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Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines with Unlabeled Data

Liang Guo, Yaguo Lei, Saibo Xing, Tao Yan, and Naipeng Li Extended abstract: Intelligent fault diagnosis is able to handle massive monitoring data and distinguish health conditions of machines, which has attracted much attention from both researchers and engineers recently. Generally, the success of intelligent fault diagnosis relies on two conditions: 1) Labeled data are available to provide fault information. 2) The training and testing data are subject to the same probability distribution. However, for some machines, it is difficult to satisfy the above two conditions due to the following aspects. 1) Labeled fault data are difficult to obtain from some machines. 2) An intelligent fault diagnosis method trained with labeled data acquired from one machine possibly fails in classifying unlabeled data acquired from other machines. The above two aspects limit the applications of intelligent methods in diagnosing mechanical faults. As a potential tool to solve the above problem, transfer learning is equipped with the ability to reduce the distribution discrepancy between a source domain and the target domain. This paper proposes a new intelligent method named deep convolutional transfer learning network (DCTLN) to diagnose mechanical faults without labeled data available. DCTLN consists of two modules: condition recognition and domain ...
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Multiple Classifiers and Data Fusion for Robust Diagnosis of Gearbox Mixed Faults

Authors: Jagath Sri Lal Senanayaka; Huynh Van Khang; Kjell G. Robbersmyr Extended Abstract: Detection and isolation of single and mixed faults in a gearbox are essential to enhance the system reliability, lifetime, and service availability. This paper proposes a hybrid learning scheme for gearbox mixed fault diagnosis applications, consisting of multilayer perceptron (MLP)- and convolutional neural network (CNN)-based classifiers, and data fusion for enhancing the robustness. The MLP classifier uses domain knowledge features generated by measuring energies from several frequency bands in the vibration spectrum. The CNN algorithm is trained to identify patterns in the spectrograms of vibration signals via continuous Wavelet transform. A data fusion framework is introduced to improve the robustness and accuracy of the learning algorithms so that the proposed diagnosis scheme can work effectively regardless of noises in the measured data. Within the framework, data fusion is used at feature and decision levels. A Naïve Bayes combiner is selected to fuse results of the individual classifiers at the decision level to enhance the reliability of the fault classification. The robustness of the proposed scheme is tested using two types of noises, namely white Gaussian noise, representing sensor noise and mixing the original vibration signal with another ...
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Wireless Power Transfer – An Overview

Authors: Zhen Zhang; Hongliang Pang; Apostolos Georgiadis; Carlo Cecati Extended Abstract: Due to their simple use, battery-powered devices are becoming widely required in many fields. Moreover, electric vehicles, and autonomous mobile platforms, are expected to be very popular but their use is severely affected by the high cost, heavy weight, encumbrance and low energy density of the batteries. As a novel pattern of energization, the wireless power transfer (WPT) offers a band new way to the energy acquisition for electric-driven devices, thus alleviating the over-dependence on the battery. The paper entitled:  Wireless Power Transfer – An Overview, has overviewed the WPT techniques, with emphasis on fundamentals, technical challenges, metamaterials, and typical applications. The manuscript firstly introduced the working mechanism of inductive power transfer (IPT) systems, then, it compared four typical capacitive compensation networks, and elaborated the magnetic resonant coupling effect as well as the capacitive coupled power transfer (CCPT) mechanism. Then, it has given the answers to two technical questions, namely
  1. why the 2-coils IPT system is commonly used for short-range (shorter than the coil diameter) transmission?
  2. and why the 4-coils IPT system can extend the transmission distance to the mid-range applications?
The key technical issues of WPT ...
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A Modified Neutral Point Balancing Space Vector Modulation for Three-Level Neutral Point Clamped Converters in High-Speed Drives

Authors: Chen Li; Tao Yang; Ponggorn Kulsangcharoen; Giovanni Lo Calzo; Serhiy Bozhko; Christopher Gerada; Patrick Wheeler Extended Abstract: Since the beginning of the More Electric Aircraft (MEA) concept an increasing number of hydraulic, pneumatic and mechanical aircraft functional systems have been considered for replacement by electrical systems to improve fuel efficiency and to reduce emissions. One of the key technologies for the MEA is the aircraft electrical starter generator (ESG) system which enables starting aircraft engine electrically and using the engine to run a generator when the engine reaches a self-sustained speed. Earlier studies presented a novel 270Vdc, 45kVA, 32krpm integrated aircraft starter/generator system. This system consists of a high speed surface-mount permanent magnet synchronous machine and a three-level neutral-point-clamped (NPC) converter. During the engine start-up process, the ESG accelerates the engine shaft up to the ignition speed. In generation mode, the ESG extracts power from the engine shaft and supplies various onboard electrical loads through the three-level converter. This paper describes a high performance neutral-point voltage balancing space vector modulation technique for the three-level NPC converter within the proposed ESG system. Conventional three-level converter space vector modulation schemes do not function well under low power factor, low sample-time-ratio and ...
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Distributed Secondary Control for Active Power Sharing and Frequency Regulation in Islanded Microgrids Using an Event-triggered Communication Mechanism

