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Simultaneous Fault Detection Of Heat Exchangers

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IFAC Proceedings Volumes 1998年第10期31卷 365-370页

作者： Gcibor Szederkényi Erik Weyer Katalin M. Hangos Dept. of Computer Science University of Veszprem H-8201 Veszprem Egyetem u. 3. Hungary Dept. of Electrical and Electronic Engineering The University of Melbourne Parkville VIC 3052 Australia Systems and Control Research Laboratory Computer and Automation Institute HAS H-1518 Budapest PO Box 63 Hungary

A grey box model-based method for fault diagnosis is proposed in this paper. The method is based on a first principle model of the process unit: a heat exchanger, and on a grey box model of the faults: the deterioration of the heat transfer surface by aging and the leaking of the outer container. The deterioration of the heat transfer surface is due to material settling, and in old heat exchangers pieces of this material can break off and cause damage in the equipment. A recursive least squares estimator with forgetting factor is used to track both the heat transfer coefficients and the cold side volume. The heat transfer coefficients are estimated both from the hot and cold side equations. The settled material breakage fault is detected via detection of abrupt positive jumps in the estimated heat transfer coefficients using a detector based on a cumulative sum (CUSUM) test. The proposed method enables simultaneous detection of the two fault types considered. Fault localization along the equipment length is also possible when temperature measurements are available along the length.

关键词： Fault detection Fault isolation Fault location Process model Grey box modelling Identification

A Two-Step Fault Diagnosis Framework for Rolling Element Bearings with Imbalanced Data

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A Two-Step Fault Diagnosis Framework for Rolling Element Bea...

International Conference on Ubiquitous Robots and Ambient Intelligence

作者： Yuan Lan Weiwei Zong Xiaojian Ding Xiaoyan Xiong Xiaohang Han Jiahai Huang Bing Ma Key Laboratory of Ministry of Education in Advance Transducers and Intelligent Control System Institute of Mechatronics Engineering under School of Mechanical Engineering Taiyuan University of Technology Department of Computer Science Wayne State University Science and Technology on Information Systems Engineering Laboratory The Second Research Institute of China Electronic Technology Group Corporation

ISBN: (纸本)9781509008223

Rolling element bearings constitute the key parts on rotating machinery and their fault diagnosis are of great importance. In this paper, a novel Two-Step fault diagnosis framework is proposed to diagnose the status of rolling element bearings with imbalanced data. The Wavelet Packet Transform (WPT) is used to determine the feature vectors. 16-dimensional wavelet packet node energies were extracted from the original datasets as the feature vectors prepared to input to the classifiers. Next, our proposed framework consists of two steps for the fault diagnosis, where Step One makes use of Weighted Extreme Learning Machine (weighted ELM) in an effort to classify the normal or abnormal categories, and Step Two further diagnoses the underlying anomaly in details. The effectiveness of our proposed approach is testified on the raw data collected from the rolling element bearing experiments conducted in our institute, and the empirical results showed that our approach is really fast and can achieve the diagnosis accuracies more than 95%.

关键词： Two-Step fault diagnosis framework Weighed ELM ELM Bearing fault diagnosis

Bayesian Neural Networks Uncertainty Quantification with Cubature Rules

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Bayesian Neural Networks Uncertainty Quantification with Cub...

International Joint Conference on Neural Networks (IJCNN)

作者： Peng Wang Nidhal C. Bouaynaya Lyudmila Mihaylova Jikai Wang Qibin Zhang Renke He Automatic Control & Systems Engineering Dept. The University of Sheffield Information and Navigation College Air Force Engineering University The 32nd Research Institute of CETC Department of Automation University of Science and Technology of China Department of Electrical and Computer Engineering Rowan University

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Bayesian neural networks are powerful inference methods by accounting for randomness in the data and the network model. Uncertainty quantification at the output of neural networks is critical, especially for applications such as autonomous driving and hazardous weather forecasting. However, approaches for theoretical analysis of Bayesian neural networks remain limited. This paper makes a step forward towards mathematical quantification of uncertainty in neural network models and proposes a cubature-rule-based computationally-efficient uncertainty quantification approach that captures layer-wise uncertainties of Bayesian neural networks. The proposed approach approximates the first two moments of the posterior distribution of the parameters by propagating cubature points across the network nonlinearities. Simulation results show that the proposed approach can achieve more diverse layer-wise uncertainty quantification results of neural networks with a fast convergence rate.

