COURSE OVERVIEW:

In the pursuit of zero downtime, traditional time based maintenance is often insufficient and inefficient. This course focuses on the principles and practical application of Condition Based Maintenance (CBM) and Predictive Maintenance (PdM), techniques that allow maintenance teams to intervene only when equipment health actually declines. It provides a technical deep dive into how various monitoring technologies can detect early warning signs of failure, allowing for planned repairs before catastrophic breakdown occurs.

The scope of this training includes a comprehensive review of the "P-F Interval" and how to select the right monitoring technology for specific failure modes. Participants will learn how to build a PdM program that integrates multiple diagnostic tools, such as vibration analysis, oil analysis, thermography, and ultrasound. The course emphasizes the shift from "fixing things that are broken" to "predicting and preventing" the root causes of failure, thereby extending asset life and reducing maintenance costs.

Coverage includes the selection of critical assets for monitoring, the setting of alarm thresholds, and the interpretation of diagnostic data. Attendees will also explore the role of "Internet of Things" (IoT) sensors and "Machine Learning" in automating the detection of anomalies. By the end of this course, participants will be able to design and manage a world class predictive maintenance program that significantly enhances the reliability and availability of their plant's critical infrastructure.

COURSE OBJECTIVES:

After completion of this course, the participants will be able to:

• Explain the concepts of the P-F Curve and the window of detection.
• Select appropriate PdM technologies based on specific failure modes.
• Design a PdM program for critical rotating and static equipment.
• Interpret basic vibration spectra to identify common machinery faults.
• Utilize Infrared Thermography to detect electrical and mechanical hot spots.
• Apply Ultrasound technology for leak detection and bearing monitoring.
• Analyze Oil and Wear Debris data to assess internal machine health.
• Set effective alarm limits and alert thresholds for condition data.
• Integrate PdM findings into the CMMS work management workflow.
• Calculate the Return on Investment (ROI) of predictive maintenance.
• Utilize IIoT sensors for real time, continuous condition monitoring.
• Develop a multi technology approach to complex fault diagnosis.

TARGET AUDIENCE:

This course is designed for Reliability Engineers, Condition Monitoring Technicians, Maintenance Managers, Asset Managers, and Mechanical/Electrical Engineers.

TRAINING COURSE METHODOLOGY:

A highly interactive combination of lectures, discussion sessions, and case studies will be employed to maximize the transfer of information, knowledge, and experience. The course will be intensive, practical, and highly interactive. The sessions will start by raising the most relevant questions and motivating everybody to find the right answers. The attendants will also be encouraged to raise more of their questions and to share in developing the right answers using their analysis and experience. There will also be some indoor experiential activities to enhance the learning experience. Course material will be provided in PowerPoint, with necessary animations, learning videos, and general discussions.

The course participants shall be evaluated before, during, and at the end of the course.

COURSE CERTIFICATE:

National Consultant Centre for Training LLC (NCC) will issue an Attendance Certificate to all participants completing a minimum of 80% of the total attendance time requirement.