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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 like the ordinary Chisquare (?2) test method, generalized likelihood ratio test have limitations such as the "false alarm" problem. From the results it is inferred that the proposed FDI scheme diagnoses better when compared to other conventional methods.
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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-loop via Indirect Rotor Flux Oriented Control (IRFOC) scheme was also described. Hence, the performance of the wind turbine system and the stability of injected power to the grid were analyzed under the ITSC fault conditions. The control schemes were proved to be inherently unstable under the faulty conditions. Then, robust SMO were investigated to design an ITSC fault detection and isolation scheme. Finally, simulation results of ITSC detection and isolation in the variable-speed grid-connected wind turbine with affected IG confirm the theoretical development.
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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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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 reconstruction algorithm and bivariate scatter plots are proposed for fault isolation, following the application of NLPCA. The proposed technique allows the diagnosis of different fault types under steady state operating conditions. More precisely, non-linear variable reconstruction can remove the fault signature from the recorded engine data, which allows the identification and isolation of the root cause of abnormal engine behaviour. The paper shows that this can lead to (a) an enhanced identification of potential root causes of abnormal events and (b) the masking of faulty sensor readings. The effectiveness of the enhanced NLPCA-based monitoring scheme is illustrated by its application to a sensor fault and a process fault. The sensor fault relates to a drift in the fuel flow reading, while the process fault relates to a partial blockage of the intercooler. These faults are introduced to a Volkswagen TDI 1.9 litre diesel engine mounted on an experimental engine test bench facility.
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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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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 being diagnosed. The residual between the estimated value of the diagnosis soft sensor and the measurement of the physical sensor is used as an indicator of the sensor’s condition. If the residual exceeds the fault threshold, the sensor is diagnosed to be faulty. In order to maintain valid sensor output, an accommodation soft sensor is constructed using the historical normal value. The erroneous output of the faulty sensor is substituted by the estimated value from the accommodation soft sensor, thereby realizing sensor fault tolerance control. Experimental results demonstrate that the average false alarm rate for sensor fault diagnosis is 1.57% and the average fault diagnosis rate is 96.51%. The predictive mean absolute error (MAE) and root-mean-square error (RMSE) of the recovered soft sensors are 0.0525 and 0.0738, respectively. Thus, the soft sensors developed in this paper exhibit satisfying ability in HVAC SFDIA.
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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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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 train speed a random nonstationary vehicle excitation. Speech recognition techniques were used to generate features that consider this phenomenon. Frequency spectrums were analysed in different operating conditions to design efficient features. The robustness of the methodology was verified with data from two different test measurement campaigns on a test ring, where the influence of the sensor locations for the fault classification process was studied. The proposed methodology achieved good fault classification performance on the investigated use cases, removed dampers and 50% damper degradation on primary and secondary vertical suspension.
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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 approach to detect, locate, and isolate the single and/or multiple real-phase current sensors in both converters. The suggested approach uses an extended Kalman filter (EKF) bank structured according to a generalized observer scheme (GOS) and relies on a nonlinear model for the RSC and a linear model for the GSC. The EKF estimates the currents in the converters, which are then compared to sensor measurements to generate residuals. These residuals are then processed in the localization, isolation, and decision blocks to precisely identify faulty sensors. The obtained results confirm the effectiveness of this approach to identify faulty sensors in the abc phases. It also demonstrates its ability to overcome the nonlinearity induced by wind fluctuations, as well as resolves the coupling issue between currents in the fault period.
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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 samples ranged from 102 to 105 cfu/ml. Specific pathogens such as, Salmonella spp., Shigella spp., Vibrio spp. and fecal coliforms could not be found in these samples. However coliforms could be detected in 10 samples. The antibiogram study showed that all Escherichia coli and Klebsiella spp. isolates found from these samples were sensitive against gentamicin (10 μg) and azithromycin (30 μg). Variable antibiotic resistance among these bacterial isolates was detected against cefotaxime (30 μg), streptomycin (10 μg) and erythromycin (15 μg).
 Stamford Journal of Microbiology, Vol.9(1) 2019: 12-14
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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 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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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 acceleration process is optimized based on the target shooting method, aiming to simulate the variation of various variables in the engine acceleration process more accurately, so as to verify the feasibility of the sensor fault-tolerant control scheme. Finally, a hardware-in-loop simulation platform is built based on the idea of distributed control, and the fault-tolerant control scheme of the sensor proposed previously is verified based on this platform. The results show that the proposed scheme can accurately diagnose the sensor faults and reconstruct the signal within 0.2 s, and the actual speed can rise from 67.87% to 99.9% in 4 s, ensuring the safe and rapid completion of the acceleration process.
