International Journal of Scientific Research and Engineering Development

In recent years, deep learning algorithms for problem detection in industrial machinery have gained interest due to manufacturing process complexity and the need for dependability and efficiency. The use of deep learning for the purpose of improving fault detection in industrial machinery. It is of the utmost importance to have defect detection mechanisms that are both reliable and effective, since the complexity of industrial processes continues to increase. In this paper, the implementation of deep learning algorithms is investigated. These algorithms make use of neural networks to understand complex patterns and anomalies that are present in data coming from machinery. There are many different models that are being researched to see whether or not they are effective in detecting defects at early stages, limiting downtime, and eliminating costly interruptions. The findings demonstrate the promise of deep learning as a significant tool for enhancing defect detection skills, thereby paving the way for industrial equipment systems that are more reliable and resilient.This research will help industrial defect detection systems become more dependable and sophisticated as technology advances.

Afrah Mahmood Abdulla,"Deep Learning Based Defect Detection" International Journal of Scientific Research and Engineering Development, V9(3): Page(2500-2507) May-June 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.