Fault Classification for Cooling System of Hydraulic Machinery Using AI
Author:
Khan Haseeb Ahmed1, Bhatti Uzair1, Kamal Khurram1, Alkahtani Mohammed2ORCID, Abidi Mustufa Haider2ORCID, Mathavan Senthan3
Affiliation:
1. Department of Engineering Sciences, National University of Sciences and Technology, Islamabad 44000, Pakistan 2. Department of Industrial Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia 3. Department of Civil and Structural Engineering, Nottingham Trent University, Burton Street, Nottingham NG1 4BU, UK
Abstract
Hydraulic systems are used in all kinds of industries. Mills, manufacturing, robotics, and Ports require the use of Hydraulic Equipment. Many industries prefer to use hydraulic systems due to their numerous advantages over electrical and mechanical systems. Hence, the growth in demand for hydraulic systems has been increasing over time. Due to its vast variety of applications, the faults in hydraulic systems can cause a breakdown. Using Artificial-Intelligence (AI)-based approaches, faults can be classified and predicted to avoid downtime and ensure sustainable operations. This research work proposes a novel approach for the classification of the cooling behavior of a hydraulic test rig. Three fault conditions for the cooling system of the hydraulic test rig were used. The spectrograms were generated using the time series data for three fault conditions. The CNN variant, the Residual Network, was used for the classification of the fault conditions. Various features were extracted from the data including the F-score, precision, accuracy, and recall using a Confusion Matrix. The data contained 43,680 attributes and 2205 instances. After testing, validating, and training, the model accuracy of the ResNet-18 architecture was found to be close to 95%.
Funder
King Saud University
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference36 articles.
1. (2023, June 18). Hydraulic Equipment Market Size, Trends|Industry Forecast 2026. Available online: https://www.technavio.com/report/hydraulic-equipment-market-industry-analysis. 2. State-of-the-art methods and results in tool condition monitoring: A review;Rehorn;Int. J. Adv. Manuf. Technol.,2005 3. Hierarchical fault classification for resource constrained systems;Adams;Mech. Syst. Signal Process.,2019 4. Identification and quantification of hydraulic system faults based on multivariate statistics using spectral vibration features;Helwig;Procedia Eng.,2015 5. Yuan, Y., Ma, G., Cheng, C., Zhou, B., Zhao, H., Zhang, H.-T., and Ding, H. (2018). Artificial intelligent diagnosis and monitoring in manufacturing. arXiv, arXiv190102057.
|
|