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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 that should be maintained below certain thresholds. This paper proposes to adapt the PCA-RBC method to cope with such limitations. The idea is first to classify the data onto normal and abnormal conditions according to a selected parameter threshold, and then to build a PCA model using the normal dataset. The RBC approach is applied on the abnormal dataset to identify the variables that mostly contribute to the faulty situations. The proposed method is successfully demonstrated using real data from an industrial case. It is noted that an attempt to develop an accurate predictive model of the selected parameter using projection to latent structures (PLS) was unsuccessful.
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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 method is reduced rank interval kernel principal component analysis (RRIKPCA), which enhances the computational skill by downgrading the kernel matrix dimension. We have proposed in this article a new fault localization technique for uncertain systems, named partial RRIKPCA, which combines the benefits of the RRIKPCA technique and the principle of partial localization. The principal of this method involves selecting partially reduced rank data subsets and then building more accurate models with fewer PCs and isolating faults with higher precision. The proposed fault isolation method is applied for monitoring air quality monitoring network (AIRLOR) data.
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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 deepened. Two black-box sub-models were developed to simulate the above described faulty functioning of the free-coolers. Subsequently, the fault signature matrix was developed, through which the “symptoms”, calculated as residuals between the “faulty” and “non faulty” conditions of the monitored variables, are associated to the corresponding faults. The peculiarity of the telecommunication sector, where nowadays data acquisition and monitoring platforms are significantly spreading to monitor most significant energy consumptions, including cooling loads, was proved essential in guaranteeing effective isolation of different faults. The simulation results highlight the reliability of the developed diagnostic tool, expected to be versatile and easy to implement enough for being extended to air-handling unit diagnosis, as well as other industrial sectors.
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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 growth to signalize a fault’s occurrence while individually evaluating each monitored variable to provide fault detection and prognosis. Additionally, the paper also provides an appropriate set of metrics to measure the accuracy of the models, which is a common disadvantage of unsupervised methods due to the lack of predefined answers during training. Computational results using the Commercial Modular Aero Propulsion System Simulation (CMAPSS) monitoring data show the effectiveness of the proposed framework.
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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 controller constitutes the outer loop FTC scheme, and the adaptive dynamic surface combined with the disturbance estimation constitutes the inner loop robust controller; these controllers finally realize the direct compensation of the compound sensor faults under the disturbance condition. This scheme does not require fault isolation and diagnosis observer loops; it only uses a variable structure FTC with a direct estimation algorithm and integrated residual to complete the self-repairing stable flight of variable-structure HFV, which exhibits a high reliability and quick response. Lyapunov theory proved the stability of the system, and numerical simulation proved the effectiveness of the FTC scheme.
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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, the existing fault isolation methods demand high computational power or complicated procedures because the overactuated systems have many actuators, and the number of simultaneous fault occurrences is increased. The method proposed in this paper exploits the concept of sparsity. The underdetermined linear system is defined from the parity equation, and fault isolation is achieved by obtaining the sparsest nonzero component of the residuals from the minimal ℓ1-norm solution. Therefore, the locations of the faults can be obtained in a sequence, and only a consistently low computational load is required regardless of the isolated number of faults. The experimental results obtained with a scaled-down overactuated EV support the effectiveness of the proposed method, and a quantitative index of the sparsity condition for the target EV is discussed with a CarSim-connected MATLAB/Simulink simulation.
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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 gains coming from the solution to an optimization problem. The proposed nonlinear observer was tested both numerically on a large database of simulations in the presence of disturbances and model uncertainties and on input-output data recorded during real flights. In this case, the possibility of successfully applying the proposed technique to detect and isolate faults on inertial and air data sensors, modelled as step or ramp signals artificially added to the real measurements, is shown.
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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 are considered, residual evaluation functions and adaptive threshold are given to judge if the faults occur. Fault isolation is implemented by a set of detection subspaces associated with every different fault which is assigned to its own detection subspace. The residual is not only sensitive to the fault, but also has a robustness against the non-decoupling disturbance and uncertainties in finite frequency. Simulation examples are given to demonstrate the effectiveness of this method.
