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Artículos de revistas sobre el tema "Faulty variable isolation"

Autor: Grafiati

Publicado: 25 de mayo de 2024

Última modificación: 31 de julio de 2025

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1

Asokan, A., and D. Sivakumar. "Model based fault detection and diagnosis using structured residual approach in a multi-input multi-output system." Serbian Journal of Electrical Engineering 4, no. 2 (2007): 133–45. http://dx.doi.org/10.2298/sjee0702133a.

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Fault detection and isolation (FDI) is a task to deduce from observed variable of the system if any component is faulty, to locate the faulty components and also to estimate the fault magnitude present in the system. This paper provides a systematic method of fault diagnosis to detect leak in the three-tank process. The proposed scheme makes use of structured residual approach for detection, isolation and estimation of faults acting on the process [1]. This technique includes residual generation and residual evaluation. A literature review showed that the conventional fault diagnosis methods l

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2

Sellami, T., H. Berriri, S. Jelassi, A. M. Darcherif, and M. F. Mimouni. "Sliding Mode Observers-based Fault Detection and Isolation for Wind Turbine-driven Induction Generator." International Journal of Power Electronics and Drive Systems (IJPEDS) 8, no. 3 (2017): 1345. http://dx.doi.org/10.11591/ijpeds.v8.i3.pp1345-1358.

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Inter-turn short-circuit (ITSC) faults on the induction machine has received much attention in the recent years. Early detection of such faults in wind turbine systems would allow to avoid fluctuation on wind power output and maintain the reliability level. In this paper, Sliding Mode Observers (SMO)-based fault detection and isolation method is developed for induction generator (IG)-based variable-speed grid-connected wind turbines. Firstly, the dynamic model of the wind turbine and IG was given and then, the control was made based on Maximum Power Point Tracking (MPPT) method. The IG closed-

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3

Zhao, Chunhui, and Wei Wang. "Efficient faulty variable selection and parsimonious reconstruction modelling for fault isolation." Journal of Process Control 38 (February 2016): 31–41. http://dx.doi.org/10.1016/j.jprocont.2015.12.002.

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4

Antory, D., U. Kruger, G. Irwin, and G. McCullough. "Fault diagnosis in internal combustion engines using non-linear multivariate statistics." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 219, no. 4 (2005): 243–58. http://dx.doi.org/10.1243/095965105x9614.

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This paper presents a statistical-based fault diagnosis scheme for application to internal combustion engines. The scheme relies on an identified model that describes the relationships between a set of recorded engine variables using principal component analysis (PCA). Since combustion cycles are complex in nature and produce non-linear relationships between the recorded engine variables, the paper proposes the use of non-linear PCA (NLPCA). The paper further justifies the use of NLPCA by comparing the model accuracy of the NLPCA model with that of a linear PCA model. A new non-linear variable

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5

Kariwala, Vinay, Pabara-Ebiere Odiowei, Yi Cao, and Tao Chen. "A branch and bound method for isolation of faulty variables through missing variable analysis." Journal of Process Control 20, no. 10 (2010): 1198–206. http://dx.doi.org/10.1016/j.jprocont.2010.07.007.

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6

Nie, Lei, Yizhu Ren, Rouhui Wu, and Mengying Tan. "Sensor Fault Diagnosis, Isolation, and Accommodation for Heating, Ventilating, and Air Conditioning Systems Based on Soft Sensor." Actuators 12, no. 10 (2023): 389. http://dx.doi.org/10.3390/act12100389.

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Heating, Ventilating, and Air Conditioning (HVAC) systems often suffer from unscheduled maintenance or abnormal shutdown due to the fault of their interior sensor system. Traditional fault diagnosis methods for HVAC sensor systems primarily focus on sensor fault diagnosis and isolation, lacking fault accommodation. Therefore, to realize effective sensor fault detection, identification, and accommodation (SFDIA), a method for HVAC SFDIA based on the soft sensor is proposed. First, a diagnosis soft sensor with multi-variable input is constructed to estimate the output of the physical sensor bein

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7

Hu, Yunyun, Yue Wang, and Chunhui Zhao. "A sparse fault degradation oriented fisher discriminant analysis (FDFDA) algorithm for faulty variable isolation and its industrial application." Control Engineering Practice 90 (September 2019): 311–20. http://dx.doi.org/10.1016/j.conengprac.2019.07.007.

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8

Sorribes Pamer, Felix, Bernd Luber, Josef Fuchs, Thomas Kern, and Martin Rosenberger. "Data-driven fault diagnosis of bogie suspension components with on- board acoustic sensors." PHM Society European Conference 5, no. 1 (2020): 13. http://dx.doi.org/10.36001/phme.2020.v5i1.1211.