关键词： Neural networks Uncertainty Bayes methods Computational modeling Gaussian distribution Two dimensional displays Weather forecasting

Event-triggered control for consensus of multi-agent systems with nonlinear output and directed topologies

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arXiv 2019年

作者： Yi, Xinlei Zhang, Shengjun Yang, Tao Wu, Junfeng Johansson, Karl H. School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Department of Electrical Engineering University of North Texas DentonTX76203 United States College of Control Science and Engineering Zhejiang University Hangzhou310027 China

We propose a distributed event-triggered control law to solve the consensus problem for multi-agent systems with nonlinear output. Under the condition that the underlying digraph is strongly connected, we propose some sufficient conditions related to the nonlinear output function and initial states to guarantee that the event-triggered controller realizes consensus. Then the results are extended to the case where the underlying directed graph contains a directed spanning tree. These theoretical results are illustrated by numerical simulations. Copyright © 2019, The Authors. All rights reserved.

关键词： Multi agent systems

Friction feedforward compensation and position feedforward in CNC machines

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Friction feedforward compensation and position feedforward i...

7th Annual International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2010

作者： Suteeratanapirom, Wijin Rattanawaorahirankul, Rapeepong Laosapan, Chutima Sanposh, Peerayot Chayopitak, Nattapon Tungpimolrut, Kanokvate Kunieda, Hiroaki TAIST Tokyo Tech ICTES Program Department of Electrical Engineering Kasetsart University 50 Phahonyothin Rd. Ladyao Jatujak Bangkok 10903 Thailand Intelligent Mechatronics Automation Robotics and Control Laboratory Department of Electrical Engineering Kasetsart University 50 Phahonyothin Rd. Ladyao Jatujak Bangkok 10903 Thailand Industrial Control and Automation Laboratory National Electronics and Computer Technology Center 112 Thailand Science Park Phahonyothin Rd. Klong 1 Klong Luang Pathumthani 12120 Thailand Department of Electrical and Electronic Engineering Tokyo Institute of Technology S3-65 2-12-1 Ookayama Meguro-ku Tokyo 152-8550 Japan

ISBN: (纸本)9789746724913

This research proposes a method to eliminate friction effect and reduced tracking error on CNC milling machines. A CNC milling machine has many connected mechanical components which have friction effects such as ball screws and rails. To control servo motors which are used to drive X-Y table in CNC milling machine, the conventional PID controller is widely used. In this research, Panasonic motor driver is set to the torque control mode. To compensate the friction effects, the friction feedforward compensation method is proposed. Moreover, to reduce the position tracking error, position feedforward controller is used. The simulation and implementation of position control in one axis of PID with Friction Feedforward Compensation and Position Feedforward controller (FFC-PFC) are presented.

关键词： Friction

Erratum to “An adaptive controller for nonlinear teleoperators” [Automatica 46 (2010) 155–159]

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Automatica 2011年第5期47卷 1093-1094页

作者： Emmanuel Nuño Romeo Ortega Luis Basañez Department of Computer Science CUCEI University of Guadalajara Guadalajara Mexico Laboratoire des Signaux et Systèmes SUPÉLEC Gif-sur-Yvette France Institute of Industrial and Control Engineering Technical University of Catalonia Barcelona Spain

Distributed Optimization via Gradient Descent with Event-Triggered Zooming over Quantized Communication

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arXiv 2023年

作者： Rikos, Apostolos I. Jiang, Wei Charalambous, Themistoklis Johansson, Karl H. The Department of Electrical and Computer Engineering Division of Systems Engineering Boston University BostonMA02215 United States Hong Kong The Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia1678 Cyprus The Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland The Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden