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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 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 signals whose amplitudes remain within specifications. They also provide little or no information about the multiplicity (number of faults in the same process cycle) or location (the portion of the cycle where the fault was detected) of the fault condition. A method of overcoming these limitations is proposed in this paper. The Haar transform is used to generate sets of detection signals from the original measurements of process monitoring signals. By partitioning these signals into disjoint segments, mutually exclusive sets of Haar coefficients can be used to locate faults at different phases of the process. The lack of a priori information on fault condition is overcomed by using the Neyman-Pearson criteria for the uniformly most powerful form (UMP) of the likelihood ratio test (LRT).
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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 and as such is appropriate for fault detection. In supervised training input-output pairs, both for normal and fault conditions, are presented to the network. The models were trained on the free fault and fault sensors. Then the Quadratic Support Vector Machine (QSVM) and k-Nearest Neighbor (KNN) as the classifiers are used. The test results for measuring the performance of 1232 sample classifiers from data show that the accuracy of fault-free sensor recognition is 97.4 % and 100% consecutively for these classifiers.
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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 neighbors they share, thereby readjusting the distance values based on the shared neighbors. Moreover, the data distribution is not constrained; therefore, it can be applied to various processes. Unlike principal component analysis and independent component analysis, which are widely used to identify fault variables, the main advantage of SNN is that it does not suffer from smearing effects, because it calculates the contributions from the original input space. The proposed method is applied to two case studies and to the failure case of a real circulating fluidized bed boiler to confirm its effectiveness. The results show that the proposed method can detect faults earlier (1 h 39 min 46 s) and identify fault variables more effectively than conventional methods.
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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 model and tracking changes in the values of selected control loop performance indicators to supervise the control circuit. A binary diagnostic matrix was used to isolate anomalies. The presented approach requires only standard operating data (process variable (PV), setpoint (SP), and control signal (CV). The proposed concept was tested using the example of a control system for superheaters in a steam line of a power unit boiler. Cyber-attacks targeting other parts of the process were also included in the study to test the proposed approach’s applicability, effectiveness, and limitations and identify further research directions.
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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 squares- k-nearest neighbor rule, which organically incorporates k-nearest neighbor rule with kernel partial least squares. Specifically, we first employ kernel partial least squares to establish a non-linear regression model between quality variables and process variables. After that, the statistics and thresholds corresponding to process space and predicted quality space are appropriately designed by adopting k-nearest neighbor rule. In the fault isolation stage, in order to match our proposed non-linear quality-related fault detection method kernel partial least squares- k-nearest neighbor seamlessly, we propose a modified variable contributions by k-nearest neighbor (VCkNN) fault isolation method called modified variable contributions by k-nearest neighbor (MVCkNN), which elaborately introduces the idea of the accumulative relative contribution rate into VC k-nearest neighbor, such that the smearing effect caused by the normal distribution hypothesis of VC k-nearest neighbor can be mitigated effectively. Finally, a widely used numerical example and the Tennessee Eastman process are employed to verify the effectiveness of our proposed approach.
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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 experimental setup of arbitrarily induced fault scenarios, the extended isolation forest (EIF) approach was used to detect anomalies in OES data compared with the conventional isolation forest method in terms of accuracy and speed. We also used the OES data to generate features related to electron temperature and found that using the electron temperature features together with equipment status variable identification data (SVID) and OES data improved the prediction accuracy of process/equipment fault detection by a maximum of 0.84%.
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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 (resp., reaction wheels) has been considered. The test results demonstrate that the fault detection and isolation are successfully accomplished.
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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 while adding additional design variables to reduce the conservatism. A numerical simulation is carried out in order to demonstrate the effectiveness of the proposed approach.
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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 is used as a system-wide health indicator. Furthermore, fault isolation is achieved by a reconstruction-based two-dimensional contribution map, in which the variables with larger contributions are responsible for the detected fault. To demonstrate the effectiveness of the proposed scheme, two case studies are carried out based on a multivariate data set from a pump system in an oil and petrochemical factory. The classical principal component analysis (PCA) method is compared with the proposed method and results show that SAE performs better in terms of fault detection than PCA, and can effectively isolate the abnormal variables, which can hence help effectively trace the root cause of the detected fault.
