Application of Artificial Neural Network Models for Condition Monitoring of Industrial Fan

Authors

  • Jitendra Kumar Sharma Research Scholar, Department Mechanical Engineering , SAGE University, Indore, India
  • Suman Sharma Professor, Department Mechanical Engineering , SAGE University, Indore, India

Keywords:

Condition monitoring, Artificial Intelligence (AI), Artificial Neural Networks (ANN), Industrial Fans & Blowers

Abstract

The condition-based maintenance philosophy receives a significant amount of attention during the operation of machine maintenance. It is an effective tool for lowering the cost of maintenance, decreasing the number of times machines are offline, and preventing unscheduled breakdowns of machines and equipments. This study aims to boost the plant's availability by safeguarding it against failure in its early stages and assuring the general safety of people and machinery. In artificial intelligence (AI), adaptive system technologies such as neural networks have been used successfully for monitoring the condition of machines. This paper aims to highlight the application of Artificial Neural Network (ANN) techniques or models in condition monitoring of industrial fans and blowers using vibration signals and, in turn, comparing the performance of the same for training and testing time, quantifying and classifying the faults. test success and accuracy.

Downloads

Download data is not yet available.

References

Shiroishi, Y. Li, S. Liang, T. Kurfess, and S. Danyluk, "Bearing condition diagnostics via vibration and acoustic emission measurements," Mechanical Systems and Signal Processing, vol. 11, no. 5, pp. 693–705, 1997.

K. Nandi, "Advanced digital vibration signal processing for condition monitoring," in Proc. 13th International Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management (COMADEM' 00), pp. 129–143, Houston, Tex, USA, December 2000.

Pradeep Yadav, Dr Asesh Tiwari: ‘Condition monitoring of gas turbine using ANN’ Proceeding of all india seminar on Advances in Tribology & Maintenance MITM Indore, Dec.2007, pp 42-43.

B. Samanta , Khamis R. Al-Balushi, Saeed A. Al-Araimi, "Bearing Fault Detection Using Artificial Neural Networks and Genetic Algorithm", EURASIP Journal on Applied Signal Processing 2004:3, 366–377

B.Kishore , M.R.S.Satyanarayana and K.Sujatha, "Intelligent Condition Monitoring of AIRBLOWER using Artificial Neural Network with Genetic Algorithm", International Journal of Engineering Research & Technology (IJERT) Vol. 1 Issue 6, August – 2012 , pp. 1-10

Mostafa M., Hassan M.M.,Hassaan G. "Diagnosis of rotating machines faults using artificial intelligence based on preprossesing for input data". Proceeding of the 26th conference of fruct association.

Omar Alshorman, Muhammad Irfan, Nordin Saad, , 'A review of artificial intelligence methods for condition monitoring and fault diagnosis of rolling element bearing for induction motor' Hindawi journal,Vol.2020, Article ID 8843759, 04 Nov.2020

Cory W. T. W., 'Overview of condition monitoring methods with emphasis on industrial fans', Journal of power and energy ,Proc. Instn. Mech. Engrs. Vol. 205, IMech, pp. 225-240 (1991).

Mohamad Hazwan Mohd Gazali, Wan Rahiman,' Vibration analysis for machine monitoring and diagnosis.: A systematic review' Review article,Volume 2021. Article ID 9469318, Hindwai publication, 11 Sep. 2021.https://doi.org/10.1155/2021/9469318

Aroui T., Koubaa Y., Toumi A., 'Application of Feed forward Neural Network for Induction Machine Rotor Faults Diagnostics using Stator Current', JES 2007 online: http://journal.esrgroups.org/jes (2007).

Samanta B., Khamis R. Al-Balushi and Saeed A. Al-Araimi, 'Bearing fault detection using Artificial Neural Networks and Genetic Algorithm', EURASIP Journal on Applied Signal Processing 2004:3, pp.366–377 (2004).

Purnawansyah B.N.,Haviluddin H. (22) comparing performance of back propagation and RBF Neural network models for predicting daily network traffic.,Makkassar International Conference on Electrical engineering and Informatics (MICEEI) 2014, At Universitas Hasanddin Makassar (November 2016).

Vana Vital Rao, Chanamala R.”Estimation of Defect Severity in Rolling Element Bearings using Vibration Signals with Artificial Neural Network” , Jordan Journal of Mechanical and Industrial Engineering, Volume 9 Number 2, April.2015, ISSN 1995-6665, Pages 113-120

Labib Sharrar and Kumeresan Danapalasingam “Intelligent Vibration Analysis of Industrial Cooling Fans,” ELEKTRIKA Journal of Electrical Engineering, VOL.21,NO.2,2022, 54-63, www.fke.utm.my/elektrika ISSN 0128-4428

Said Haggag, Ahmed R. Adly and Magdy M.Z. Abdelaal.” Artificial Neural Network Model for Fault Diagnosis of Rotating Machine in ETRR-2 Research Reactor, Arab J. Nucl. Sci. Appl., Vol.55, 3, 55-61 [2022] ISSN 1110-0451

C. -C. Peng and C. -Y. Su, "Modeling and Parameter Identification of a Cooling Fan for Online Monitoring," in IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-14, 2021, Art no. 3522914, doi: 10.1109/TIM.2021.3104375.

Azzeddine Dkhane,Adel Djellal,Fouaz B.Rabah Lakel “Cooling fan combined fault vibration analysis using convolutional neural network classifier” NISS2020: Proceedings of the 3rd International Conference on Networking, Information Systems & Security , March 2020, Article No.: 79, Pages 1-6, https://doi.org/10.1145/3386723.3387898

Giuseppe Ciaburro” Machine fault detection methods based on machine learning algorithms:A review,” Journal of Mathematical Biosciences and Engineering, MBE, : 11453-11490. DOL: 10.3934/mbe.2022534, Published: 10 August 2022, http://www.aimspress.com/journal/MBE

Timande, S., & Dhabliya, D. (2019). Designing multi-cloud server for scalable and secure sharing over web. International Journal of Psychosocial Rehabilitation, 23(5), 835-841. doi:10.37200/IJPR/V23I5/PR190698

Dhabliya, P. D. . (2020). Multispectral Image Analysis Using Feature Extraction with Classification for Agricultural Crop Cultivation Based On 4G Wireless IOT Networks. Research Journal of Computer Systems and Engineering, 1(1), 01–05. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/10

Mr. Dharmesh Dhabliya. (2012). Intelligent Banal type INS based Wassily chair (INSW). International Journal of New Practices in Management and Engineering, 1(01), 01 - 08. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/2

Thangamayan, S., Kumar, B., Umamaheswari, K., Arun Kumar, M., Dhabliya, D., Prabu, S., & Rajesh, N. (2022). Stock price prediction using hybrid deep learning technique for accurate performance. Paper presented at the IEEE International Conference on Knowledge Engineering and Communication Systems, ICKES 2022, doi:10.1109/ICKECS56523.2022.10060833 Retrieved from www.scopus.com

Downloads

Published

16.08.2023

How to Cite

Sharma, J. K. ., & Sharma, S. . (2023). Application of Artificial Neural Network Models for Condition Monitoring of Industrial Fan. International Journal of Intelligent Systems and Applications in Engineering, 11(10s), 574–590. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3312

Issue

Section

Research Article