35 research outputs found

    Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

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    In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM

    Transformada wavelet para análisis del motor de inducción: revisión

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    This study makes a revision of the most recent investigations that have implemented the wavelet transform by analyzing the electrical and mechanical variables of the induction motors. The investigations can be grouped into three main topics: diagnosis and detection of faults, control and detection systems and the classification of electromagnetic disturbances.Este trabajo realiza una revisión de las investigaciones más recientes que han implementado la transformada wavelet analizando las variables eléctricas y mecánicas de los motores de inducción. Las investigaciones se pueden agrupar en tres temas principales: diagnóstico y detección de fallas; sistemas de control y detección y la clasificación de perturbaciones electromagnéticas

    PHM survey: implementation of signal processing methods for monitoring bearings and gearboxes

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    The reliability and safety of industrial equipments are one of the main objectives of companies to remain competitive in sectors that are more and more exigent in terms of cost and security. Thus, an unexpected shutdown can lead to physical injury as well as economic consequences. This paper aims to show the emergence of the Prognostics and Health Management (PHM) concept in the industry and to describe how it comes to complement the different maintenance strategies. It describes the benefits to be expected by the implementation of signal processing, diagnostic and prognostic methods in health-monitoring. More specifically, this paper provides a state of the art of existing signal processing techniques that can be used in the PHM strategy. This paper allows showing the diversity of possible techniques and choosing among them the one that will define a framework for industrials to monitor sensitive components like bearings and gearboxes

    Physics-Based and Data-Driven Analytics for Enhanced Planning and Operations in Power Systems with Deep Renewable Penetration

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    This dissertation is motivated by the lack of combined physics-based and data-driven framework for solving power system challenges that are introduced by the integration of new devices and new system components. As increasing number of stochastic generation, responsive loads, and dynamic measurements are involved in the planning and operations of modern power systems, utilities and system operators are in great need of new analysis framework that could combine physical models and measuring data together for solving challenging planning and operational problems. In view of the above challenges, the high-level objective of this dissertation is to develop a framework for integrating measurement data into large physical systems modeled by dynamical equations. To this end, the dissertation first identifies four critical tasks for the planning and operations of the modern power systems: the data collection and pre-processing, the system situational awareness, the decision making process, as well as the post-event analysis. The dissertation then takes one concrete application in each of these critical tasks as the example, and proposes the physics-based/data-driven approach for solving the challenging problems faced by this specific application. To this end, this dissertation focuses on solving the following specific problems using physics-based/data-driven approaches. First, for the data collection and pre-processing platform, a purely data-driven approach is proposed to detect bad metering data in the phasor measurement unit (PMU) monitoring systems, and ensure the overall PMU data quality. Second, for the situational awareness platform, a physics-based voltage stability assessment method is presented to improve the situational awareness of system voltage instabilities. Third, for the decision making platform, a combined physics-based and data-driven framework is proposed to support the decision making process of PMU-based power plant model validation. Forth, for the post-event analysis platform, a physics-based post-event analysis is presented to identify the root causes of the sub-synchronous oscillations induced by the wind farm integration. The above problems and proposed solutions are discussed in detail in Section 2 through Section 5. The results of this work can be integrated to address practical problems in modern power system planning and operations

    Condition Monitoring and Fault Diagnosis of Fluid Machines in Process Industries

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    Condition Monitoring (CM) of fluid machines plays a critical role in maintaining efficient productivity in many processing industries. Conventional vibration techniques generally provide more localised information with the need for many sensors, associated data acquiring and processing efforts, which are difficult for system deployment and are reluctantly accepted by those industries, for example paper mills and food production lines making marginal profits. To find adequate CM techniques for such industries this research investigates a new cost- effective scheme of implementing CM, which combines the high diagnostic capability of using Surface Vibration (SV) with the global detection capability of using the Instantaneous Angular Speed (IAS) measurements and Airborne Sound (AS). To address specific techniques involved in the scheme, this research is arranged in three consecutive Phases: Phase I is the technical evaluation; Phase II is the field implementation practices and Phase III is the application of AS through Convolution Neural Networks (CNN). In Phase I, widely used reciprocating compressor is investigated numerically and experimentally, which clarifies the performances of SV, IAS, AS, pressure and motor current in a quantitative way for differentiating common faults such as leakages happening in valves and intercoolers, faulty motor drives and mechanical transmission systems. It paves the foundations for the field implementation in Phase II. In Phase II, this novel scheme is realised on three sets of vacuum pumps in a paper mill. Based on an analytic study of dynamic responses to common faults on these pumps, a field test was conducted to verify the feasibility of the scheme and the preliminary study shows that airborne sound can show the relative spectral components for each machine to a good degree of accuracy. Knowledge gained from the preceding phases of study is now applied to Phase III. New techniques based on airborne signal differences through CNN have been demonstrated to give a good indication of the sound propagation and location of noise sources under all operating discharge pressure conditions at 100% validation accuracy, proving that the state of the art deep leaning approaches can be used to deal with complicated acoustic data