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This paper proposes a data-driven approach for fault-detection and isolation of bogie suspension components with on-board acoustic sensors. The fault detection technique is based on the acoustic emissions variation due to structural modal coupling changes in the presence of faulty components. A suspensions component failure introduces an imbalance into the system, resulting in dynamics interferences between the motions. These interferences modify the energy introduced into the system as well as its acoustic emissions. The unknown arbitrary track irregularities generate together with a variable

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9

Abbas, Mohammed, Houcine Chafouk, and Sid Ahmed El Mehdi Ardjoun. "Fault Diagnosis in Wind Turbine Current Sensors: Detecting Single and Multiple Faults with the Extended Kalman Filter Bank Approach." Sensors 24, no. 3 (2024): 728. http://dx.doi.org/10.3390/s24030728.

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Currently, in modern wind farms, the doubly fed induction generator (DFIG) is commonly adopted for its ability to operate at variable wind speeds. Generally, this type of wind turbine is controlled by using two converters, one on the rotor side (RSC) and the other one on the grid side (GSC). However, the control of these two converters depends mainly on current sensors measurements. Nevertheless, in the case of sensor failure, control stability may be compromised, leading to serious malfunctions in the wind turbine system. Therefore, in this article, we will present an innovative diagnostic ap

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10

Uddin, Md Aftab, Mst Aysha Siddiqua, and Mst Sadia Ahmed. "Isolation and quantification of indicator and pathogenic microorganisms along with their drug resistance traits from bottled and jar water samples within Dhaka city, Bangladesh." Stamford Journal of Microbiology 9, no. 1 (2020): 12–14. http://dx.doi.org/10.3329/sjm.v9i1.45651.

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Commercial drinking water may serve as potential threat to public health if these items are contaminated with a number of pathogenic microorganisms due to faulty manufacturing process. Present study attempted to isolate and quantify the microorganisms from various jar and bottle water samples collected from various areas of Dhaka city. Antibiotic susceptibility pattern of suspected bacterial isolates were also determined in this study. Out of the eighteen samples studied, ten were jar water samples and eight were bottled water samples. The range of total viable bacterial count (TVBC) in these

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11

Dippenaar, Marchel C., George van Schoor, Kenneth R. Uren, and Willem M. K. van Niekerk. "Fault Detection and Isolation in Transient Conditions on a Heated Two-Tank System: A Multiway Principal Component Analysis Approach." Processes 12, no. 8 (2024): 1620. http://dx.doi.org/10.3390/pr12081620.

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This paper presents a methodology for fault detection and isolation (FDI) in transient conditions using a multiway principal component analysis (MPCA) approach where practical data have been augmented with simulated data to conduct FDI when there are insufficient practical data. The motivation for using a heated two-tank system is due to the fact that it resembles a basic process in terms of controllable variables, noise, disturbances, and changes in operating points. Normal and faulty condition data of the practical heated two-tank system as well as a Simulink® model of the heated two-tank sy

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12

Li, Lingwei, Yuan Yuan, Xinglong Zhang, Songwei Wu, and Tianhong Zhang. "Fault-Tolerant Control Scheme for the Sensor Fault in the Acceleration Process of Variable Cycle Engine." Applied Sciences 12, no. 4 (2022): 2085. http://dx.doi.org/10.3390/app12042085.

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This paper presents a fault-tolerant control scheme for the sensor fault in the acceleration process of the variable cycle engine. Firstly, an adaptive equilibrium manifold model with multiple inputs and multiple outputs is established. Combined with the Kalman filter bank, sensor fault diagnosis is carried out to realize the diagnosis and signal reconstruction of the engine in the case of a single sensor and double sensor faults. On this basis, isolation and group isolation are used to diagnose sensor faults and reconstruct signal in speed closed-loop control. Then, the control plan of the ac

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13

EL KOUJOK, MOHAMED, MOULOUD AMAZOUZ, and BRUNO POULIN. "Comprehensive fault detection and isolation method applied to a recovery boiler." May 2016 15, no. 5 (2016): 323–28. http://dx.doi.org/10.32964/tj15.5.323.