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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 for fault analysis and diagnosis, and its methods are suggested. A PEM fuel cell is mathematically modelled, analyzed, and verified for the analysis and simulations. Relationships among variables are shown using an incidence matrix and with a Dulmage–Mendelsohn decomposition of the matrix. When it is difficult to detect faults due to a deficient degree of redundancy, a bi-partite graph is used to analyze the effect of faults and to assess the possibility of fault detection through the appropriate redundant sensor placement. Thereafter, residuals are obtained based on analytical redundancies of the system, and a fault signature matrix is subsequently constructed. A fault detection and isolation (FDI) algorithm is developed based on a fault signature matrix that describes the connection between faults and residuals. The simulation results demonstrate the validity and effectiveness of the proposed FDI algorithm for diagnosing faults. With the proposed FDI algorithm, eight faults could be diagnosed by FDI algorithm with given sensors in the system.
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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 earthquakes representing a wide variety of ground motions are considered as the ground excitations in the simulation. The numerical show that, compared with conventional isolation systems, the semi-active isolation system controlled by the STFT algorithm can reduce the excessive response in near-fault earthquakes effectively.
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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 detection methodology presented in Part 1. By partitioning the tonnage signature into disjoint segments, mutually exclusive sets of Haar coefficients can be used to isolate faults in each stage of the process.
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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 operator is employed to achieve an automatic variable selection procedure of the observed spectral data. Simultaneously, if some faults occur, the least absolute shrinkage and selection operator can be used to locate the spectral region affected by the failure. In such a way, the roots of faults can be tracked according to the change of the wavelength numbers. Performances of the proposed fault detection and isolation approaches are evaluated based on the near-infrared spectroscopy sampled for the crude oil desalting process to show the effectiveness.
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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 representative of any fault scenario. Additionally, ML techniques suffer from issues related to overfitting and unpredictable performance in regions which are not fully explored in the training phase. This paper proposes a non-linear additive model to characterize the non-linear redundancy relationships among the system signals. Using the multivariate adaptive regression splines (MARS) algorithm, these relationships are identified directly from the data. Next, the non-linear redundancy relationships are linearized to derive a local time-dependent fault signature matrix. The faulty sensor can then be isolated by measuring the angular distance between the column vectors of the fault signature matrix and the primary residual vector. A quantitative analysis of fault isolation and fault estimation performance is performed by exploiting real data from multiple flights of a semi-autonomous aircraft, thus allowing a detailed quantitative comparison with state-of-the-art machine-learning-based fault diagnosis algorithms.
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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 successfully used to monitor severe dynamic processes such as WWTPs. We have removed this limitation by developing reconstruction methods based on a dynamic version of PCA. Artificial scenarios of sensor faults generated from the simulation benchmark have been used to validate the proposed sensor identifying methodology. Also, it is compared to a previous method to show its relative superiority in sensor fault validation in the WWTP.
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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 Analysis and Pattern Recognition is developed. A novel procedure based on the statistical test ANOVA (ANalysis Of VAriance) is applied to determine the most suitable number of Principal Components (PCs). A key design issue of the proposed fault isolation scheme is the data Cluster Analysis performed to solve the practical issue of the complexity growth experienced when analyzing process faults, which typically involve many variables. In addition, an automatic online Pattern Recognition procedure for finding the most probable faults is proposed. Clustering procedure and Pattern Recognition are implemented within a Fuzzy Faults Classifier module. Experimental results on real plant data illustrate the validity of the approach. The main benefits produced by the FDI system concern the improvement of the maintenance operations, the enhancement of the reliability and availability of the compressor, the increase in the plant safety while achieving reduction in plant functioning costs.
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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 not. A decision algorithm based on multi-CUSUM is used. The performances of the proposed approach are shown through a real application to a Caterpillar 3126b engine.
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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 adaptive algorithm based on a Finite State Machine (FSM) for the detection and isolation of faults within the grid. This layer primarily relies on communication protocols for effective coordination. A Second-Order Generalized Integrator (SOGI) expedites the derivation of the estimated variables, ensuring fast detection with minimal computational overhead. The second layer uses the behavior of the positive- and negative-sequence components of the grid voltages during fault events to locate and isolate these faults. In the event that the first layer exposes a communication failure, the second layer will automatically be activated to ensure secure protection as it operates, using the local information of the Protective devices (PDs), without the need for communication channels to transmit trip signals between the PDs. The proposed protection system has been assessed using simulations with MATLAB/Simulink and providing experimental results considering an IEEE 9-bus standard radial system. The obtained results confirm the capability of the system for identifying and isolating different types of faults, varying conditions, and modifications to the grid configuration. The results show good behavior of the initial THD-based layer, with fast time responses ranging from 6 to 8.5 ms in all the examined scenarios. In contrast, the sequence-based layer exhibits a protection time response of approximately 150 ms, making it a viable backup option in the event of a communication failure.