Authors: Lei Ding; Qing-Long Han; Xian-Ming Zhang Extended Abstract: As a promising paradigm of power grids, a microgrid has been gaining popularity due to its strong ability of integration of renewable energy sources such as wind turbines, photovoltaic arrays and fuel cells. The large-scale penetration of such distributed energy sources makes it costly and sometimes impractical to carry out a centralized control scheme for microgrids. As a result, it is more desirable for microgrids to adopt a distributed control scheme which requires support from communication networks. While the distributed control scheme can improve the reliability, efficiency and scalability of microgrids of microgrids, it always suffers from the limitation of communication resources, probably resulting in detrimental consequences, such as degradation or even damage of control performance. Therefore, it is essential and critical to develop a new and efficient distributed control for microgrids subject to limited communication resources. This article is concern with active power sharing and frequency regulation in an islanded microgrid under event-triggered communication. A distributed secondary control scheme with a sampled-data-based event-triggered communication mechanism is proposed to achieve active power sharing and frequency regulation in a unified framework, where neighbourhood sampled-data exchange occurs only when the predefined triggering condition ...
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Reduced Multilevel Converter: A Novel Multilevel Converter with a Reduced Number of Active Switches

Authors: Margarita Norambuena ; Samir Kouro ; Sibylle Dieckerhoff ; Jose Rodriguez Extended Abstract: Multilevel converters have made a strong entrance in the low voltage market (typically below 690V), particularly for wind and photovoltaic energy conversion systems, UPS, and EV fast-charging stations. Since the device blocking voltage is not an issue in this voltage range, the main driver behind this trend is the more demanding grid codes, the filter size reduction, and the increased efficiency, which are directly or indirectly achieved by the more sinusoidal multilevel voltage waveforms. However since these applications are reaching quite high power levels (e.g. several wind turbines above 5MW, central PV inverters above 2MW), they require several converters operating in parallel, multichannel or interleaved mode. In fact, some PV inverters have up to 12 converters (four per phase) to reach 2MW. In these cases, a reduction in the number of active devices and capacitors for each individual converter could translate in cost reduction, higher power density and less failure probability (less gate drives and capacitors). This paper proposes a new multilevel converter topology which reduces the number of power switches and capacitors to generate the same or more number of output voltage levels as traditional ...
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Industrial Cyberphysical Systems: Realizing Cloud-Based Big Data Infrastructures

Authors: Bo Cheng ; Jingyi Zhang ; Gerhard P. Hancke ; Stamatis Karnouskos ; Armando Walter Colombo Extended Abstract: Internet of Things (IoT) technology provides new opportunities to build powerful industrial systems by connecting a large number of smart networked embedded devices. Devices within these industrial IoT (IIoT) or industrial cyber physical systems (ICPSs) can sense and control physical processes, make autonomous decisions, and communicate and cooperate, thereby collectively generating a massive amount of system data. By capturing, processing, and analyzing significant amounts of data from these devices effectively, industrial companies and organizations can manage their enterprise resources, optimize technical processes, understand the market demand, and develop business intelligence and analytics (BI&A). Due to poor scalability and low performance, many traditional computing technologies are inadequate for handling big data, which are characterized by the volume, velocity, variety, and veracity of the data (each of these characteristics applies to ICPS data). The volume of data will grow with the adoption of IIoT technology. The velocity, i.e., the rate at which data are generated, ingested, and processed, is crucial for decisions that feed back into the system to control real-time industrial processes. Since the ICPS consists of heterogeneous systems of systems, the ...
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Energy Management System With PV Power Forecast to Optimally Charge EVs at the Workplace

Authors: Dennis van der Meer ; Gautham Ram Chandra Mouli ; Germán Morales-España Mouli ; Laura Ramirez Elizondo ; Pavol Bauer Extended Abstract: The number of battery electric vehicles (BEVs) increases rapidly. Some countries, such as Norway and Denmark, are considering banning internal combustion vehicles in the coming decade and the BEV is typically seen as their replacement. However, uncontrolled charging of BEVs poses challenges to the operational performance of the electricity grid while the improvement in greenhouse gas emissions that BEVs offer could be nullified when these are charged with electricity generated from coal or gas fired power plants. To that end, we present an energy management system (EMS) capable of forecasting photovoltaic (PV) power production of a solar carport and optimizing power flows between the solar carport, grid, and BEVs at the workplace. The aim is to minimize the charging cost while reducing the energy demand from the grid by increasing PV self-consumption and consequently increasing sustainability of the BEV fleet. The developed EMS consists of two components: An autoregressive integrated moving average model to predict PV power production and a mixed-integer linear programming framework that optimally allocates power to minimize charging cost. The results show that the ...
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Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries

