Namangan Institute of Engineering and Technology
nammti.uz
10.25.2023
Pg.376
Introduction. Manufacturing and industrial facilities generate vast amounts of data through
sensors, control systems, and equipment logs. This data contains valuable insights that can be
utilized to make maintenance predictive and processes more efficient. However, transforming raw
data into actionable insights requires advanced analytical capabilities. This is where machine
learning algorithms come into play. Machine learning refers to the use of artificial intelligence (AI)
that provides systems the ability to automatically learn and improve from experience without being
explicitly programmed. Machine learning algorithms can analyze manufacturing and industrial data
to uncover patterns and relationships that would be difficult or impossible to discover using
traditional rule-based techniques.
There are several types of machine learning algorithms, including supervised learning,
unsupervised learning, and reinforcement learning. Supervised learning algorithms train on labeled
example data, while unsupervised algorithms find hidden patterns and associations in unlabeled
data. Reinforcement learning optimizes decision making through trial-and-error interactions with
an environment. Each technique offers different benefits for predictive maintenance and process
optimization. This article explores key applications of machine learning algorithms within
manufacturing and industrial settings.
