麻豆传媒AV

Course structure

This course is delivered through a balanced combination of lectures and practical sessions. All delegates will receive a Certificate of Attendance upon completion of this course.

What you will learn

On successful completion of this course you will be able to:

• Identify and recognise the asset management best practices and advanced maintenance strategies for engineering systems in different industries,
• Analyse key and fundamental aspects of system’s life-cycle and understand the financial implications involved with assessing the maintenance and risk factors,
• Differentiate between classical maintenance strategies (run-to-failure, time-based) and novel maintenance strategies (e.g. risk/reliability centred maintenance, predictive and diagnostic maintenance, predictive maintenance) and evaluate their main advantages and limitations,
• Determine the concept and utilise applications of Monte-Carlo Simulation (MSC), Bayesian Network (BN) in system reliability and availability assessment,
• Evaluate the capabilities and limitations of robotic and autonomous maintenance systems, and outline the future trends and impacts on the maintenance strategy,
• Design an appropriate maintenance strategy for complex engineering systems, detailing how the strategy is embedded throughout the asset life-cycle.

Core content

• Introduction: Asset management, overall equipment effectiveness (OEE), asset productivity,
• Asset integrity: Asset integrity management (AIM), Risk-based integrity, through-life engineering,
• Maintenance engineering: Maintenance regimes, reactive vs. proactive maintenance; Age and block maintenance, reliability-centred maintenance (RCM), risk-based maintenance (RBM), total productive maintenance (TPM), world-class maintenance (WCMain),
• Fault diagnosis and prognosis: Fault detection and failure location; root-cause analysis (RCA), Common-cause analysis (CCA), Condition-based maintenance (CBM), predictive maintenance (PdM), prognostics,
• Maintenance modelling, planning, scheduling, and optimization,
• Reliability data analysis: types and sources of reliability data, data collection, data cleansing, data accuracy and precision, model fitting, big-data, incomplete data, redundant data, not-detailed data,
• Applications of Monte-Carlo Simulation (MCS) and Bayesian Network (BN) in system reliability and availability assessment,
• Probability of failure, Cost of failure, and risk of failure in networked infrastructures,
• System’s life-cycle: Life-cycle cost (LCC) analysis, whole-life costing, how to identify cost drivers of system operation,
• Robotic and autonomous maintenance; overview of the capabilities and limitations of commercially available aerial and underwater remote and autonomous systems, and how these systems are integrated in the overall maintenance strategy,
• Reliability of condition monitoring technologies and sensors, Probability of Detection (POD) and Probability of Sizing (POS),
• Decommissioning vs. life extension,
• Warranty and service contracts analysis: guarantees, warranties, extended warranties, service contracts, and maintenance outsourcing with several examples from different industries,
• Workshops and case studies: Work in groups to analyse the reliability, availability and maintainability of various offshore systems and components.

Upgrade to a professional qualification

Cranfield credits are available for this short course which you can put towards selected Cranfield degrees. Find out more about short course credit points.

Who should attend

The course will benefit mechanical, structural, pipeline, industrial engineers and technicians who are or plan to be involved in reliability engineering and risk management of industrial assets.

Speakers

Dr Mahmood Shafiee - Senior Lecturer in Risk and Reliability Engineering

Concessions

10% discount applies if booked 8 weeks in advance. 10% discount for 3rd and subsequent delegates from the same company/site. Discounts can be combined.

Accommodation options and prices

This is a non-residential course. If you would like to book accommodation on campus, please contact Mitchell Hall or Cranfield Management Development Centre directly. Further information regarding our accommodation on campus can be .

Alternatively you may wish to make your own arrangements at a nearby hotel.

Location and travel

麻豆传媒AV is situated in Bedfordshire close to the border with Buckinghamshire. The University is located almost midway between the towns of Bedford and Milton Keynes and is conveniently situated between junctions 13 and 14 of the M1.

London Luton, Stansted and Heathrow airports are 30, 90 and 90 minutes respectively by car, offering superb connections to and from just about anywhere in the world. 

For further location and travel details

Location address

麻豆传媒AV
College Road
Cranfield
Bedford 
MK43 0AL