Multi-sensor edge computing architecture for identification of failures short-circuits in wind turbine generators

Xu, Yue; Nascimento, Navar Medeiros M. e; Sousa, Pedro Henrique Feijó de; Nogueira, Fabrício G.; Torrico, Bismark C.; Han, Tao; Jia, Chuanyu; Filho, Pedro Pedrosa Rebouças

doi:10.1016/j.asoc.2020.107053

Cited by 28 publications

(21 citation statements)

References 36 publications

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“…Uhlmann et al [117] described the solution of sensor network enhanced by cloud. The edge technologies [156] allow integration between PLC and cloud for modern sensors. Miniaturisation of current sensors and nanotechnology [157] provides higher flexibility of maintenance systems.…”

Section: Sensor-based Smart Factory Discussionmentioning

confidence: 99%

Predictive Maintenance and Intelligent Sensors in Smart Factory: Review

Pech

Vrchota

Bednář

2021

Sensors

212

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With the arrival of new technologies in modern smart factories, automated predictive maintenance is also related to production robotisation. Intelligent sensors make it possible to obtain an ever-increasing amount of data, which must be analysed efficiently and effectively to support increasingly complex systems’ decision-making and management. The paper aims to review the current literature concerning predictive maintenance and intelligent sensors in smart factories. We focused on contemporary trends to provide an overview of future research challenges and classification. The paper used burst analysis, systematic review methodology, co-occurrence analysis of keywords, and cluster analysis. The results show the increasing number of papers related to key researched concepts. The importance of predictive maintenance is growing over time in relation to Industry 4.0 technologies. We proposed Smart and Intelligent Predictive Maintenance (SIPM) based on the full-text analysis of relevant papers. The paper’s main contribution is the summary and overview of current trends in intelligent sensors used for predictive maintenance in smart factories.

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Section: Sensor-based Smart Factory Discussionmentioning

confidence: 99%

Predictive Maintenance and Intelligent Sensors in Smart Factory: Review

Pech

Vrchota

Bednář

2021

Sensors

212

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“…According to this framework, a machine learning prediction model is built based on historical data in the cloud, which has theoretically unlimited resources; then, the model is applied to new incoming data streams at the edge, which has fewer computation resources, to identify possible failures with increased responsiveness. A similar approach was adopted in [24], where a multi-sensor edge computing architecture was proposed for wind turbine generators. To the best of our knowledge, no edge-cloud PHM framework has been designed for facilitating the data exchange and analysis between a machine producer and a machine user.…”

Section: Phm: Reference Methodologies Framework and Architecturesmentioning

confidence: 99%

Predictive Maintenance: A Novel Framework for a Data-Driven, Semi-Supervised, and Partially Online Prognostic Health Management Application in Industries

et al. 2021

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Prognostic Health Management (PHM) is a predictive maintenance strategy, which is based on Condition Monitoring (CM) data and aims to predict the future states of machinery. The existing literature reports the PHM at two levels: methodological and applicative. From the methodological point of view, there are many publications and standards of a PHM system design. From the applicative point of view, many papers address the improvement of techniques adopted for realizing PHM tasks without covering the whole process. In these cases, most applications rely on a large amount of historical data to train models for diagnostic and prognostic purposes. Industries, very often, are not able to obtain these data. Thus, the most adopted approaches, based on batch and off-line analysis, cannot be adopted. In this paper, we present a novel framework and architecture that support the initial application of PHM from the machinery producers’ perspective. The proposed framework is based on an edge-cloud infrastructure that allows performing streaming analysis at the edge to reduce the quantity of the data to store in permanent memory, to know the health status of the machinery at any point in time, and to discover novel and anomalous behaviors. The collection of the data from multiple machines into a cloud server allows training more accurate diagnostic and prognostic models using a higher amount of data, whose results will serve to predict the health status in real-time at the edge. The so-built PHM system would allow industries to monitor and supervise a machinery network placed in different locations and can thus bring several benefits to both machinery producers and users. After a brief literature review of signal processing, feature extraction, diagnostics, and prognostics, including incremental and semi-supervised approaches for anomaly and novelty detection applied to data streams, a case study is presented. It was conducted on data collected from a test rig and shows the potential of the proposed framework in terms of the ability to detect changes in the operating conditions and abrupt faults and storage memory saving. The outcomes of our work, as well as its major novel aspect, is the design of a framework for a PHM system based on specific requirements that directly originate from the industrial field, together with indications on which techniques can be adopted to achieve such goals.

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“…A remote expert system is deployed at the cloud platform layer to provide a rich knowledge system and experiences, make in-depth reasoning and decision-making on the analysis results, and process various issues effectively. Moreover, the cloud platform layer can display the status data of intelligent equipment and components in real-time, and users can openly and transparently understand the overall status of industrial processing and production [ 21 ]. The cloud platform can also share real-time data to the mobile PC via the Web terminal, ensuring that the industrial processing and production information can be viewed at any time.…”

Section: Methodsmentioning

confidence: 99%

Design and fault diagnosis of DCS sintering furnace’s temperature control system for edge computing

Wen

2021

PLoS ONE

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Under the background of modern industrial processing and production, the sintering furnace’s temperature control system is researched to achieve intelligent smelting and reduce energy consumption. First, the specific application and implementation of edge computing in industrial processing and production are analyzed. The industrial processing and production intelligent equipment based on edge computing includes the equipment layer, the edge layer, and the cloud platform layer. This architecture improves the operating efficiency of the intelligent control system. Then, the sintering furnace in the metallurgical industry is taken as an example. The sintering furnace connects powder material particles at high temperatures; thus, the core temperature control system is investigated. Under the actual sintering furnace engineering design, the Distributed Control System (DCS) is used as the basis of sintering furnace temperature control, and the Programmable Logic Controller (PLC) is adopted to reduce the electrical wiring and switch contacts. The hardware circuit of DCS is designed; on this basis, an embedded operating system with excellent performance is transplanted according to functional requirements. The final DCS-based temperature control system is applied to actual monitoring. The real-time temperature of the upper, middle, and lower currents of 1# sintering furnace at a particular point is measured to be 56.95°C, 56.58°C, and 57.2°C, respectively. The real-time temperature of the upper, middle, and lower currents of 2# sintering furnaces at a particular point is measured to be 144.7°C, 143.8°C, and 144.0°C, respectively. Overall, the temperature control deviation of the three currents of the two sintering furnaces stays in the controllable range. An expert system based on fuzzy logic in the fault diagnosis system can comprehensively predict the situation of the sintering furnaces. The prediction results of the sintering furnace’s faults are closer to the actual situation compared with the fault diagnosis method based on the Backpropagation (BP) neural network. The designed system makes up for the shortcomings of the sintering furnace’s traditional temperature control systems and can control the temperature of the sintering furnace intelligently and scientifically. Besides, it can diagnose equipment faults timely and efficiently, thereby improving the sintering efficiency.

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Multi-sensor edge computing architecture for identification of failures short-circuits in wind turbine generators

Cited by 28 publications

References 36 publications

Predictive Maintenance and Intelligent Sensors in Smart Factory: Review

Predictive Maintenance and Intelligent Sensors in Smart Factory: Review

Predictive Maintenance: A Novel Framework for a Data-Driven, Semi-Supervised, and Partially Online Prognostic Health Management Application in Industries

Design and fault diagnosis of DCS sintering furnace’s temperature control system for edge computing

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