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Optical flow-based vertical angular rate fault detection on UAVs 12

Optical flow-based vertical angular rate fault detection on ...

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12th ifac symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)

作者： Jevuczo, Gabor Bauer, Peter HUN REN Syst & Control Lab Inst Comp Sci & Control HUN REN SZTAKI Budapest Hungary

This paper discusses optical flow-based vertical angular rate fault detection based-on real flight IMU data and camera image sequences. The flight test platform utilizes a gimbal stabilized downward looking camera that is why only the angular rate around the inertial vertical axis can be estimated. Improvements relative to the previous work of the authors are the application of forward-backward optical flow to filter outliers, the direct detection of angular rate faults without the need for Euler angles or GPS velocities and tuning and verification on real images and flight data. After presenting the optical flow equations and the method for image-based vertical angular rate estimation the flight test scenarios and video processing steps are introduced. Four different artificial errors are added to the IMU measurements and the fault detection applies residual thresholding and up-down counters. Tuning is based on false alarm and missed detection rates and the application of receiver operating characteristics. Finally, the estimation results and possible further improvements are presented. Copyright (c) 2024 The Authors.

关键词： angular rate fault detection real flight video optical flow UAV

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Nodal Hydraulic Head Estimation through Unscented Kalman Filter for Data-driven Leak Localization in Water Networks 12

Nodal Hydraulic Head Estimation through Unscented Kalman Fil...

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12th ifac symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)

作者： Romero-Ben, Luis Irofti, Paul Stoican, Florin Puig, Vicenc UPC CSIC Inst Robot & Informat Ind Llorens & Artigas 4-6 Barcelona 08028 Spain Univ Bucharest LOS CS FMI Bucharest Romania Univ Politehn Bucuresti Dept Automat Control & Syst Engn Bucharest Romania UPC Supervis Safety & Automat Control Res Ctr CS2AC Campus Terrassa Barcelona 08222 Spain

In this paper, we present a nodal hydraulic head estimation methodology for water distribution networks (WDN) based on an Unscented Kalman Filter (UKF) scheme with application to leak localization. The UKF refines an initial estimation of the hydraulic state by considering the prediction model, as well as available pressure and demand measurements. To this end, it provides customized prediction and data assimilation steps. Additionally, the method is enhanced by dynamically updating the prediction function weight matrices. Performance testing on the Modena benchmark under realistic conditions demonstrates the method's effectiveness in enhancing state estimation and data-driven leak localization. Copyright (c) 2024 The Authors.

关键词： Fault isolation water distribution system data fusion state estimation Unscented Kalman Filter

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modelling smart machining process towards intelligent manufacturing a case study 18

Modelling smart machining process towards intelligent manufa...

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18th ifac symposium on Information control Problems in Manufacturing (INCOM)

作者： Musalekar, Dineshkumar Patalas-Maliszewska, Justyna Lumel Alucast Sp Zoo Slubicka 1 PL-65127 Zielona Gora Poland Univ Zielona Gora Inst Mech Engn Licealna St 9 PL-65417 Zielona Gora Poland

According to the concept of Industry 4.0 (even Industry 5.0) managers strive to improve production by applying information technologies and next integrating them into operational technologies, that's enabled to transform the traditional into Intelligent Manufacturing (IM). Introduction of these changes is particularly important to improve the machining process, which is complex and dynamic In this article the smart machining process is modelled and demonstrated for a real-world case, namely manufacturing of the die casting mould in order to increase life of the die casting mould. Therefore, firstly, the data was acquired based on the applied within a company information system and additionally on the IoT sensors from the diecasting machines, the moulds themselves and the process parameters which are used during the manufacturing process of the diecasting parts from such mould. Next, a model for predicting the life of the die-casting mould using regression analysis, based on acquired data was applied. The results of mould design and mould manufacturing process are ultimately visible on the die casting machines. Such gathered data is used to model the prediction of the possibilities for increasing life of the die casting was elaborated. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： smart machining process modelling intelligent manufacturing diecasting mould manufacturing diecasting mould Design

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Dynamics Modeling of Robot Joints with Asymmetric Load-Dependent Friction 20

Dynamics Modeling of Robot Joints with Asymmetric Load-Depen...

