مقاله انگلیسی رایگان در مورد نظارت بر توربین‌ های بادی توسط مدل پیش‌بینی شبکه عصبی هوش مصنوعی – تیلور و فرانسیس ۲۰۲۲

مقاله انگلیسی رایگان در مورد نظارت بر توربین‌ های بادی توسط مدل پیش‌بینی شبکه عصبی هوش مصنوعی – تیلور و فرانسیس ۲۰۲۲

 

مشخصات مقاله
ترجمه عنوان مقاله یک مدل پیش‌بینی شبکه عصبی هوش مصنوعی برای تشخیص ناهنجاری و پایش توربین‌ های بادی با استفاده از داده‌ های SCADA
عنوان انگلیسی مقاله An Artificial Intelligence Neural Network Predictive Model for Anomaly Detection and Monitoring of Wind Turbines Using SCADA Data
انتشار  مقاله سال ۲۰۲۲
تعداد صفحات مقاله انگلیسی  ۱۴ صفحه
هزینه  دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده  نشریه تیلور و فرانسیس – Taylor & Francis
نوع نگارش مقاله مقاله پژوهشی (Research article)
مقاله بیس این مقاله بیس میباشد
نمایه (index) JCR – Master Journal List – Scopus
نوع مقاله
ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۲٫۹۱۹ در سال ۲۰۲۰
شاخص H_index ۵۹ در سال ۲۰۲۲
شاخص SJR ۰٫۵۰۶ در سال ۲۰۲۰
شناسه ISSN ۱۰۸۷-۶۵۴۵
شاخص Quartile (چارک) Q3 در سال ۲۰۲۰
فرضیه ندارد
مدل مفهومی دارد
پرسشنامه دارد
متغیر دارد
رفرنس دارد
رشته های مرتبط مهندسی کامپیوتر – مهندسی انرژی
گرایش های مرتبط هوش مصنوعی – مهندسی نرم افزار – سیستم های انرژی
نوع ارائه مقاله
ژورنال
مجله / کنفرانس هوش مصنوعی کاربردی – Applied Artificial Intelligence
دانشگاه Brunel Innovation Centre, Brunel University London, UK
شناسه دیجیتال – doi https://doi.org/10.1080/08839514.2022.2034718
کد محصول e16623
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:

Abstract

Introduction

Methods

Results

Discussion

Conclusion

References

 

بخشی از متن مقاله:

Abstract

     The industry 4.0 has created a paradigm shift in how industrial equipment could be monitored and diagnosed with the help of emerging technologies such as artificial intelligence (AI). AI-driven troubleshooting tools play an important role in high-efficacy diagnosis and monitoring processes, especially for systems consisting of several components including wind turbines (WTs). The utilization of such approaches not only reduces the troubleshooting and diagnosis time but also enables fault prevention by predicting the behavior of different components and calculating the probability of near future failure. This not only decreases the costs of repair by providing constant component’s monitoring and identifying faults’ causes but also increases the efficacy of the apparatus by lowering the downtimes due to the AI-driven early warning system. This article evaluated, compared, and contrasted eight different artificial neural network (ANN) models for diagnosis and monitoring of WTs that predict the machinery’s system failure based on internal components’ sensor signals and generation temperature. This article employed a machine learning model approach with two hidden layers using multilayer linear regression to achieve its objective. The developed system predicted the output of the WT’s generator temperature with an accuracy of 99.8% with 2 months in advance measurement prediction.

Introduction

     Industry 4.0 introduced a new paradigm to the machineries’ monitoring and diagnosis procedure. Enabled by advances in artificial intelligence (AI) and notions such as Internet of Things (IoT) in recent years, Industry 4.0 has proved to be an effective and reliable trend toward digitization and automation (Haag and Anderl 2018). Industry 4.0 is the fourth paradigm shift and major breakthrough in industrial revolution made possible by the advancements in electronics and information technology; a continuation of the evolution of automation commenced from the invention of steam engines and mass production as a result of assembly lines and standardization (Xu, Xu, and Li 2018).

     The wind power industry and the whole renewable energy sector could benefit significantly from the employment of industry 4.0. Most of the machineries including wind turbines (WTs) produce a huge amount of data related to power consumption, current, voltage, vibration, and environmental factors that are not necessarily utilized. The information processed from these gathered data could be used to improve the troubleshooting, monitoring, and maintenance procedures. Sensors attached to different parts of WTs will provide important data of the health state of the apparatus, which require interpretation and processing.

Conclusion

     Eight different ML models were developed with different sensors’ data based on SCADA data collected from nine WTs over 10 years received from the Westmill Windfarm located in Swindon, United Kingdom, to predict the generator failure in WTs in advance using pattern recognition based on historical data. The results of each model’s accuracy in terms of minimum, maximum, and standard deviation offsets between the predicted and actual generator temperature values were compared and contrasted, and the effect of the input sensor data was explored. Overall, this research showed the possibility of utilizing ML-driven regression algorithms to predict WTs’ generator failure caused by heat, lowering the maintenance costs related to downtime and staff,and, at the same time, improving the operational availability of the apparatuses. For the future works, the authors of this article aim to explore the possibility of implementing transfer learning for fast adaptation and deployment of the trained models to new WTs, allowing quick training of new assets and lowering the readiness time required for the model.

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