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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, 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, 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, Flotation Process Fault Detection and Isolation Using Neural ODE for Generation of Vector-Field Features(Elsevier B.V., 2023-07-01)Flotation in the mining industry is of vital importance for obtaining the right quality of product with efficiency and represents a critical process where possible failures must be monitored at all times. In this paper, complete fault detection and isolation system (FDI) based on the Neural Ordinary Differential Equations (NODE) framework is proposed; the NODE is employed to represent the dynamics of the studied plant based on the measured variables and inputs. Then, a classifier can be used to identify the faults based on the projections of the derivatives or local vector field generated by the NODE using the estimations and actual measurements. The proposed approach is applied to a controlled mining flotation process that has perturbations. The solution is compared with other known machine learning techniques showing better performance metrics. Moreover, it is demonstrated with t-SNE representation that features generated from the NODE model improve the classification. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Hybrid controller based on data and physical modeling using Neural ODE networks(Elsevier BV, 2026-06-01)Combining model-based (MB) and data-based (DB) techniques can offer significant benefits in control system design. However, achieving high performance requires a robust and efficient hybridization (MB+DB). A well-designed hybrid controller with these characteristics can be particularly advantageous for systems with only partial physical knowledge. Another motivation for this hybrid approach is the rapid advancement of AI, which enables high efficiency and diverse modeling possibilities. In this paper, we propose an adaptation of neural ODEs to incorporate prior physical dynamics. Specifically, during the forward phase, the system's physical model is propagated in parallel with a neural ODE network, while, in the backward phase, the network's training mechanism is adjusted to account for the prior dynamics; thus, using the ODE solver and the adjoint method naturally fuses the ODE equations of the prior physics and the network. This approach has broad applicability, and, in this article, it is used to design and train both a system identifier and a neuro-controller, demonstrating strong performance in modeling and tracking, when compared with a tuned classical PID controller in the benchmark tasks (see section 5.2), the proposed controller achieves reductions exceeding 75% in both settling time and maximum control effort, meanwhile, the learning process consistently converges from multiple initial conditions within reduced training iterations. These results highlight the effectiveness of the proposed hybrid Neural ODE framework as a general and efficient solution for control problems involving limited physical insight and available data, respectively. The proposed method can be applied to various common control challenges involving partial knowledge of system dynamics and available data. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Lab Pipeline System for Teaching Control and Supervision Systems(Elsevier BV, 2025-01-01)Pipeline systems (PS) are considered the most efficient means of transporting fluids worldwide. These systems transport fluids from extraction areas to processing plants and/or consumption sites, in this context they have reached a high level of importance for the well-being of society. Therefore, it is crucial for society that PS operate properly, however this demand could become difficult to meet due to breakdowns such leakage and component fault. In this way, to ensure smooth transport of fluids, control and supervision systems are implemented along the PS. Last decades, to improve the safety of PS, supervision systems are complemented with fault detection and isolation (FDI) tools. This paper presents a laboratory-scale pipeline system (LPS) for teaching control and supervision systems. Thus, this LPS is suitable for learning modeling from physical and experimental data, control system design and FDI algorithm design, and solution testing. The use of the LPS in teaching is illustrated through a lab session on control system design based on process identification. Likewise, a teaching procedure for the design of a leak detection and localization algorithm followed by its testing in the LPS is shown.1