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20th ifac symposium on System Identification (SYSID)

作者： Graabaek, Soren Poulsen, Niels K. Sloth, Christoffer Univ Southern Denmark Maersk McKinney Moller Inst Copenhagen Denmark Universal Robots AS Copenhagen Denmark Tech Univ Denmark Dept Appl Math & Comp Sci Lyngby Denmark

This paper presents a strain wave gear model intended for industrial robots that are operated in both forward drive and backward drive under different load conditions. We show that a simple joint model captures the joint dynamics well at one load, but fails to capture the joint dynamics when the load is changed. Similarly, the simple model cannot capture the efficiency of the joint in both forward drive and backward drive. We propose a tooth model with sliding friction to resolve these issues. This model is simple to simulate and can thus be used for real-time control;the proposed model captures the load dependent friction and difference in efficiency in backward drive and forward drive. We validate the simulation performance of the tooth models on data from a Universal Robots UR10e robot with different payloads which shows an improvement compared to the standard rigid joint model. Copyright (c) 2024 The Authors.

关键词： Grey Box modelling Nonlinear System Identification Identification for control

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Fault Diagnosis using Interval Data-driven LPV Observers and Structural Analysis 12

Fault Diagnosis using Interval Data-driven LPV Observers and...

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12th ifac symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)

作者： Fang, Xin Blesa, Joaquim Puig, Vicenc UPC CSIC Inst Robot & Informat Ind Llorens & Artigas 4-6 Barcelona 08028 Spain Univ Politecn Cataluna Supervis Safety & Automat Control Res Ctr CS2AC Rambla St Nebridi 22 Terrassa 08222 Spain UPC Automat Control Dept ESAII Eduard Maristany 16 Barcelona 08019 Spain

This paper presents a Fault Detection and Isolation (FDI) method that combines Structural Analysis (SA) and machine learning data-driven techniques. The adaptive network fuzzy inference system (ANFIS) is used to obtain a model of the real system to be monitored using historical data from non-faulty scenarios. The structure of the model is given by the SA by means of graphical or textual description of the system. The obtained model is formulated as an observer where unknown but bounded process and sensor noises are considered. Then, LPV observers are used to carry out the fault diagnosis using model residuals. Once observers detect a residual inconsistency, a fault alarm is raised and the fault isolation triggered. Fault isolation is carried out through the transformation of residuals using the Kramer function and the Dempster-Shafer theory. The proposed fault isolation method considers the plausibility of the activation of each residual and the SA information to provide the most probable fault. Finally, a well-known four-tanks system illustrates the performance and results of the proposed method. Copyright (c) 2024 The Authors.

关键词： Fuzzy inference

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A novel dynamical model for diagnosis, prognosis and health-aware control of Lithium-ion batteries 12

A novel dynamical model for diagnosis, prognosis and health-...

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12th ifac symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)

作者： Martinez, John J. Felix, Monica S. Kulkarni, Chetan Orchard, Marcos Berenguer, Christophe Univ Grenoble Alpes CNRS Grenoble INP GIPSA Lab F-38000 Grenoble France KBR Inc NASA Ames Res Ctr Moffett Field CA 94043 USA Univ Chile CASE Santiago Chile

In this work it is proposed a novel dynamical model of the Lithium-ion batteries intended for diagnosis, prognosis and health-aware control. The proposed model is a particular version of an equivalent electrical circuit and a thermal model. The modeling problem considers that a new battery can be considered as a reference battery with nominal behavior, and the deteriorated one has the same mathematical structure with a multiplicative degradation parameter, considered here to be an input of the system. The battery degradation is considered to simultaneously affect both the capacity of the battery and the value of the internal resistance. The proposed approach considers that just one degradation parameter can absorbs all possible sources of degradation. Experimental data is used to illustrate the proposed battery model and its corresponding degradation parameter estimation. The latter problem is solved by finding the system input (i.e. the degradation parameter) as the solution of a feedback control problem which minimizes the prediction error concerning the terminal voltage. The results also include obtained estimations on temperatures and State of Charge for different battery aging scenarios. Here, we illustrate how a more accurate model of both: the open-circuit voltage and the internal resistance can be used for improving the estimation of the battery aging. Copyright (c) 2024 The Authors.

关键词： Health monitoring and diagnosis modeling and simulation of batteries Robust estimation energy storage systems Remaining Useful lifetime prediction health-aware control

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Safe vehicle motion design with learning for moving in environment with uncertainties 12

Safe vehicle motion design with learning for moving in envir...