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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 derivatives of the measured output signal. Hence the exact state observer will play the role of virtual sensors for reconstruction of the derivatives of the output signal. For designing the exact state observer, the model parameters before and after the moment of fault occurrence must be known. To this end, a special identification method with modulating functions will be used. A novel concept presented in this paper concerns the structure of the observer. It will take the form of a double moving window observer which consists of two signal processing windows, each of width T. These windows are coupled to each other with a common edge. The right-hand side edge of the left-side moving window in the interval [t − 2T, t − T ] is connected to the left-hand side edge of the right-side window which operates in the interval [t − T, t]. The double observer uses different measurements of input/output signals in both the windows, and for each current time t simultaneously reconstructs two values of the state—the final value of the state in the left-side window zT (t − T) and the initial value of the state z0(t − T) in the right-side window. If the process parameters are constant, the values of both the states on the common joint edge are the same. If an abrupt change (fault) in some parameter at the moment tA = t − T occurs in the system, then step changes in some variables of the canonical state vector will also occur and the difference between the states will be detected. This will enable localization of the faulty parameter in the system.
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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 only two global inputs. Then, through analytical redundancy, the estimated variables are compared to the output of the system in the FDI module, which generates alarms in the presence of faults or drifts in the system. Furthermore, the interconnected nature of the model allows for the direct localization and isolation of any detected abnormalities. The implementation of the proposed framework requires no additional instrumentation as the used variables are measured by the system. Finally, we use multiple experimental setups for the validation of our approach and also proving that it can be applied to most of the existing embedded systems.
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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 laws, designed by making use of the certainty equivalence principle. Fault effects are compensated by repositioning the actuators. This allows relaxation of the boundedness condition of the super twisting algorithm, as sliding mode controller gains are required to dominate the unstructured parts only, which consequently reduces chattering. A nonlinear multi-input multi-output reduced state control-oriented model has been employed for working out the FTC strategy for EGR and VGT actuators. The stability of the overall system has been analysed using the Lyapunov stability criterion. Faults and proposed controllers are simulated using a fully validated industrial scale diesel engine model to establish the effectiveness of the algorithm.
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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 idea of the approach is to use MCM-GPNMF to extract the dominant variables from incomplete process data and to combine them with statistical process monitoring techniques. TG2 and SPEG statistics are defined as online monitoring quantities for fault detection and corresponding contribution plots are also considered for fault isolation. The proposed method is applied to a 1000 MW unit boiler process. The simulation results clearly illustrate the feasibility of the proposed method.
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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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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 empirical best linear unbiased prediction (EBLUP) method is incorporated into this framework. The effectiveness of this framework is evaluated using benchmark simulation model No. 2 (BSM2). Our simulation results show the ability of the proposed approach to distinguish between time-varying behavior and faulty events while correctly isolating the sensor faults even when these faults are relatively small.
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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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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 responses to earthquakes. The energy distribution of the seismic velocity spectrum is considered, and the balance between the kinetic energy and potential energy is optimized to reduce the seismic energy. To demonstrate the performance of the FPEEA algorithm, a two-degree-of-freedom structure was used as the benchmark in the numerical simulation. The peak structural responses under two near-fault and far-field earthquakes of different earthquake intensities were simulated. The isolation layer displacement was suppressed most by the FPEEA, which outperformed the other three control methods. Moreover, superior control on superstructure acceleration was also supported by the FPEEA. Experimental verification was then conducted with shaking table test, and the satisfactory performance of the FPEEA on both isolation layer displacement and superstructure acceleration was demonstrated again. In summary, the proposed FPEEA has potential for practical application to unexpected near-fault and far-field earthquakes.
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Deng, Xiaogang, and Zheng Zhang. "Nonlinear Chemical Process Fault Diagnosis Using Ensemble Deep Support Vector Data Description." Sensors 20, no. 16 (2020): 4599. http://dx.doi.org/10.3390/s20164599.
