We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Transfer Learning-Based Fault Diagnosis of Single-Stage Single-Acting Air Compressor.
- Authors
Chakrapani, G.; Naveen Venkatesh, S.; Aravinth, S.; Sugumaran, V.
- Abstract
Introduction: Reciprocating air compressor, which is also known as piston compressor is one of the crucial machinery used in various production lines to move gas at high pressure. Research question: The prolonged operation of this machine can lead to internal damage. Therefore, it is highly important to incorporate fault diagnosis to prevent sudden and unforeseen failure. Condition monitoring and fault diagnosis of machines are becoming more and more crucial in various industries. They have special importance in places where the breakdown of machines can cause a tremendous financial crisis. Although there are extensive research works in this area, fault diagnosis of reciprocating air compressors using deep learning is still unexplored. Methodology: In this paper, the condition monitoring of air compressors is discussed using deep learning methods. First, different modes of signal acquisition are surveyed, and the best one among them is chosen. Out of several faults, five significant faults that are prone to air compressors are taken into study. Magnitudes of vibration signals are captured using the accelerometer sensor. These signals are converted to plots using MATLAB and the faults are classified using pretrained networks like AlexNet, GoogLeNet, ResNet50, VGG19 and VGG16. Results: The results obtained show that the AlexNet pretrained network exhibits the best fault classification rate of 100% in a minimum computational time of 570 s.
- Subjects
FAULT diagnosis; AIR compressors; DEEP learning; FINANCIAL crises; AIR conditioning; RESEARCH questions; MONITORING of machinery
- Publication
Journal of Vibration Engineering & Technologies, 2024, Vol 12, Issue 3, p4411
- ISSN
2523-3920
- Publication type
Article
- DOI
10.1007/s42417-023-01128-8