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Early and accurate detection and isolation of industrial process faults are crucial to avoiding abnormal situations that cause productivity losses. Principal component analysis and reconstruction-based contribution (PCA-RBC) is a popular method used for such tasks. Unfortunately, this method does not guarantee correct fault isolation in cases where the faulty variables contribute little or do not contribute at all to the main principal components of the PCA model. This is the case, for example, of some pollutant emission levels that do not affect the global performance of a biomass boiler, but

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14

Hamrouni, Imen, Hajer Lahdhiri, Khaoula Ben Abdellafou, Ahamed Aljuhani, Okba Taouali, and Kais Bouzrara. "Anomaly Detection and Localization for Process Security Based on the Multivariate Statistical Method." Mathematical Problems in Engineering 2022 (February 3, 2022): 1–11. http://dx.doi.org/10.1155/2022/5580774.

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Anomaly detection is very important for system monitoring and security since successful execution of these engineering tasks depends on access to validated data. The localization of the variable causing the fault is very essential. Indeed, the localization of the fault is defined as the ability to determine the source of the fault on a system. Generally, the identification of faults is linked to the detection procedure implemented. Therefore, it is very important to choose the adequate fault detection model to locate fault. For nonlinear uncertain systems, the most performed fault detection me

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15

Ding, Jingxian, Tianyu Xia, Xinzhou Wu, Shize Zheng, and Tianyi Wang. "Fault Detection and Isolation Strategy for Brake Actuation Units of High-Speed Trains Under Variable Operating Conditions." Mathematics 13, no. 7 (2025): 1022. https://doi.org/10.3390/math13071022.

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The brake system is a key system for the safe operation and stopping of trains. As a core component of brake systems, the brake actuation unit (BAU) is essential for slowing down or stopping trains, and faults in the BAU will affect the safety and efficiency of train operation. In order to detect and locate faults in the BAU in time, a fault detection and isolation (FDI) strategy, based on mutual residuals (MRs), principal component analysis (PCA) and improved reconstruction-based contribution plots (IRBCP), was proposed. Firstly, the structural composition and working principle of the BAU wer

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16

Sorrentino, Marco, and Alena Trifirò. "Model-Based Diagnosis of Telecommunication Cooling Systems Malfunctioning." E3S Web of Conferences 238 (2021): 10003. http://dx.doi.org/10.1051/e3sconf/202123810003.

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A model is developed that allows simulating the most-likely failures possibly occurring in freecooling (FC) systems of telecommunication (TLC) switching rooms. Main aim is to provide an effective and online implementable diagnosis method, which in turn will allow fulfilling the threefold function of safeguarding electronic equipment, ensuring desired air quality in case of human presence and reducing malfunction-related waste of energy. Specifically in this work, obstruction (reduction of the volumetric flow of air introduced into the room) and loss of efficiency (degradation of the fan) are d

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17

Yang, Zhimin, Yi Chai, Hongpeng Yin, and Songbing Tao. "LPV Model Based Sensor Fault Diagnosis and Isolation for Permanent Magnet Synchronous Generator in Wind Energy Conversion Systems." Applied Sciences 8, no. 10 (2018): 1816. http://dx.doi.org/10.3390/app8101816.

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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 introduce

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18

Rosa, Tiago Gaspar da, Arthur Henrique de Andrade Melani, Fabio Henrique Pereira, Fabio Norikazu Kashiwagi, Gilberto Francisco Martha de Souza, and Gisele Maria De Oliveira Salles. "Semi-Supervised Framework with Autoencoder-Based Neural Networks for Fault Prognosis." Sensors 22, no. 24 (2022): 9738. http://dx.doi.org/10.3390/s22249738.

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This paper presents a generic framework for fault prognosis using autoencoder-based deep learning methods. The proposed approach relies upon a semi-supervised extrapolation of autoencoder reconstruction errors, which can deal with the unbalanced proportion between faulty and non-faulty data in an industrial context to improve systems’ safety and reliability. In contrast to supervised methods, the approach requires less manual data labeling and can find previously unknown patterns in data. The technique focuses on detecting and isolating possible measurement divergences and tracking their growt

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19

Hu, Kai-Yu, Chunxia Yang, and Wenjing Sun. "Adaptive Sliding Mode Fault Compensation for Sensor Faults of Variable Structure Hypersonic Vehicle." Sensors 22, no. 4 (2022): 1523. http://dx.doi.org/10.3390/s22041523.

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This paper investigates the sensor fault detection and fault-tolerant control (FTC) technology of a variable-structure hypersonic flight vehicle (HFV). First, an HFV nonlinear system considering sensor compound faults, disturbance, and the variable structure parameter is established, which is divided into the attitude angle outer and angular rate inner loops. Then a nonlinear fault integrated detector is proposed to detect the moment of fault occurrence and provide the residual to design the sliding mode equations. Furthermore, the sliding mode method combined with the virtual adaptive control

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20

Park, Jinseong, та Youngjin Park. "Multiple-Actuator Fault Isolation Using a Minimal ℓ1-Norm Solution with Applications in Overactuated Electric Vehicles". Sensors 22, № 6 (2022): 2144. http://dx.doi.org/10.3390/s22062144.