In this paper, we study unconstrained distributed optimization strongly convex problems, in which the exchange of information in the network is captured by a directed graph topology over digital channels that have limited capacity (and hence information should be quantized). Distributed methods in which nodes use quantized communication yield a solution at the proximity of the optimal solution, hence reaching an error floor that depends on the quantization level used;the finer the quantization the lower the error floor. However, it is not possible to determine in advance the optimal quantization level that ensures specific performance guarantees (such as achieving an error floor below a predefined threshold). Choosing a very small quantization level that would guarantee the desired performance, requires information packets of very large size, which is not desirable (could increase the probability of packet losses, increase delays, etc) and often not feasible due to the limited capacity of the channels available. In order to obtain a communication-efficient distributed solution and a sufficiently close proximity to the optimal solution, we propose a quantized distributed optimization algorithm that converges in a finite number of steps and is able to adjust the quantization level accordingly. The proposed solution uses a finite-time distributed optimization protocol to find a solution to the problem for a given quantization level in a finite number of steps and keeps refining the quantization level until the difference in the solution between two successive solutions with different quantization levels is below a certain pre-specified threshold. Therefore, the proposed algorithm progressively refines the quantization level, thus eventually achieving low error floor with a reduced communication burden. The performance gains of the proposed algorithm are demonstrated via illustrative examples. © 2023, CC BY.

关键词： Directed graphs

Optimal CPU scheduling in data centers via a finite-time distributed quantized coordination mechanism

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arXiv 2021年

作者： Rikos, Apostolos I. Grammenos, Andreas Kalyvianaki, Evangelia Hadjicostis, Christoforos N. Charalambous, Themistoklis Johansson, Karl H. Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Alan Turing Institute London United Kingdom Department of Electrical and Computer Engineering University of Cyprus Nicosia1678 Cyprus Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo02150 Finland

In this paper we analyze the problem of optimal task scheduling for data centers. Given the available resources and tasks, we propose a fast distributed iterative algorithm which operates over a large scale network of nodes and allows each of the interconnected nodes to reach agreement to an optimal solution in a finite number of time steps. More specifically, the algorithm (i) is guaranteed to converge to the exact optimal scheduling plan in a finite number of time steps and, (ii) once the goal of task scheduling is achieved, it exhibits distributed stopping capabilities (i.e., it allows the nodes to distributely determine whether they can terminate the operation of the algorithm). Furthermore, the proposed algorithm operates exclusively with quantized values (i.e., the information stored, processed and exchanged between neighboring agents is subject to deterministic uniform quantization) and relies on event-driven updates (e.g., to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage). We also provide examples to illustrate the operation, performance, and potential advantages of the proposed algorithm. Finally, by using extensive empirical evaluations through simulations we show that the proposed algorithm exhibits state-of-the-art performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Multitasking

Risk-Averse Learning with Non-Stationary Distributions

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arXiv 2024年

作者： Wang, Siyi Wang, Zifan Yi, Xinlei Zavlanos, Michael M. Johansson, Karl H. Hirche, Sandra Department of Electrical and Computer Engineering Technical University of Munich Munich80333 Germany Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden Lab for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Mechanical Engineering and Material Science Duke University DurhamNC27708 United States

Considering non-stationary environments in online optimization enables decision-maker to effectively adapt to changes and improve its performance over time. In such cases, it is favorable to adopt a strategy that minimizes the negative impact of change to avoid potentially risky situations. In this paper, we investigate risk-averse online optimization where the distribution of the random cost changes over time. We minimize risk-averse objective function using the Conditional Value at Risk (CVaR) as risk measure. Due to the difficulty in obtaining the exact CVaR gradient, we employ a zeroth-order optimization approach that queries the cost function values multiple times at each iteration and estimates the CVaR gradient using the sampled values. To facilitate the regret analysis, we use a variation metric based on Wasserstein distance to capture time-varying distributions. Given that the distribution variation is sub-linear in the total number of episodes, we show that our designed learning algorithm achieves sub-linear dynamic regret with high probability for both convex and strongly convex functions. Moreover, theoretical results suggest that increasing the number of samples leads to a reduction in the dynamic regret bounds until the sampling number reaches a specific limit. Finally, we provide numerical experiments of dynamic pricing in a parking lot to illustrate the efficacy of the designed algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Learning algorithms