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Predictive_Maintenance technique ppt.pptx
1. Name Registration No. Slides
Adil Bashir 2020-IM-11 Slide no. 11-14
Syed. M. Akrama 2020-IM-12 Slide no. 1-4
Muhammad Umer 2020-IM-16 Slide no. 5-10
Predictive Maintenance:
Enhancing Asset Reliability
Plant Maintenance:
Group 3
Presented to : Dr. Shahzad Faisal
2. 1. Introduction
2. Importance of Predictive Maintenance
3. Key Components
4. Benefits
5. Technologies Involved
6. Implementation Process
7. Case Studies
8. Challenges
9. Best Practices
10. Future Trends
11. Conclusion
Table of Content
3. Introduction:
What is Definition of Predictive Maintenance (PdM) ?
Predictive maintenance is a proactive management strategy that
involves continuously monitoring and analyzing the operational status
of equipment, production processes, and administrative operations in
order to anticipate and prevent any problems before they cause
downtime or poor performance [1].
Slide no. 1
4. 1.Reactive Maintenance:
• Addressing difficulties as they arise or equipment fails.
• Characterized by unanticipated downtime and higher repair
costs.
2. Preventive Maintenance:
• Routine maintenance, regardless of equipment condition.
• Aims to prevent potential faults and extend equipment
lifespan [2].
Brief overview of traditional maintenance approaches: Slide no. 2
5. 3.Scheduled Maintenance:
• Maintenance activities determined by calendar time or usage
criteria.
• Helps plan maintenance efforts, but may not handle
equipment condition differences.
4.Corrective Maintenance:
• Addressing concerns that are found through normal
inspections or monitoring.
• Aims to fix recognized problems quickly, but wait for the
failure to occur.
Slide no. 3
Brief overview of traditional maintenance approaches:
6. 1. It comprises using data analytics, sensors, and machine
learning to predict equipment failures before they happen.
2. Predictive techniques offer proactive maintenance through
real-time state of the equipment monitoring.
3. This change intends to reduce downtime, lower
maintenance costs, and improve overall equipment
performance [2].
Slide no. 4
Shift towards predictive strategies:
7. • Data collection and sensors:
This includes collecting data about how machines are doing
using particular devices known as sensors. Sensors measure
temperatures and vibrations.
• Condition monitoring:
This involves monitoring the machines on a regular basis to ensure that
they are working properly. We examine the data from the sensors to
ensure that everything is normal [3].
Slide no. 5
Key Components of Predictive Maintenance
8. • Data analysis and machine learning:
We analyze sensor data and utilize computers to identify trends
or signals that anything is wrong with the devices. Computers can learn
from data to become better at predicting issues.
• Predictive modeling:
This is when we use the data to predict when problems may
occur in the future. By anticipating when something may fail, we can
remedy it before it becomes a major issue [3].
Slide no. 6
Key Components of Predictive Maintenance
10. • Internet of Things (IoT)
• Machine Learning and Artificial Intelligence
• Data Analytics
• Cloud Computing [3]
Slide no. 8
Technologies Involved:
11. • Data collection and preprocessing
• Sensor deployment
• Integration with existing systems
• Training predictive models [3]
Slide no. 9
Implementation Process:
12. • Data quality and availability
• Initial implementation costs
• Workforce training
• Overcoming resistance to change [3]
Slide no. 10
Challenges
13. • Regular equipment health checks
• Continuous monitoring and analysis
• Collaboration between operations and maintenance teams
• Scalability considerations [3]
Slide no. 11
Best Practices
14. • Edge computing in predictive maintenance:
Using small computers set up directly on equipment to assess
data in real-time, assisting in predicting when maintenance is required
without sending data to a central server.
• Integration with Industry 4.0:
Connecting predictive maintenance systems to other modern
technologies like automation, IoT (Internet of Things), and data
analytics to develop smarter, more efficient production processes [4].
Slide no. 12
Future Trends:
15. • Enhanced AI capabilities:
Making predictive maintenance systems even smarter by
enhancing artificial intelligence (AI) algorithms, which allow them to
better analyze data and predict equipment faults with more accuracy.
• Predictive maintenance for different industries:
Predictive maintenance approaches are being used in industries
other than manufacturing, such as transportation, healthcare, and
energy, to prevent breakdowns and enhance reliability in numerous
types of machinery and equipment [4].
Slide no. 12
Future Trends:
16. In short, predictive maintenance represents a significant shift in how
we approach equipment maintenance and management.
Organizations can handle maintenance needs proactively by employing
data gathering, condition monitoring, complex analytics, and
predictive modeling before they result in costly downtime or
breakdowns.
Slide no. 13
Conclusion
17. [1]R. K. L. R. H. D. J. W. Mobley, in Maintenance Engineering Hadbook,
McGraw-Hill.
[2]https://toolsense.io/maintenance/the-6-types-of-maintenance-
definitions-benefits-examples
[3] https://www.techtarget.com/searcherp/feature/Predictive-
maintenance-Definition-benefits-example-strategy
[4] https://knowhow.distrelec.com/internet-of-things/top-5-emerging-
trends-in-predictive-maintenance
Slide no. 14
Reference