Authors: Ephrem Chemali ; Phillip J. Kollmeyer ; Matthias Preindl ; Ryan Ahmed ; Ali Emadi Extended Abstract: About half of the total health-related economic cost due to outdoor air pollution can be attributed to the road transport sector. As a result, some countries, like Norway, are considering plans to ban petrol and diesel powered vehicles by 2025. State of charge (SOC) estimation is critical to the safe and reliable operation of Li-ion battery packs, which nowadays are becoming increasingly used in electric vehicles (EVs), Hybrid EVs, unmanned aerial vehicles, and smart grid systems. We introduce a new method to perform accurate SOC estimation for Li-ion batteries using a recurrent neural network (RNN) with long short-term memory (LSTM). The time series-nature of battery data is a great fit for a LSTM-RNN which is widely recognized for its strong performance when encoding dynamics in time. We showcase the LSTM-RNN’s ability to encode dependencies in time and accurately estimate SOC. The LSTM-RNN can map battery measurement signals like voltage, current, and temperature directly to the battery SOC, avoiding computationally intensive filters and inference algorithms like Kalman filters used in traditional SOC estimators. In addition, this machine-learning technique, like all others, is capable ...
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Intelligent Buildings of the Future: Cyberaware, Deep Learning Powered, and Human Interacting

Authors: Milos Manic ; Kasun Amarasinghe ; Juan J. Rodriguez-Andina ; Craig Rieger Abstract: Intelligent buildings are quickly becoming cohesive and integral inhabitants of cyberphysical ecosystems. Modern buildings adapt to internal and external elements and thrive on ever-increasing data sources, such as ubiquitous smart devices and sensors, while mimicking various approaches previously known in software, hardware, and bioinspired systems. This article provides an overview of intelligent buildings of the future from a range of perspectives. It discusses everything from the prospects of U.S. and world energy consumption to insights into the future of intelligent buildings based on the latest technological advancements in U.S. industry and government. 2017 Best paper award for IEEE Industrial Electronics Magazine Check full paper at: https://ieeexplore.ieee.org/document/7792825 ...
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A Robust Adaptive Iterative Learning Control for Trajectory Tracking of Permanent-Magnet Spherical Actuator

Authors: Liang Zhang ; Weihai Chen ; Jingmeng Liu ; Changyun Wen Abstract: This paper presents a robust adaptive iterative learning control (ILC) algorithm for 3-DOF permanent magnet (PM) spherical actuators to improve their trajectory tracking performance. The dynamic model of a PM spherical actuator is a multivariable nonlinear system with interaxis coupling terms. Uncertainties such as modeling errors, loads, and external disturbances exist in the model unavoidably, which will affect the performance, including the precision of the control system. Hence, to compensate for these uncertainties, a new hybrid control scheme that consists of a proportional–derivative (PD) feedback control with varying gains, a PD-type ILC with adjustable gains, and a robust term is developed. The new control law combines the advantages of simplicity and easy design of the PD control, the effectiveness of the ILC to handle model uncertainties and repetitive disturbances, and the robustness of the robust term to random disturbances. In addition, to expedite the convergence rate, the gains in the PD feedback control and the PD-type ILC are adaptively adjusted according to the iteration times. It is shown that the system tracking error approaches zero as the number of iterations increases ...
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Seamless Link-Level Redundancy to Improve Reliability of Industrial Wi-Fi Networks

Authors: Gianluca Cena ; Stefano Scanzio ; Adriano Valenzano Abstract: The adoption of wireless communications and, in particular, Wi-Fi, at the lowest level of the factory automation hierarchy has not increased as fast as expected so far, mainly because of serious issues concerning determinism. Actually, besides the random access scheme, disturbance and interference prevent reliable communication over the air and, as a matter of fact, make wireless networks unable to support distributed real-time control applications properly. Several papers recently appearing in literature suggest that diversity could be leveraged to overcome this limitation effectively. In this paper, a reference architecture is introduced, which describes how seamless link-level redundancy can be applied to Wi-Fi. The framework is general enough to serve as a basis for future protocol enhancements, and also includes two optimizations aimed at improving the quality of wireless communication by avoiding unnecessary replicated transmissions. Some relevant solutions have been analyzed by means of a thorough simulation campaign, in order to highlight their benefits when compared with conventional Wi-Fi. Results show that both packet losses and network latencies improve noticeably. 2017 Best paper award for IEEE Transactions on Industrial Informatics Check full paper at: https://ieeexplore.ieee.org/document/7393813 ...
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