conditions, rotation and mixing of uneven hot gas, performance degradation of compressor, etc., which will reduce the sensitivity and reliability of fault detection. For this reason, many scholars have devoted themselves to the research of combustion system fault detection and proposed many excellent methods. However, few studies have compared these methods. This paper will introduce the main methods of combustion system fault detection and select current mainstream methods for analysis. And a circumferential temperature distribution model of gas turbine is established to simulate the EGT profile when a fault is coupled with interference factors, then use the simulation data to compare the detection results of selected methods. Besides, the comparison results are verified by the actual operation data of a gas turbine. Finally, through comparative research and mechanism analysis, the study points out a more suitable method for gas turbine combustion system fault detection and proposes possible development directions.


Author(s):  
Yuqi Pang ◽  
Gang Ma ◽  
Xiaotian Xu ◽  
Xunyu Liu ◽  
Xinyuan Zhang

Background: Fast and reliable fault detection methods are the main technical challenges faced by photovoltaic grid-connected systems through modular multilevel converters (MMC) during the development. Objective: Existing fault detection methods have many problems, such as the inability of non-linear elements to form accurate analytical expressions, the difficulty of setting protection thresholds and the long detection time. Method: Aiming at the problems above, this paper proposes a rapid fault detection method for photovoltaic grid-connected systems based on Recurrent Neural Network (RNN). Results: The phase-to-mode transformation is used to extract the fault feature quantity to get the RNN input data. The hidden layer unit of the RNN is trained through a large amount of simulation data, and the opening instruction is given to the DC circuit breaker. Conclusion: The simulation verification results show that the proposed fault detection method has the advantage of faster detection speed without difficulties in setting and complicated calculation.


Processes ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 122
Author(s):  
Yang Li ◽  
Fangyuan Ma ◽  
Cheng Ji ◽  
Jingde Wang ◽  
Wei Sun

Feature extraction plays a key role in fault detection methods. Most existing methods focus on comprehensive and accurate feature extraction of normal operation data to achieve better detection performance. However, discriminative features based on historical fault data are usually ignored. Aiming at this point, a global-local marginal discriminant preserving projection (GLMDPP) method is proposed for feature extraction. Considering its comprehensive consideration of global and local features, global-local preserving projection (GLPP) is used to extract the inherent feature of the data. Then, multiple marginal fisher analysis (MMFA) is introduced to extract the discriminative feature, which can better separate normal data from fault data. On the basis of fisher framework, GLPP and MMFA are integrated to extract inherent and discriminative features of the data simultaneously. Furthermore, fault detection methods based on GLMDPP are constructed and applied to the Tennessee Eastman (TE) process. Compared with the PCA and GLPP method, the effectiveness of the proposed method in fault detection is validated with the result of TE process.


Author(s):  
Dmytro Shram ◽  
Oleksandr Stepanets

The main objective of this paper is to review of fault detection and isolation (FDI) methods and applications on various power plants. Due to the focus of the topic, on model and model-free FDI methods, technical details were kept in the references. We will overview the methods in terms of model-based, data driven and signal based methods further in the paper. Principles of three FDI methods are explained and characteristics of number of some popular techniques are described. It also summarizes data-driven methods and applications related to power generation plants. Parts of control system applications of FDI in TPPs with possible faults are shown in the Table I. Some popular techniques for the various faults in TPPs are discussed also.


High Voltage ◽  
2017 ◽  
Vol 2 (4) ◽  
pp. 211-219 ◽  
Author(s):  
Bin Chang ◽  
Oliver Cwikowski ◽  
Mike Barnes ◽  
Roger Shuttleworth ◽  
Antony Beddard ◽  
...  

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