    Multilevel Converters: An Enabling Technology for High-Power Applications

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    | Multilevel converters are considered today as the state-of-the-art power-conversion systems for high-power and power-quality demanding applications. This paper presents a tutorial on this technology, covering the operating principle and the different power circuit topologies, modulation methods, technical issues and industry applications. Special attention is given to established technology already found in industry with more in-depth and self-contained information, while recent advances and state-of-the-art contributions are addressed with useful references. This paper serves as an introduction to the subject for the not-familiarized reader, as well as an update or reference for academics and practicing engineers working in the field of industrial and power electronics.Ministerio de Ciencia y Tecnología DPI2001-3089Ministerio de Eduación y Ciencia d TEC2006-0386

    Improved wind turbine monitoring using operational data

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    With wind energy becoming a major source of energy, there is a pressing need to reduce all associated costs to be competitive in a market that might be fully subsidy-free in the near future. Before thousands of wind turbines were installed all over the world, research in e.g. understanding aerodynamics, developing new materials, designing better gearboxes, improving power electronics etc., helped to cut down wind turbine manufacturing costs. It might be assumed, that this would be sufficient to reduce the costs of wind energy as the resource, the wind itself, is free of costs. However, it has become clear that the operation and maintenance of wind turbines contributes significantly to the overall cost of energy. Harsh environmental conditions and the frequently remote locations of the turbines makes maintenance of wind turbines challenging. Just recently, the industry realised that a move from reactive and scheduled maintenance towards preventative or condition-based maintenance will be crucial to further reduce costs. Knowing the condition of the wind turbine is key for any optimisation of operation and maintenance. There are various possibilities to install advanced sensors and monitoring systems developed in recent years. However, these will inevitably incur new costs that need to be worthwhile and retro-fits to existing turbines might not always be feasible. In contrast, this work focuses on ways to use operational data as recorded by the turbine's Supervisory Control And Data Acquisition (SCADA) system, which is installed in all modern wind turbines for operating purposes -- without additional costs. SCADA data usually contain information about the environmental conditions (e.g. wind speed, ambient temperature), the operation of the turbine (power production, rotational speed, pitch angle) and potentially the system's health status (temperatures, vibration). These measurements are commonly recorded in ten-minutely averages and might be seen as indirect and top-level information about the turbine's condition. Firstly, this thesis discusses the use of operational data to monitor the power performance to assess the overall efficiency of wind turbines and to analyse and optimise maintenance. In a sensitivity study, the financial consequences of imperfect maintenance are evaluated based on case study data and compared with environmental effects such as blade icing. It is shown how decision-making of wind farm operators could be supported with detailed `what-if' scenario analyses. Secondly, model-based monitoring of SCADA temperatures is investigated. This approach tries to identify hidden changes in the load-dependent fluctuations of drivetrain temperatures that can potentially reveal increased degradation and possible imminent failure. A detailed comparison of machine learning regression techniques and model configurations is conducted based on data from four wind farms with varying properties. The results indicate that the detailed setup of the model is very important while the selection of the modelling technique might be less relevant than expected. Ways to establish reliable failure detection are discussed and a condition index is developed based on an ensemble of different models and anomaly measures. However, the findings also highlight that better documentation of maintenance is required to further improve data-driven condition monitoring approaches. In the next part, the capabilities of operational data are explored in a study with data from both the SCADA system and a Condition Monitoring System (CMS) based on drivetrain vibrations. Analyses of signal similarity and data clusters reveal signal relationships and potential for synergistic effects of the different data sources. An application of machine learning techniques demonstrates that the alarms of the commercial CMS can be predicted in certain cases with SCADA data alone. Finally, the benefits of having wind turbines in farms are investigated in the context of condition monitoring. Several approaches are developed to improve failure detection based on operational statistics, CMS vibrations or SCADA temperatures. It is demonstrated that utilising comparisons with neighbouring turbines might be beneficial to get earlier and more reliable warnings of imminent failures. This work has been part of the Advanced Wind Energy Systems Operation and Maintenance Expertise (AWESOME) project, a European consortium with companies, universities and research centres in the wind energy sector from Spain, Italy, Germany, Denmark, Norway and UK. Parts of this work were developed in collaboration with other fellows in the project (as marked and explained in footnotes)
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