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A multiple-actuator fault isolation approach for overactuated electric vehicles (EVs) is designed with a minimal ℓ1-norm solution. As the numbers of driving motors and steering actuators increase beyond the number of controlled variables, an EV becomes an overactuated system, which exhibits actuator redundancy and enables the possibility of fault-tolerant control (FTC). On the other hand, an increase in the number of actuators also increases the possibility of simultaneously occurring multiple faults. To ensure EV reliability while driving, exact and fast fault isolation is required; however,

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21

Ariola, Marco, Massimiliano Mattei, Immacolata Notaro, Federico Corraro, and Adolfo Sollazzo. "An SFDI Observer–Based Scheme for a General Aviation Aircraft." International Journal of Applied Mathematics and Computer Science 25, no. 1 (2015): 149–58. http://dx.doi.org/10.1515/amcs-2015-0011.

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Abstract The problem of detecting and isolating sensor faults (sensor fault detection and isolation-SFDI) on a general aviation aircraft, in the presence of external disturbances, is considered. The proposed approach consists of an extended Kalman observer applied to an augmented aircraft plant, where some integrators are added to the output variables subject to faults. The output of the integrators should be ideally zero in the absence of model uncertainties, external disturbances and sensor faults. A threshold-based decision making system is adopted where the residuals are weighted with gain

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22

Wang, Runze, Tiantian Liang, Xiang Zheng, and Kexin Li. "Robust fault detection and isolation for dynamics of high-speed train with uncertainties based on descriptor systems." Advances in Mechanical Engineering 14, no. 7 (2022): 168781322211121. http://dx.doi.org/10.1177/16878132221112139.

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This paper proposes a new type of robust fault detection and isolation filter for dynamics of HST based on descriptor systems with uncertainties in finite frequency. This filter is designed based on the unknown input filter to decouple the non-linear variables due to the aerodynamic drag pressing on the trains. The exogenous disturbance is partitioned into two parts-the decoupling one is regarded as the augmented variables of the non-linear part of the systems, and the non-decoupling one is seen as the augmented disturbance along with the uncertainties. Concurrent faults of different positions

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23

Koh, C. K. H., J. Shi, W. J. Williams, and J. Ni. "Multiple Fault Detection and Isolation Using the Haar Transform, Part 1: Theory." Journal of Manufacturing Science and Engineering 121, no. 2 (1999): 290–94. http://dx.doi.org/10.1115/1.2831218.

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Most manufacturing processes involve several process variables which interact with one another to produce a resultant action on the part. A fault is said to occur when any of these process variables deviate beyond their specified limits. An alarm is triggered when this happens. Low cost and less sophisticated detection schemes based on threshold bounds on the original measurements (without feature extraction) often suffer from high false alarm and missed detection rates when the process measurements are not properly conditioned. They are unable to detect frequency or phase shifted fault signal

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24

Abed, Ahmed M., Sabah A. Gitaffa, and Abbas H. Issa. "Quadratic Support Vector Machine and K-Nearest Neighbor Based Robust Sensor Fault Detection and Isolation." Engineering and Technology Journal 39, no. 5A (2021): 859–69. http://dx.doi.org/10.30684/etj.v39i5a.2002.

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Fault detection plays a serious role in high-cost and safety-critical processes. There are two main drivers for continuous improvement in the area of early detection of process faults safety and reliability of technical plants. Detect fault in Geophone string sensors (SG-10) are very important in oil exploration to avoid loss economy. Methods are developed to enable earlier detection of process faults than the traditional limit and trend checking based on a single process variable and the development of these methods is a key matter. Classification methods will be used for pattern recognition

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25

Kim, Minseok, Seunghwan Jung, Eunkyeong Kim, Baekcheon Kim, Jinyong Kim, and Sungshin Kim. "A Fault Detection and Isolation Method via Shared Nearest Neighbor for Circulating Fluidized Bed Boiler." Processes 11, no. 12 (2023): 3433. http://dx.doi.org/10.3390/pr11123433.

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Accurate and timely fault detection and isolation (FDI) improve the availability, safety, and reliability of target systems and enable cost-effective operations. In this study, a shared nearest neighbor (SNN)-based method is proposed to identify the fault variables of a circulating fluidized bed boiler. SNN is a derivative method of the k-nearest neighbor (kNN), which utilizes shared neighbor information. The distance information between these neighbors can be applied to FDI. In particular, the proposed method can effectively detect faults by weighing the distance values based on the number of

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26

Sztyber-Betley, Anna, Michał Syfert, Jan Maciej Kościelny, and Zuzanna Górecka. "Controller Cyber-Attack Detection and Isolation." Sensors 23, no. 5 (2023): 2778. http://dx.doi.org/10.3390/s23052778.

