Uneingeschränkter Zugang

Research on the identification of common faults of agricultural machinery based on vibration characteristics


Zitieren

Zhang Y., Qian T., Tang W. (2022). Buildings-to-distribution-network integration considering power transformer loading capability and distribution network reconfiguration. Energy, 244. Search in Google Scholar

T. Qian, Xingyu Chen, Yanli Xin, W. H. Tang, Lixiao Wang. (2022). Resilient Decentralized Optimization of Chance Constrained Electricity-gas Systems over Lossy Communication Networks. Energy, 239, 122158. Search in Google Scholar

T. Qian, Y. Liu, W. H Zhang, W. H. Tang, M. (2020). Shahidehpour. Event-Triggered Updating Method in Centralized and Distributed Secondary Controls for Islanded Microgrid Restoration. IEEE Transactions on Smart Gird, 11(2), 1387-1395. Search in Google Scholar

Ch Fang, Yn Tao, Jg Eang, et al. (2021). Mapping Relation of Leakage Currents of Polluted Insulators and Discharge Arc Area. Frontiers In Energy Research. Search in Google Scholar

Jan, Sana Ullah, Young Doo Lee, and In Soo Koo. (2021). A distributed sensor-fault detection and diagnosis framework using machine learning. Information Sciences 547, 777-796. Search in Google Scholar

Lu, Siliang, Qingbo He, and Jun Wang. (2019). A review of stochastic resonance in rotating machine fault detection. Mechanical Systems and Signal Processing 116, 230-260. Search in Google Scholar

Rehman, Tanzeel U., et al. (2019). Current and future applications of statistical machine learning algorithms for agricultural machine vision systems. Computers and electronics in agriculture 156 585-605. Search in Google Scholar

Van Loon, Jelle, et al. (2020). Scaling agricultural mechanization services in smallholder farming systems: Case studies from sub-Saharan Africa, South Asia, and Latin America. Agricultural systems 180, 102792. Search in Google Scholar

Choudhary, Anurag, et al. (2019). Condition monitoring and fault diagnosis of induction motors: A review. Archives of Computational Methods in Engineering 26. 4, 1221-1238. Search in Google Scholar

Gerasymova, Irina, et al. (2019). Forming Professional Mobility in Future Agricultural Specialists: the Sociohistorical Context. Romanian Journal for Multidimensional Education/Revista Romaneasca pentru Educatie Multidimensionala 11. Search in Google Scholar

Yan, Xiaoan, Ying Liu, and Minping Jia. (2020). Multiscale cascading deep belief network for fault identification of rotating machinery under various working conditions. Knowledge-Based Systems 193, 105484. Search in Google Scholar

Silva, Sergio, et al. (2018). High impedance fault detection in power distribution systems using wavelet transform and evolving neural network. Electric power systems research 154, 474-483. Search in Google Scholar

Lakhiar, Imran Ali, et al. (2018). Monitoring and control systems in agriculture using intelligent sensor techniques: A review of the aeroponic system. Journal of Sensors 2018. Search in Google Scholar

Kim, Woohyun, and Srinivas Katipamula. (2018). A review of fault detection and diagnostics methods for building systems. Science and Technology for the Built Environment 24.1, 3-21. Search in Google Scholar

Liu, Jun, and Xuewei Wang. (2021). Plant diseases and pests detection based on deep learning: a review. Plant Methods 17.1, 1-18. Search in Google Scholar

Geetharamani, G., and Arun Pandian. (2019). Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Computers & Electrical Engineering 76, 323-338. Search in Google Scholar

Choudhary, Anurag, Tauheed Mian, and Shahab Fatima. (2021). Convolutional neural network based bearing fault diagnosis of rotating machine using thermal images. Measurement 176, 109196. Search in Google Scholar

Keller, Thomas, et al. (2019). Historical increase in agricultural machinery weights enhanced soil stress levels and adversely affected soil functioning. Soil and Tillage Research 194, 104293. Search in Google Scholar

Sambasivam, G., and Geoffrey Duncan Opiyo. (2021). A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using convolutional neural networks. Egyptian Informatics Journal 22. 1, 27-34. Search in Google Scholar

Roldán, Juan Jesús, et al. (2018). Robots in agriculture: State of art and practical experiences. Service robots, 67-90. Search in Google Scholar

Bakhshipour, Adel, and Abdolabbas Jafari. (2018). Evaluation of support vector machine and artificial neural networks in weed detection using shape features. Computers and Electronics in Agriculture 145, 153-160. Search in Google Scholar

Edwards, Clive A. (2020). The importance of integration in sustainable agricultural systems. Sustainable agricultural systems. CRC Press, 249-264. Search in Google Scholar

Ding, Ying, et al. (2018). Model predictive control and its application in agriculture: A review. Computers and Electronics in Agriculture 151, 104-117. Search in Google Scholar

Villa-Henriksen, Andrés, et al. (2020). Internet of Things in arable farming: Implementation, applications, challenges and potential. Biosystems engineering 19, 60-84. Search in Google Scholar

Habib, Md Tarek, et al. (2020). Machine vision based papaya disease recognition. Journal of King Saud University-Computer and Information Sciences 32. 3, 300-309. Search in Google Scholar

Rolnick, David, et al. (2022). Tackling climate change with machine learning. ACM Computing Surveys (CSUR) 55. 2, 1-96. Search in Google Scholar

Aazam, Mohammad, Sherali Zeadally, and Khaled A. Harras. (2018). Deploying fog computing in industrial internet of things and industry 4.0. IEEE Transactions on Industrial Informatics 14. 10, 4674-4682. Search in Google Scholar

Oh, Dong Yul, and Il Dong Yun. (2018). Residual error based anomaly detection using auto-encoder in SMD machine sound. Sensors 18. 5, 1308. Search in Google Scholar

Tang, Yunchao, et al. (2020). Recognition and localization methods for vision-based fruit picking robots: A review. Frontiers in Plant Science 11, 510. Search in Google Scholar

Choudhary, Anurag, Deepam Goyal, and Shimi Sudha Letha. (2020). Infrared thermography-based fault diagnosis of induction motor bearings using machine learning.” IEEE Sensors Journal 21. 2, 1727-1734. Search in Google Scholar

Yang, Xing, et al. (2021). A survey on smart agriculture: Development modes, technologies, and security and privacy challenges. IEEE/CAA Journal of Automatica Sinica 8. 2, 273-302. Search in Google Scholar

Nassar, Dina Mamdouh, and Hanan Gamil Elsayed. (2018). From informal settlements to sustainable communities. Alexandria engineering journal 57. 4, 2367-2376. Search in Google Scholar

Nassar, Dina Mamdouh, and Hanan Gamil Elsayed. (2018). From informal settlements to sustainable communities. Alexandria engineering journal 57. 4, 2367-2376. Search in Google Scholar

Sarker, Iqbal H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science 2. 3, 1-21. Search in Google Scholar

Zhang, Zongzhen, et al. (2019). General normalized sparse filtering: A novel unsupervised learning method for rotating machinery fault diagnosis. Mechanical Systems and Signal Processing 124, 596-612. Search in Google Scholar

eISSN:
2444-8656
Sprache:
Englisch
Zeitrahmen der Veröffentlichung:
Volume Open
Fachgebiete der Zeitschrift:
Biologie, andere, Mathematik, Angewandte Mathematik, Allgemeines, Physik