Course Description

This practical and highly-interactive course includes practical sessions and exercises. Theory learnt will be applied using our state-of-the-art simulators. Modern process industries are seeking to maximize the value of their existing assets by leveraging new technologies to optimize operations and maintenance activities. One of the most successful maintenance strategies is a conditioned-based approach which utilizes data collected from periodic inspections, testing and predictive maintenance technologies to determine the optimum maintenance requirements. Contrary to the traditional time-based maintenance approach, Condition Based Maintenance (CBM) is a process, which utilizes monitoring and diagnostic data to drive the maintenance decision process. Condition Based Monitoring (CBM) of power plants can help reduce downtime, increase the safety of plant operations and provide an accurate indicator of impending faults. This can lead to better planned maintenance shutdowns, the avoidance of unplanned shutdowns and a reduction in cost.Almost any equipment, be it electrical, hydraulic, mechanical, or thermal, generates characteristic signals or signature during optimal performance. A change in this signal, even if marginal, could be an early warning regarding potential equipment failure. The practice of condition-based monitoring and maintenance can be an invaluable tool in improving maintenance efficiency, safety and equipment use. With the proper skills and equipment, plant maintenance technicians not only detect problems before they result in a major machine malfunction or breakdown, but they also perform root cause failure analysis to prevent problems from recurring. Highly trained condition monitoring technicians can have a significant impact on a plant?s bottom line profitability. This course is designed to provide an insight into Condition Based Monitoring (CBM). It will cover the various methods of maintenance and it will give the participant an introduction to the techniques utilized in Condition Based Monitoring such as Noise & Vibration Measurement, Infrared Thermography, Oil Debris Analysis, Laser Alignment and Balancing. Following this course participants will understand the place of condition monitoring in the maintenance process and will appreciate the implications for maintenance cost saving and improved machine reliability. They will be able to assess plant for the most appropriate monitoring parameter, will learn of the various specialist instruments and methods, be able to plan a monitoring programme and set up measurement rounds. The course will introduce participants to the dynamic behaviour of machines and discuss appropriate fault detection and diagnostic criteria and schemes for various applications. It will address the more popular techniques which employ dynamic data analysis, including vibration and acoustic emission signals for the recognition of early life failures in machines. Emphasis will be placed on the practical application of tools to identify a wide range of mechanical, electrical and lubrication flaws in machinery and an objective approach to the optimum choice of analysis procedure. link to course overview PDF

TRAINING METHODOLOGY

This interactive training course includes the following training methodologies as a percentage of the total tuition hours

30 % Lectures
20 % Workshops & Work Presentations
20 % Case Studies & Practical Exercises
30 % Videos, Software & Simulators

In an unlikely event, the course instructor may modify the above training methodology before or during the course for technical reasons.

VIRTUAL TRAINING (IF APPLICABLE)

If this course is delivered online as a Virtual Training, the following limitations will be applicable

Certificates	:	Only soft copy certificates will be issued to participants through Haward’s Portal. This includes Wallet Card Certificates if applicable
Training Materials	:	Only soft copy Training Materials (PDF format) will be issued to participant through the Virtual Training Platform
Training Methodology	:	80% of the program will be theory and 20% will be practical sessions, exercises, case studies, simulators or videos
Training Program	:	The training will be for 4 hours per day starting at 09:30 and ending at 13:30
H-STK Smart Training Kit	:	Not Applicable
Hands-on Practical Workshops	:	Not Applicable
Site Visit	:	Not Applicable
Simulators	:	Only software simulators will be used in the virtual courses. Hardware simulators are not applicable and will not be used in Virtual Training