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This article deals with the cyber security of industrial control systems. Methods for detecting and isolating process faults and cyber-attacks, consisting of elementary actions named “cybernetic faults” that penetrate the control system and destructively affect its operation, are analysed. FDI fault detection and isolation methods and the assessment of control loop performance methods developed in the automation community are used to diagnose these anomalies. An integration of both approaches is proposed, which consists of checking the correct functioning of the control algorithm based on its

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27

Ren, Zelin, Yongqiang Tang, and Wensheng Zhang. "Quality-related fault diagnosis based on k-nearest neighbor rule for non-linear industrial processes." International Journal of Distributed Sensor Networks 17, no. 11 (2021): 155014772110559. http://dx.doi.org/10.1177/15501477211055931.

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The fault diagnosis approaches based on k-nearest neighbor rule have been widely researched for industrial processes and achieve excellent performance. However, for quality-related fault diagnosis, the approaches using k-nearest neighbor rule have been still not sufficiently studied. To tackle this problem, in this article, we propose a novel quality-related fault diagnosis framework, which is made up of two parts: fault detection and fault isolation. In the fault detection stage, we innovatively propose a novel non-linear quality-related fault detection method called kernel partial least squa

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28

Nasri, Othman, Imen Gueddi, Philippe Dague, and Kamal Benothman. "Spacecraft Actuator Diagnosis with Principal Component Analysis: Application to the Rendez-Vous Phase of the Mars Sample Return Mission." Journal of Control Science and Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/204918.

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This paper presents a fault detection and isolation (FDI) approach in order to detect and isolate actuators (thrusters and reaction wheels) faults of an autonomous spacecraft involved in the rendez-vous phase of the Mars Sample Return (MSR) mission. The principal component analysis (PCA) has been adopted to estimate the relationships between the various variables of the process. To ensure the feasibility of the proposed FDI approach, a set of data provided by the industrial “high-fidelity” simulator of the MSR and representing the opening (resp., the rotation) rates of the spacecraft thrusters

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Kwon, Hyukjoon, and Sang Jeen Hong. "Use of Optical Emission Spectroscopy Data for Fault Detection of Mass Flow Controller in Plasma Etch Equipment." Electronics 11, no. 2 (2022): 253. http://dx.doi.org/10.3390/electronics11020253.

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To minimize wafer yield losses by misprocessing during semiconductor manufacturing, faster and more accurate fault detection during the plasma process are desired to increase production yields. Process faults can be caused by abnormal equipment conditions, and the performance drifts of the parts or components of complicated semiconductor fabrication equipment are some of the most unnoticed factors that eventually change the plasma conditions. In this work, we propose improved stability and accuracy of process fault detection using optical emission spectroscopy (OES) data. Under a controlled ex

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30

Taoufik, Anass, Michael Defoort, Mohamed Djemai, and Krishna Busawon. "A distributed fault detection scheme in disturbed heterogeneous networked systems." Nonlinear Dynamics 107, no. 3 (2021): 2519–38. http://dx.doi.org/10.1007/s11071-021-07129-0.

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AbstractThis paper deals with the problem of distributed fault detection and isolation in multi-agent systems with disturbed high-order dynamics subject to communication uncertainties and faults. Distributed finite-frequency mixed $${\mathcal {H}}_-$$ H - $$/{\mathcal {H}}_\infty $$ / H ∞ unknown input observers are designed to detect and distinguish actuator, sensor and communication faults. Furthermore, an agent is capable of detecting not only its own faults but also faults in its neighbouring agents. Sufficient conditions are then derived in terms of a set of linear matrix inequalities whi

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El Maharat, Anis, Chakour Chouaib, and Azzedine Hamza. "Fault Detection Using an Adapted Interval PCA Approach." International Journal of Informatics and Applied Mathematics 8, no. 1 (2025): 22–38. https://doi.org/10.53508/ijiam.1603442.

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Principal Component Analysis (PCA) is a commonly employed technique in industrial systems for process monitoring and fault diagnosis, owing to its capability to efficiently process large datasets. Traditionally, it is applied to single-valued variables, where critical information can be lost in real scenarios with data uncertainties. Interval-valued PCA methods like Symbolic Covariance PCA (SCPCA) and Complete Information PCA (CIPCA) have been developed to enhance fault detection by incorporating data uncertainties in the PCA model. This paper presents a novel adaptation of SCPCA for detecting

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Liang, Xiaoxia, Fang Duan, Ian Bennett, and David Mba. "A Sparse Autoencoder-Based Unsupervised Scheme for Pump Fault Detection and Isolation." Applied Sciences 10, no. 19 (2020): 6789. http://dx.doi.org/10.3390/app10196789.

