title: THE APPLICATION OF MACHINE LEARNING ALGORITHMS FOR PREDICTIVE MAINTENANCE AND
text: 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.