5,701 research outputs found

    A NEURAL NETWORK BASED APPROACH TO FAULT DETECTION IN INDUSTRIAL PROCESSES

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    The need for automated fault detection methods has increased in line with the complexity of processing plant technology and their control systems. Fast and accurate fault detection and isolation (FDI) is essential if a controller is to be effective in a supervisory role. This thesis is concerned with developing an FDI system based upon artificial neural network techniques. The artificial neural network (ANN) is a mechanism based upon the concepts of information processing within the brain, and consequently has the ability to self adjust, or learn about a given problem domain. It can thus be utilised in currently favoured model-based FDI systems with the advantage that it can learn process dynamics by being presented examples of process input-output pairs without the need for traditional mathematically complex models. Similarly, ANNs can be taught to classify characteristics in the residual (or plant-model difference) signal without the necessity of constructing the types of filter used in more classical solutions. Initially, a class of feedforward neural network called the multilayer perceptron (MLP) is used to model mathematically simulated linear and nonlinear plants in order to demonstrate their abilities in this field, as well as investigating the consequence of parameter variation on model effectiveness and how the model can be utilised in a model-based FDI system. A principle aim of this research is to demonstrate the ability of the system to work online and in real-time on genuine industrial processes, and the plant nominated as a test bed - the Unilever Automated Freezer (UAF) - is introduced. The UAF, being a time-varying system, requires a novel system identification approach which has resulted in a number of cascaded MLPs to model the various stages in the phased startup of the process. In order to reduce model mismatch to a minimum, it was necessary to develop an effective switching mechanism between one MLP in the cascade and the next. Attempts using a rule-based switching mechanism, a simple MLP switch and an error based switching mechanism were made, before a solution incorporating a genetic algorithm and an MLP network was developed which had the capability of learning the optimum switching points. After the successful development of the model, a series of MLPs were trained to recognise the characteristics of a number of faults within the residual signals. Problems involving false alarms between certain faults were reduced by the introduction of templates - or information pertaining to when a particular fault was most evident in the residuals. The final solution consisting of an MLP Cascade model and fault isolation MLPs is essentially generic for this class of time-varying system, and the results achieved on the UAF were far superior to those of the currently used FDI system without the need for any extra sensory information. The MLP Cascade and associated switching device together with the development of an online real-time FDI system for a time-varying piece of industrial machinery, are deemed to be original contributions to knowledge.Unilever Research Colworth Laborator

    Meta-heuristic algorithms in car engine design: a literature survey

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    Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

    Model based fault diagnosis and prognosis of nonlinear systems

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    Rapid technological advances have led to more and more complex industrial systems with significantly higher risk of failures. Therefore, in this dissertation, a model-based fault diagnosis and prognosis framework has been developed for fast and reliable detection of faults and prediction of failures in nonlinear systems. In the first paper, a unified model-based fault diagnosis scheme capable of detecting both additive system faults and multiplicative actuator faults, as well as approximating the fault dynamics, performing fault type determination and time-to-failure determination, is designed. Stability of the observer and online approximator is guaranteed via an adaptive update law. Since outliers can degrade the performance of fault diagnostics, the second paper introduces an online neural network (NN) based outlier identification and removal scheme which is then combined with a fault detection scheme to enhance its performance. Outliers are detected based on the estimation error and a novel tuning law prevents the NN weights from being affected by outliers. In the third paper, in contrast to papers I and II, fault diagnosis of large-scale interconnected systems is investigated. A decentralized fault prognosis scheme is developed for such systems by using a network of local fault detectors (LFD) where each LFD only requires the local measurements. The online approximators in each LFD learn the unknown interconnection functions and the fault dynamics. Derivation of robust detection thresholds and detectability conditions are also included. The fourth paper extends the decentralized fault detection from paper III and develops an accommodation scheme for nonlinear continuous-time systems. By using both detection and accommodation online approximators, the control inputs are adjusted in order to minimize the fault effects. Finally in the fifth paper, the model-based fault diagnosis of distributed parameter systems (DPS) with parabolic PDE representation in continuous-time is discussed where a PDE-based observer is designed to perform fault detection as well as estimating the unavailable system states. An adaptive online approximator is incorporated in the observer to identify unknown fault parameters. Adaptive update law guarantees the convergence of estimations and allows determination of remaining useful life --Abstract, page iv

    A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning

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    Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network in which neurons are randomly connected. Once initialized, the connection strengths remain unchanged. Such a simple structure turns RC into a non-linear dynamical system that maps low-dimensional inputs into a high-dimensional space. The model's rich dynamics, linear separability, and memory capacity then enable a simple linear readout to generate adequate responses for various applications. RC spans areas far beyond machine learning, since it has been shown that the complex dynamics can be realized in various physical hardware implementations and biological devices. This yields greater flexibility and shorter computation time. Moreover, the neuronal responses triggered by the model's dynamics shed light on understanding brain mechanisms that also exploit similar dynamical processes. While the literature on RC is vast and fragmented, here we conduct a unified review of RC's recent developments from machine learning to physics, biology, and neuroscience. We first review the early RC models, and then survey the state-of-the-art models and their applications. We further introduce studies on modeling the brain's mechanisms by RC. Finally, we offer new perspectives on RC development, including reservoir design, coding frameworks unification, physical RC implementations, and interaction between RC, cognitive neuroscience and evolution.Comment: 51 pages, 19 figures, IEEE Acces

    Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward

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    Advancements in digital automation for smart grids have led to the installation of measurement devices like phasor measurement units (PMUs), micro-PMUs (μ\mu-PMUs), and smart meters. However, a large amount of data collected by these devices brings several challenges as control room operators need to use this data with models to make confident decisions for reliable and resilient operation of the cyber-power systems. Machine-learning (ML) based tools can provide a reliable interpretation of the deluge of data obtained from the field. For the decision-makers to ensure reliable network operation under all operating conditions, these tools need to identify solutions that are feasible and satisfy the system constraints, while being efficient, trustworthy, and interpretable. This resulted in the increasing popularity of physics-informed machine learning (PIML) approaches, as these methods overcome challenges that model-based or data-driven ML methods face in silos. This work aims at the following: a) review existing strategies and techniques for incorporating underlying physical principles of the power grid into different types of ML approaches (supervised/semi-supervised learning, unsupervised learning, and reinforcement learning (RL)); b) explore the existing works on PIML methods for anomaly detection, classification, localization, and mitigation in power transmission and distribution systems, c) discuss improvements in existing methods through consideration of potential challenges while also addressing the limitations to make them suitable for real-world applications

    Deep Learning-Based, Passive Fault Tolerant Control Facilitated by a Taxonomy of Cyber-Attack Effects

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    In the interest of improving the resilience of cyber-physical control systems to better operate in the presence of various cyber-attacks and/or faults, this dissertation presents a novel controller design based on deep-learning networks. This research lays out a controller design that does not rely on fault or cyber-attack detection. Being passive, the controller’s routine operating process is to take in data from the various components of the physical system, holistically assess the state of the physical system using deep-learning networks and decide the subsequent round of commands from the controller. This use of deep-learning methods in passive fault tolerant control (FTC) is unique in the research literature. The proposed controller is applied to both linear and nonlinear systems. Additionally, the application and testing are accomplished with both actuators and sensors being affected by attacks and /or faults
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