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Pumps are one of the most critical machines in the petrochemical process. Condition monitoring of such parts and detecting faults at an early stage are crucial for reducing downtime in the production line and improving plant safety, efficiency and reliability. This paper develops a fault detection and isolation scheme based on an unsupervised machine learning method, sparse autoencoder (SAE), and evaluates the model on industrial multivariate data. The Mahalanobis distance (MD) is employed to calculate the statistical difference of the residual outputs between monitoring and normal states and

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33

Dai, Zi Shu. "Base Isolation Systems Employing Variable Friction Dampers Based on a STFT Controller." Applied Mechanics and Materials 90-93 (September 2011): 1566–75. http://dx.doi.org/10.4028/www.scientific.net/amm.90-93.1566.

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Conventional isolation systems may induce an excessive response in near-fault earthquakes. A new short time Fourier transformation (STFT) control algorithm for variable friction dampers (VFD) is developed to improve the performance of base isolation buildings in near-fault earthquakes. The STFT controller varies the clamping force in the VFD damper to achieve the response reduction. In addition, the STFT algorithm is implemented analytically on a multi degree of freedom system (MDOF) with laminated rubber bearings and variable friction dampers in Simulink environment. Three types of earthquake

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Koh, C. K. H., J. Shi, W. J. Williams, and J. Ni. "Multiple Fault Detection and Isolation Using the Haar Transform, Part 2: Application to the Stamping Process." Journal of Manufacturing Science and Engineering 121, no. 2 (1999): 295–99. http://dx.doi.org/10.1115/1.2831219.

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The sheet metal drawing operation is a complex manufacturing process involving more than forty process variables. The intricate interaction among these variables affect the forming tonnage which is measured by strain gages mounted on the press. A fault is said to occur when any of these process variables deviate beyond their specified limits. Current detection schemes based on thresholding do not fully exploit the information in the tonnage signals for the detection and isolation of multiple fault condition. It is thus an excellent case study for demonstrating the implementation of the detecti

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Kang, Byungwoo, Wonbin Na, and Hyeongcheol Lee. "Model-Based Fault Analysis and Diagnosis of PEM Fuel Cell Control System." Applied Sciences 12, no. 24 (2022): 12733. http://dx.doi.org/10.3390/app122412733.

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This paper presents a systematic fault analysis and diagnosis method of a PEM fuel cell control system using a model-based approach. With a model-based approach, it is possible to analyze the causal relationship and effect of probable faults in the system, and to diagnose them under the assumption that the model and the process are similar. With a model-based approach, it is possible to analyze the causal relationship and effect of probable faults in the system and diagnose them under the assumption that the model and the process are similar. In this work, a model-based approach was adopted fo

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Wang, Zhiguo, Yawen Gao, Yuan Ge, and Fei Liu. "Fault Isolation for Desalting Processes Using Near-Infrared Measurements." Mathematical Problems in Engineering 2021 (July 14, 2021): 1–9. http://dx.doi.org/10.1155/2021/9954172.

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Due to the important role of crude oil desalting for the whole petroleum refining process, the near-infrared spectroscopy resulting from molecular vibration is used to detect and isolate potential faults of the desalting process in this paper. With the molecular spectral data reflected by the near-infrared spectroscopy, the principal component analysis is adopted to monitor the process to see if it is in a normal operating condition or not. Considering the feature that the dimension of near-infrared spectroscopy is much larger than the sample size, the least absolute shrinkage and selection op

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Cartocci, Nicholas, Marcello R. Napolitano, Francesco Crocetti, Gabriele Costante, Paolo Valigi, and Mario L. Fravolini. "Data-Driven Fault Diagnosis Techniques: Non-Linear Directional Residual vs. Machine-Learning-Based Methods." Sensors 22, no. 7 (2022): 2635. http://dx.doi.org/10.3390/s22072635.

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Linear dependence of variables is a commonly used assumption in most diagnostic systems for which many robust methodologies have been developed over the years. In case the system nonlinearities are relevant, fault diagnosis methods, relying on the assumption of linearity, might potentially provide unsatisfactory results in terms of false alarms and missed detections. In recent years, many authors have proposed machine learning (ML) techniques to improve fault diagnosis performance to mitigate this problem. Although very powerful, these techniques require faulty data samples that are representa

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38

Lee, C., S. W. Choi, and I. B. Lee. "Sensor fault diagnosis in a wastewater treatment process." Water Science and Technology 53, no. 1 (2006): 251–57. http://dx.doi.org/10.2166/wst.2006.027.

