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Item type:Publication, Soft Sensor Design for Restricted Variable Sampling Time(Elsevier, 2020-01-01)Difficult-to-obtain variables in industrial applications have led to the rise of soft sensors, which use prior system information and measurements to estimate these difficult-to-obtain variables. In real systems, the measurements that need to be estimated by a soft sensor are often infrequently measured or delayed. Sometimes, these delays and sampling time are variable in time. Though there are papers considering soft sensors in the presence of time delays and different sampling times, the variation of those parameters has not been considered when evaluating the adequacy of the soft sensors. Therefore, this paper will evaluate the impact of such variations for a data-driven soft sensor and propose modifications of the soft sensor that increase its robustness. The reliability of its estimate will be shown using the Bauer-Premaratne-Durán Theorem. Furthermore, the soft sensor will be simulated applying it to a continuous stirred tank reactor. Simulation showed that the modified soft sensor gives good estimates, whereas the traditional soft sensor gives an unstable estimate. - Some of the metrics are blocked by yourconsent settings
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, Simultaneous occurrences and false-positives analysis in discrete event dynamic systems(Elsevier, 2020-07-01)Simultaneous occurrences of events have been a crucial and hard problem since the beginning of the research about automaton and simulation theories of discrete event systems, for more than 50 years. This article addresses some diagnosis problems in industrial processes, situations such as simultaneity of events, false positives, and partial recognition of event sequences. V-nets are presented as a means to model dynamic processes without the state machine concept and, the robustness and capability to identify different sequences of discrete events. With the V-nets formalism, it is possible to identify the evolution of the discrete events, simultaneous occurrences of events, partial recognition, counting the number of times that each discrete event occurred in a temporal sequence and this formalism also has the capability to model sequences of sequences. An example of one industrial application is presented and a comparative analysis of the Time Petri Nets, Timed Automata, and Chronicles with the V-nets is exposed. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Soft sensor design for variable time delay and variable sampling time(Elsevier, 2020-08-01)Often industrial variables can be difficult to measure due to such factors as extreme conditions or complex compositions. In such cases, soft sensors have been developed that use available system information and measurements to estimate these difficult-to-obtain variables. In practice, the measurements that are to be estimated by a soft sensor are often infrequently measured or delayed. Occasionally, these sampling times or delays are time varying. At present, most research has considered these parameters to be time invariant, and thus, there is a need to consider the time-varying case. Therefore, this paper will evaluate the impact of time-varying delays and sampling times for the design of a data-driven soft sensor. Modifications will be proposed that will increase the robustness and performance of the soft sensor. The reliability of the estimate will be shown using the Bauer–Premaratne–Durán Theorem. Furthermore, the proposed soft sensor system will be tested using simulations of a continuous stirred tank reactor (CSTR) and an reverse osmosis plant. Simulation showed that the modified soft sensor gives good estimates, whereas the traditional soft sensor gives an unstable estimate for the CSTR and reverse osmosis plant. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An additional layer of protection through superalarms with diagnosis capability(Ecopetrol S.A.ctyf@ecopetrol.com.co, 2020-06-01)An alarm management methodology can be proposed as a discrete event sequence recognition problem where time patterns are used to identify the process safe condition, especially in the start-up and shutdown stages. Industrial plants, particularly in the petrochemical, energy, and chemical sectors, require a combined approach of all the events that can result in a catastrophic accident. This document introduces a new layer of protection (super-alarm) for industrial processes based on a diagnostic stage. Alarms and actions of the standard operating procedure are considered discrete events involved in sequences, where the diagnostic stage corresponds to the recognition of a special situation when these sequences occur. This is meant to provide operators with pertinent information regarding the normal or abnormal situations induced by the flow of alarms. Chronicles Based Alarm Management (CBAM) is the methodology used to build the chronicles that will permit to generate the super-alarms furthermore, a case study of the petrochemical sector using CBAM is presented to build the chronicles of the normal startup, abnormal start-up, and normal shutdown scenarios. Finally, the scenario validation is performed for an abnormal start-up, showing how a super-alarm is generated. - 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, Modulating function based fault diagnosis using the parity space method(Elsevier, 2021-07-01)A model-based method for the detection and estimation of faults in dynamic systems is proposed. The method is based on the combination of the parity space approach and the modulating function framework for estimation. The parity space method is employed as an efficient geometric procedure determining null subspaces for annihilating unknown terms and formulating residuals. With the modulating functions technique the dynamic relation from output differentiation is reformulated as an algebraic expression. This substantially reduces the noise sensitivity of the output derivatives required. The design allows for the robust fault detection and isolation also for some nonlinear systems. The robustness of the approach is demonstrated on a nonlinear model of a four-tank process. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Highly maneuverable target tracking under glint noise via uniform robust exact filtering differentiator with intrapulse median filter(Institute of Electrical and Electronics Engineers Inc., 2021-12-28)Highly maneuverable target tracking under glint noise and nonlinear uncertainties during course changes and terminal maneuvers has been solved suboptimally for many years via the interacting multiple model algorithm with the use of the Kalman filter (KF), unscented Kalman filter, and the extended Kalman filter (EKF). Also, nonlinear KFs such as the cubature Kalman filter have been proposed to improve nonlinear tracking without being able to filter out glint noise. To this end, the particle filter and some KF based on variational Bayesian approach have been proposed with very good results in filtering out glint noise. Nonetheless, it is difficult for state-of-the-art methods to achieve efficient filtering of glint noise and nonlinear tracking at the same time. On the other hand, robust exact differentiators, based on the super-twisting algorithm, have been used for many years in output-feedback control and state observation in order to obtain the derivatives of an input signal with theoretical finite-time exactness. However, their potential for target tracking applications has not been explored sufficiently. In this article, a uniform robust exact filtering differentiator with intrapulse median filtering is proposed to filter out glint noise at the sliding manifold, while offering nonlinear tracking robustness via high-degree super-twisting terms outside the sliding manifold. Numerical simulations comparing the proposed solution to other state-of-the-art methods were conducted, showing promising results.
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