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As one classical anomaly detection technology, support vector data description (SVDD) has been successfully applied to nonlinear chemical process monitoring. However, the basic SVDD model cannot achieve a satisfactory fault detection performance in the complicated cases because of its intrinsic shallow learning structure. Motivated by the deep learning theory, one improved SVDD method, called ensemble deep SVDD (EDeSVDD), is proposed in order to monitor the process faults more effectively. In the proposed method, a deep support vector data description (DeSVDD) framework is firstly constructed by introducing the deep feature extraction procedure. Different to the traditional SVDD with only one feature extraction layer, DeSVDD is designed with multi-layer feature extraction structure and optimized by minimizing the data-enclosing hypersphere with the regularization of the deep network weights. Further considering the problem that DeSVDD monitoring performance is easily affected by the model structure and the initial weight parameters, an ensemble DeSVDD (EDeSVDD) is presented by applying the ensemble learning strategy based on Bayesian inference. A series of DeSVDD sub-models are generated at the parameter level and the structure level, respectively. These two levels of sub-models are integrated for a holistic monitoring model. To identify the cause variables for the detected faults, a fault isolation scheme is designed by applying the distance correlation coefficients to measure the nonlinear dependency between the original variables and the holistic monitoring index. The applications to the Tennessee Eastman process demonstrate that the proposed EDeSVDD model outperforms the traditional SVDD model and the DeSVDD model in terms of fault detection performance and can identify the fault cause variables effectively.
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Boukerdja, Mahdi, Youness Radi, Omprakash, et al. "LFT Bond Graph for Online Robust Fault Detection and Isolation of Hybrid Multi-Source System." Journal of Physics: Conference Series 2065, no. 1 (2021): 012010. http://dx.doi.org/10.1088/1742-6596/2065/1/012010.
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Abstract Green hydrogen is undoubtedly the most promising energy vector of the future because it is captured by renewable and inexhaustible sources, such as wind and/or solar energy, and can be stored over the long in high-pressure cylinders, which can be used to feed the fuel cells to produce the electricity without emitting any pollutants. The system incorporated renewable sources and process used to produce the green hydrogen is the hybrid multi-source system (HMS). The production of hydrogen needs a reliable HMS, which always requires online monitoring for real-time Fault Detection and Isolation (FDI) because the risk of accidents in HMS and safety issues increases due to the possibility of faults. However, online monitoring of FDI is challenging due to the multi-physics dynamics of HMS and the inclusion of uncertain parameters and several disturbances. This paper proposes an online robust fault detection algorithm to detect system faults based on the properties of the graphical linear fractional transformation bond graph (LFT-BG) modeling approach. Here, the analytical redundancy relations (ARRs) and their uncertain parts extracted from the LFT-BG model are used to develop an online robust FDI algorithm for HMS. Numerical evaluations of ARRs and their uncertain parts, respectively, generate the residual signals known as “faults indicators” and their uncertain bounds known as “adaptive thresholds.” These thresholds evolve with system variables in the presence of parameter uncertainties for ensuring robust FDI for HMS to minimize false alarms. The validation of this approach is carried out using 20sim software that is familiar with BG modeling.
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Ulmer, Markus, Eskil Jarlskog, Gianmarco Pizza, Jaakko Manninen, and Lilach Goren Huber. "Early Fault Detection Based on Wind Turbine SCADA Data Using Convolutional Neural Networks." PHM Society European Conference 5, no. 1 (2020): 9. http://dx.doi.org/10.36001/phme.2020.v5i1.1217.
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Early fault detection in wind turbines using the widely available SCADA data has been receiving growing interest due to its cost-effectiveness. As opposed to the large variety of fault detection methods based on high resolusion vibration data, the use of 10-minute SCADA data alone does not require any additional hardware or data storage solutions and would be immediately implementable in most wind farms. However, the strong variability of these data is challenging and requires significant improvements of existing methods to ensure early and reliable fault detection and isolation. Here we suggest to use Convolutional Neural Networks (CNNs) to enhance the detection accuracy and robustness. We demonstrate the superiority of the CNN model over standard fully connected neural networks (FCNN) using examples for faults with very different time dependent characteristics: an abruptly evolving and a slowly degrading fault. We show that the CNN is able to detect the faults earlier and with a higher accuracy and robustness of prediction than the FCNN model. We then extend the CNN model to a multi-output CNN (CNNm) which provides early fault detection based on a multitude of output variables simultaneously. We show that with the same training time and a similar detection quality as the single output CNN, the CNNm model is an ideal candidate for a practical and scalable fault detection algorithm based on already available 10-minute SCADA data for wind turbines.
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Wang, Kaibo, and Wei Jiang. "High-Dimensional Process Monitoring and Fault Isolation via Variable Selection." Journal of Quality Technology 41, no. 3 (2009): 247–58. http://dx.doi.org/10.1080/00224065.2009.11917780.
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