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There are many sensors in a wastewater treatment process (WWTP) plant for monitoring process performance and condition. Sensor validation is essential to the success of process monitoring. In this paper, various sensor faults which can occur in WWTP are identified for taking proper remedial action at an early time. A proposed sensor fault isolation method is based on the variable reconstruction using principal component analysis (PCA). Even though several methods have been developed to identify sensor faults, they are only applicable to a static process. In other words, they cannot be successf

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39

Zanoli, Silvia Maria, and Crescenzo Pepe. "Design and Implementation of a Fuzzy Classifier for FDI Applied to Industrial Machinery." Sensors 23, no. 15 (2023): 6954. http://dx.doi.org/10.3390/s23156954.

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In the present work, the design and the implementation of a Fault Detection and Isolation (FDI) system for an industrial machinery is proposed. The case study is represented by a multishaft centrifugal compressor used for the syngas manufacturing. The system has been conceived for the monitoring of the faults which may damage the multishaft centrifugal compressor: instrument single and multiple faults have been considered as well as process faults like fouling of the compressor stages and break of the thrust bearing. A new approach that combines Principal Component Analysis (PCA), Cluster Anal

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40

Boem, Francesca, Riccardo Ferrari, Thomas Parisini, and Marios Polycarpou. "Distributed Fault Diagnosis for Continuous time nonlinear systems: The input-output case." Annual review in Control 37, no. 1 (2013): 163–69. https://doi.org/10.5281/zenodo.1256568.

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In this paper, new results on distributed fault diagnosis of continuous time nonlinear systems with partial state measurements are proposed. Following an overlapping decomposition framework, the dynamics of a nonlinear uncertain large-scale dynamical system are described as the interconnections of several subsystems. Each subsystem is monitored by a corresponding Local Fault Diagnoser: a set of local estimators, based on the nominal local dynamic model and on an adaptive approximation of the interconnection and of the fault function, permit to derive a local fault decision. A consensus-based p

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41

Boulkroune, Boulaïd, Abdel Aitouche, Vincent Cocquempot, Li Cheng, and Zhijun Peng. "Actuator Fault Diagnosis with Application to a Diesel Engine Testbed." Mathematical Problems in Engineering 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/189860.

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This work addresses the issues of actuator fault detection and isolation for diesel engines. We are particularly interested in faults affecting the exhaust gas recirculation (EGR) and the variable geometry turbocharger (VGT) actuator valves. A bank of observer-based residuals is designed using a nonlinear mean value model of diesel engines. Each residual on the proposed scheme is based on a nonlinear unknown input observer and designed to be insensitive to only one fault. By using this scheme, each actuator fault can be easily isolated since only one residual goes to zero while the others do n

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42

Al Hanaineh, Wael, Jose Matas, Josep M. Guerrero, and Mostafa Bakkar. "A Secure Dual-Layer Fault Protection Strategy for Distribution Network with DERs: Enhancing Security in the Face of Communication Challenges." Sensors 24, no. 4 (2024): 1057. http://dx.doi.org/10.3390/s24041057.

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Earlier protection methods mainly focused on using communication channels to transmit trip signals between the protective devices (PDs), with no solutions provided in the case of communication failure. Therefore, this paper introduces a dual-layer protection system to ensure secure protection against fault events in the Distribution Systems (DSs), particularly in light of communication failures. The initial layer uses the Total Harmonic Distortion (THD), the estimates of the amplitude voltages, and the zero-sequence grid voltage components, functioning as a fault sensor, to formulate an adapti

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43

Byrski, Jędrzej, and Witold Byrski. "A double window state observer for detection and isolation of abrupt changes in parameters." International Journal of Applied Mathematics and Computer Science 26, no. 3 (2016): 585–602. http://dx.doi.org/10.1515/amcs-2016-0041.

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Abstract The paper presents a new method for diagnosis of a process fault which takes the form of an abrupt change in some real parameter of a time-continuous linear system. The abrupt fault in the process real parameter is reflected in step changes in many parameters of the input/output model as well as in step changes in canonical state variables of the system. Detection of these state changes will enable localization of the faulty parameter in the system. For detecting state changes, a special type of exact state observer will be used. The canonical state will be represented by the derivati

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44

Djedidi, Oussama, and Mohand Djeziri. "Incremental Modeling and Monitoring of Embedded CPU-GPU Chips." Processes 8, no. 6 (2020): 678. http://dx.doi.org/10.3390/pr8060678.

