Ieuan Griffiths
ABSTRACT
Machine maintenance is a major part of total operating costs for manufacturing and production plants, hence, having a cost-effective maintenance strategy is key to success. Most standard solutions operate on a time-driven approach where repairs or replacements are carried out after a given amount of time. This results in healthy machines being replaced unnecessarily incurring losses in both time, resource, and parts. Predictive maintenance utilises sensors capable of collecting real-time operating data and analytical techniques to predict when a machine will require maintenance. This research develops a vibration analysis tool that can detect and differentiate a machines operating conditions without any human analysis or intervention demonstrated via a case study. By using spectrograms in conjunction with image recognition and unsupervised learning, the tool is capable of being applied to many different machines without modification. Results show that features extracted using convolutional auto-encoding and t-SNE, are capable of being clustered in an unsupervised manner accurately based on case study test conditions. Future work looks at the application of this tool into a real-time data stream that can determine operating conditions in real-time.
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Index Terms
- Automated Condition Monitoring Using Artificial Intelligence
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