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Item type:Publication, Teaching Model-based Fault Detection and Isolation using a Virtual Laboratory Environment(Elsevier, 2020-01-01)Fault detection and isolation (FDI) systems play a key role to provide efficiency, reliability and safety in today's industrial processes. The teaching of FDI systems is facilitated if it is carried out not only with theoretical lectures but also with practical experiences. This paper proposes a virtual laboratory environment (VLE) to carry out online practical experiences with FDI systems for a benchmark process. Thanks to this VLE, students can set up faults in sensors, actuators or in the process itself, program model-based FDI algorithms and test FDI system performance. The use of this environment is illustrated by testing the performance of FDI systems for the quadruple-tank process (4TP) under different fault scenarios. Finally, the procedure of using this proposal for practical experience with two model-based FDI design methods is shown.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Fault detection and isolation system based on structural analysis of an industrial seawater reverse Osmosis desalination plant(MDPI, 2020-09-01)Currently, the use of industrial seawater reverse osmosis desalination (ISROD) plants has increased in popularity in light of the growing global demand for freshwater. In ISROD plants, any fault in the components of their control systems can lead to a plant malfunction, and this condition can originate safety risks, energy waste, as well as affect the quality of freshwater. This paper addresses the design of a fault detection and isolation (FDI) system based on a structural analysis approach for an ISROD plant located in Lima (Peru). Structural analysis allows obtaining a plant model, which is useful to generate diagnostic tests. Here, diagnostic tests via fault-driven minimal structurally overdetermined (FMSO) sets are computed, and then, binary integer linear programming (BILP) is used to select the FMSO sets that guarantee isolation. Simulations shows that all the faults of interest (sensors and actuators faults) are detected and isolated according to the proposed design. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Design of a robust H2 state feedback temperature controller for a steel slab reheating furnace(MDPI, 2020-03-01)This article addresses the design of a robust H2 state feedback controller (H2-SFC) for the effective temperature control in the heating zone of the steel slab reheating furnace. Based on the available field data and system identification procedures, a mathematical model of the heating zone is derived, which presents autoregressive-moving average with exogenous input (ARMAX) structure and fourth order. The design of an H2-SFC controller for the effective control of the heating zone temperature of the slab reheating furnace under study is developed. The simulation results of the designed control system showed its high effectiveness compared to the conventional PID control. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Super-alarms with diagnosis proficiency used as an additional layer of protection applied to an oil transport system(MDPI AG, 2021-02-01)In automated plants, particularly in the petrochemical, energy, and chemical industries, the combined management of all of the incidents that can produce a catastrophic accident is required. In order to do this, an alarm management methodology can be formulated as a discrete event sequence recognition problem, in which time patterns are used to identify the safe condition of the process, especially in the start-up and shutdown stages. In this paper, a new layer of protection (a Super-Alarm), based on the diagnostic stage to industrial processes is presented. The alarms and actions of the standard operating procedures are considered to be discrete events involved in sequences; the diagnostic stage corresponds to the recognition of the situation when these sequences occur. This provides operators with pertinent information about the normal or abnormal situations induced by the flow of the alarms. Chronicles Based Alarm Management (CBAM) is the methodology used in this document to build the chronicles that will permit us to generate the Super-Alarms; in addition, a case study of the petrochemical sector using CBAM is presented in order to build one chronicle that represents the scenario of an abnormal start-up of an oil transport system. Finally, the scenario’s validation for this case is performed, showing the way in which, a Super-Alarm is generated. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Distributed fault detection and isolation approach for oil pipelines(MDPI, 2021-12-01)Fault detection and isolation (FDI) in oil pipeline systems (OPS) is a very critical issue because faults in these systems such as leaks or equipment malfunctions may cause significant safety accidents and economic losses. These are the challenging factors, along with the environmental regulations for developing efficient FDI approaches for OPS. This paper proposes a model-based distributed FDI approach, which uses a structural model of the system in conjunction with algorithms to generate diagnostic tests that may be implemented in local diagnosers along the OPS. The proposed approach allows detection and isolation of faults in pipeline sections (pipeline segments), pump stations, as well as process control equipment. In this way, simulation of the obtained diagnostic tests in a benchmark application shows that all faults of interest (pipeline segment faults and sensor faults) are detected and isolated. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Fault diagnosis via neural ordinary differential equations(MDPI AG, 2021-05-01)Implementation of model-based fault diagnosis systems can be a difficult task due to the complex dynamics of most systems, an appealing alternative to avoiding modeling is to use machine learning-based techniques for which the implementation is more affordable nowadays. However, the latter approach often requires extensive data processing. In this paper, a hybrid approach using recent developments in neural ordinary differential equations is proposed. This approach enables us to combine a natural deep learning technique with an estimated model of the system, making the training simpler and more efficient. For evaluation of this methodology, a nonlinear benchmark system is used by simulation of faults in actuators, sensors, and process. Simulation results show that the proposed methodology requires less processing for the training in comparison with conventional machine learning approaches since the data-set is directly taken from the measurements and inputs. Furthermore, since the model used in the essay is only a structural approximation of the plant; no advanced modeling is required. This approach can also alleviate some pitfalls of training data-series, such as complicated data augmentation methodologies and the necessity for big amounts of data. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Fault detection and isolation for UAVs using neural ordinary differential equations(Elsevier B.V., 2022-01-01)In recent years, the increasing complexity and diversity of data-based fault detection and isolation (FDI) methods usually require high computational efforts in the pre-processing stage, large amounts of data, and, most of the time, some feature extraction to obtain relevant information for the data-based algorithms. This paper proposes using the Neural Ordinary Differential Equations (NODE) framework to represent the dynamics of the studied plant and later employ such representation in FDI system design. Such an approach enables loss optimization to be performed jointly in the plant dynamics and external inputs without previous use of complex pre-processing and is useful for working with nonlinear systems. The approach is first validated using a simulated Unmanned Aerial Vehicle (UAV) and later applied to a data-set that contains actuators and sensors faults. Ultimately, the proposed approach is compared with other usual machine learning techniques, showing better performance metrics. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Design of a dead-time compensator robust H∞ state feedback temperature controller for a precalciner of a cement rotary kiln(MDPI, 2022-03-01)A dead‐time compensator robust H∞ state feedback controller (DTC‐H∞‐SFC) for the temperature control in a precalciner of a cement rotary kiln is designed. A mathematical model of the process under study with ARMAX structure was obtained. A dead‐time compensator robust H∞ state feedback controller is therefore designed. The results of the comparative evaluation of the DTC‐H∞‐SFC vs. DTC+PI designed controllers showed that the DTC‐H∞‐SFC gives improved performance of the control system. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Adaptive Smith predictor controller design for industrial processes with time-varying time delay(Elsevier B.V., 2024-01-01)The design of an adaptive Smith predictor controller for control of industrial hydraulic processes subjected to the simultaneous effect of load disturbances and time-varying time-delay is developed in this paper. In order to improve the performance of the Smith predictor, an adaptive block is introduced into the control structure to estimate and update the current value of the time-varying time-delay. Furthermore, a disturbance compensator is introduced to reject the effect of load disturbances. Simulations of the control system are carried out with the proposed controller and with a classical Smith predictor. The comparison of the obtained results shows the higher performance of our proposed controller, both in rejecting load disturbances and in maintaining the closed-loop stability when the time-delay is time-varying. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Enhanced leakage detection and estimation via a hybrid genetic algorithm and high-order sliding modes observer approach(Institute of Electrical and Electronics Engineers Inc., 2024-01-01)This paper introduces a hybrid approach designed for both detecting and estimating the magnitude of leaks in oil pipelines. The method integrates a High Order Sliding Mode Observer(HOSMO) with a Super Twisting Algorithm to serve as an observer for state estimation of the system. A parameterized model based on momentum and mass balance equations with discretization is used, where the parameters are the location and magnitude of the leakage. To find these parameters, it incorporates the Genetic Algorithm to solve an optimization problem that relies on a function cost related to the error norm between measurements and states estimation from HOSMO in order to measure the difference between the model with an assumed leakage and the real leakage. The solution of the minimization problem represents the leak position and magnitude. The feasibility and effectiveness of this method are evaluated using a simulation model representing a 306 km sector of the North-Peruvian Oil Pipeline. The results demonstrate its robustness against noise, showcasing a precision of ±250 m in pinpointing the location of leaks.