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This paper presents a monitoring framework to detect drifts and faults in the behavior of the central processing unit (CPU)-graphics processing unit (GPU) chips powering them. To construct the framework, an incremental model and a fault detection and isolation (FDI) algorithm are hereby proposed. The reference model is composed of a set of interconnected exchangeable subsystems that allows it to be adapted to changes in the structure of the system or operating modes, by replacing or extending its components. It estimates a set of variables characterizing the operating state of the chip from on

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45

Murtaza, Ghulam, Aamir I. Bhatti, and Yasir A. Butt. "Super twisting controller-based unified FDI and FTC scheme for air path of diesel engine using the certainty equivalence principle." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 232, no. 12 (2017): 1623–33. http://dx.doi.org/10.1177/0954407017732860.

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This paper proposes a combination of higher order sliding mode and adaptive control for unified fault detection and isolation and fault tolerant control (FTC) of the air path of a diesel engine. Current diesel engines are equipped with features such as variable geometry turbochargers (VGT) and exhaust gas recirculation (EGR) for exhaust emission control. Since EGR and VGT systems are present in the exhaust channel, they are strongly coupled and are prone to both structured as well as unstructured faults. The proposed controller detects and estimates the structured faults by means of adaptation

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46

Yuguang, Niu, Wang Shilin, and Du Ming. "A Combined Markov Chain Model and Generalized Projection Nonnegative Matrix Factorization Approach for Fault Diagnosis." Mathematical Problems in Engineering 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/7067025.

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The presence of sets of incomplete measurements is a significant issue in the real-world application of multivariate statistical process monitoring models for industrial process fault detection. Since the missing data in the incomplete measurements are usually correlated with some of the available variables, these measurements can be used if an efficient algorithm is presented. To resolve the problem, a novel method combining Markov chain model and generalized projection nonnegative matrix factorization (MCM-GPNMF) is proposed to detect and diagnose the faults in industrial process. The basic

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47

Kuang, Te-Hui, Zhengbing Yan, and Yuan Yao. "Multivariate fault isolation via variable selection in discriminant analysis." Journal of Process Control 35 (November 2015): 30–40. http://dx.doi.org/10.1016/j.jprocont.2015.08.011.

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48

Yousefi, Iman, Hamid Khaloozadeh, and Ali Ashraf-Modarres. "Identification and Fault Diagnosis of an Industrial Gas Turbine Using State-Space Methods." Advanced Materials Research 383-390 (November 2011): 1000–1006. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.1000.

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The objective of this paper is to identify, detect and isolate faults to an industrial gas turbine. The detection scheme is based on the generation of so-called "residuals" that are errors between estimated and measured variables of the process. A State-Space model is used for identification and some observer-based methods are used for residual generation, while for residual evaluation a neural network classifier for MLP is used. The proposed fault detection and isolation tool has been tested on a single-shaft industrial gas turbine simulator.

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49

Kazemi, Pezhman, Jaume Giralt, Christophe Bengoa, Armin Masoumian, and Jean-Philippe Steyer. "Fault detection and diagnosis in water resource recovery facilities using incremental PCA." Water Science and Technology 82, no. 12 (2020): 2711–24. http://dx.doi.org/10.2166/wst.2020.368.

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Abstract Because of the static nature of conventional principal component analysis (PCA), natural process variations may be interpreted as faults when it is applied to processes with time-varying behavior. In this paper, therefore, we propose a complete adaptive process monitoring framework based on incremental principal component analysis (IPCA). This framework updates the eigenspace by incrementing new data to the PCA at a low computational cost. Moreover, the contribution of variables is recursively provided using complete decomposition contribution (CDC). To impute missing values, the empi

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50

Lin, Tzu-Kang, Tappiti Chandrasekhara, Zheng-Jia Liu, and Ko-Yi Chen. "Verification of a Stiffness-Variable Control System with Feed-Forward Predictive Earthquake Energy Analysis." Sensors 21, no. 22 (2021): 7764. http://dx.doi.org/10.3390/s21227764.

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Semi-active isolation systems with controllable stiffness have been widely developed in the field of seismic mitigation. Most systems with controllable stiffness perform more robustly and effectively for far-field earthquakes than for near-fault earthquakes. Consequently, a comprehensive system that provides comparable reductions in seismic responses to both near-fault and far-field excitations is required. In this regard, a new algorithm called Feed-Forward Predictive Earthquake Energy Analysis (FPEEA) is proposed to identify the ground motion characteristics of and reduce the structural resp

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