Page 9 - Predictive Maintenance and Enhanced Quality with

2.3 Big data analytics

It is the process of drawing conclusions and patterns

from vast amounts of data.

Predictive maintenance processes and analyzes the

gathered data to find trends, patterns, and
abnormalities. It does this by utilizing big data

analytics techniques.

The use of advanced analytics tools improves

maintenance decision-making by revealing hidden
connections and anticipating equipment failure.

2.4 Artificial Intelligence and Machine Learning

By examining data and identifying trends, artificial
intelligence (AI) and machine learning (ML) algorithms

enable predictive maintenance.

Algorithms powered by AI and ML can automatically
find abnormalities, forecast equipment faults, and

suggest the best course of maintenance.

The accuracy and efficiency of predictive maintenance
systems may be increased by these algorithms as they
learn from fresh data over time.

2.5 Sensor Technologies

Sensors are essential for predictive maintenance since
they record information about the health and performance

of the equipment.

A variety of sensors, including pressure, vibration, and

temperature sensors, offer real-time data on important
variables.

Sensor technologies allow for continuous equipment
monitoring, spotting changes from the expected operating

parameters, and sending out maintenance notifications.