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12th ifac symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)

作者： Tompos, Denes Nemeth, Balazs Hegedus, Tamas Vu Van Tan Gaspar, Peter Hungarian Res Network HUN REN Inst Comp Sci & Control SZTAKI Kende U 13-17 H-1111 Budapest Hungary Budapest Univ Technol & Econ Dept Control Transportat & Vehicle Syst Stoczek U 2 H-1111 Budapest Hungary Univ Transport & Commun Dept Automot Mech Engn Fac Mech Engn 3 Cau Giay St Hanoi 100000 Vietnam

In this paper a motion profile design for unmanned aerial vehicles is proposed which method is able to guarantee safe collision-free motion. The motivations of the work are provided by the uncertainties of covered areas by the vehicles, and also the need of high performance fast vehicle motion. The uncertain information on the environment for detecting conflict areas is processed through clustering and Mahalanobis-distance-based filtering methods. The resulted conflict areas are involved in the motion design method, which is facilitated through reinforcement learning. This paper shows the application of the method on a drone that moves together with a mobile robot in the same environment. The safe and high performance motion of the drone is illustrated through simulation example. Copyright (c) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Unmanned aerial vehicles safe motion clustering point clouds

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SINDy vs Hard Nonlinearities and Hidden Dynamics: a Benchmarking Study 20

SINDy vs Hard Nonlinearities and Hidden Dynamics: a Benchmar...

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20th ifac symposium on System Identification (SYSID)

作者： Ugolini, Aurelio Raffa Breschi, Valentina Manzoni, Andrea Tanelli, Mara Politecn Milan Dip Elettron Informaz & Bioingn Via G Ponzio 34-5 I-20133 Milan Italy Eindhoven Univ Technol Dept Elect Engn NL-5600 MB Eindhoven Netherlands Politecn Milan Dip Matemat Pzza Leonardo da Vinci 32 I-20133 Milan Italy

In this work, we analyze the effectiveness of the Sparse Identification of Nonlinear Dynamics (SINDy) technique on three benchmark datasets for nonlinear identification, to provide a better understanding of its suitability as a modeling tool for real dynamical systems. While SINDy is often portrayed as an appealing strategy for pursuing physics-based learning, our analysis highlights two weaknesses, i.e., the difficulties in applying this technique when dealing with unobserved states and non-smooth dynamics. Due to the ubiquity of these features in real systems in general, and control applications in particular, we complement our analysis with hands-on approaches to tackle these issues. Copyright (c) 2024 The Authors.

关键词： Grey box modelling Nonlinear system identification Machine learning

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Sustainable supply chain control strategies for the natural stone market 12

Sustainable supply chain control strategies for the natural ...

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12th ifac symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)

作者： Azevedo, Elen Carpitella, Silvia Calif State Univ Northridge Dept Mfg Syst Engn & Management 18111 Nordhoff St Northridge CA 91330 USA

The natural stone market is an important component of the modern economy, with increasing demand driven by a growing population and urbanization. However, the industry faces a range of challenges related to supply chain risk management, including sourcing raw materials, transportation, and quality control. Effective risk management is crucial for the success of any business in this sector, as it helps to minimize losses, maintain quality, and ensure timely delivery of products to customers. This paper proposes the use of a flexible decision-making approach to classify major risks and identify effective mitigation strategies for order management in the natural stone market. The proposed method considers multiple criteria, including financial, operational, and mitigation factors, to develop a comprehensive risk assessment framework that can be used to optimize control processes. By implementing the proposed framework, stakeholders in the natural stone market can improve their risk management capabilities and enhance their competitive advantage in the global market. The framework can help to identify the most critical risks and prioritize mitigation strategies, resulting in a more efficient and effective supply chain management system. This paper presents a real case study of a Brazilian company, which illustrates the practical application of the proposed methodological framework in the natural stone market, highlighting its effectiveness in identifying and mitigating risks related to stone slabs transportation network. The insights gained from this case study have potential for application across various industries facing analogous challenges in managing risks within their supply chains. Copyright (c) 2024 The Authors.

关键词： Safety and Security Supply Chain Risk Management Multi-Criteria Decision-Making Network systems Process Engineering

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Neural Data-Enabled Predictive control 20

Neural Data-Enabled Predictive Control

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20th ifac symposium on System Identification (SYSID)

作者： Lazar, Mircea Eindhoven Univ Technol Eindhoven Netherlands

Data-enabled predictive control (DeePC) for linear systems utilizes data matrices of recorded trajectories to directly predict new system trajectories, which is very appealing for real-life applications. In this paper we leverage the universal approximation properties of neural networks (NNs) to develop neural DeePC algorithms for nonlinear systems. Firstly, we point out that the outputs of the last hidden layer of a deep NN implicitly construct a basis in a so-called neural (feature) space, while the output linear layer performs affine interpolation in the neural space. As such, we can train of-line a deep NN using large data sets of trajectories to learn the neural basis and compute on-line a suitable affine interpolation using DeePC. Secondly, methods for guaranteeing consistency of neural DeePC and for reducing computational complexity are developed. Several neural DeePC formulations are illustrated on a nonlinear pendulum example. Copyright (c) 2024 The Authors.

关键词： Predictive control Data-driven control Nonlinear systems Neural networks

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