Application of a Generic Curriculum Change Management Process to Motivate and Excite Students

The School of Mechanical and Aerospace Engineering at Queen’s University Belfast is committed to enhancing the quality of student learning. A plan to implement curriculum change around this goal has been formulated and is already several years underway. A specific part of the plan involved instigating a first year introductory module to engage the students in the practice of their engineering discipline. The complicated nature of devising this type of module with regard to objectives, resources, timeframe and the number of students involved meant that a very systematic approach had to be adopted. This paper presents the simple but definitive change management process that facilitated in the creation of a first year Introduction to Engineering module. The generic nature of this process is described and its application to other facets of curriculum change is discussed. Within this process the importance of collaboration to establish a forward momentum is emphasised. This enables academic staff to progress as a group and build curriculum development based on their own experiences, expertise and established practice

Application of auto-associative neural networks to transient fault detection in an IC engine

The tailpipe emissions from automotive engines have been subject to steadily reducing legislative limits. This reduction has been achieved through the addition of sub-systems to the basic four-stroke engine which thereby increases its complexity. To ensure the entire system functions correctly, each system and / or sub-systems needs to be continuously monitored for the presence of any faults or malfunctions. This is a requirement detailed within the On-Board Diagnostic (OBD) legislation. To date, a physical model approach has been adopted by me automotive industry for the monitoring requirement of OBD legislation. However, this approach has restrictions from the available knowledge base and computational load required. A neural network technique incorporating Multivariant Statistical Process Control (MSPC) has been proposed as an alternative method of building interrelationships between the measured variables and monitoring the correct operation of the engine. Building upon earlier work for steady state fault detection, this paper details the use of non-linear models based on an Auto-associate Neural Network (ANN) for fault detection under transient engine operation. The theory and use of the technique is shown in this paper with the application to the detection of air leaks within the inlet manifold system of a modern gasoline engine whilst operated on a pseudo-drive cycle. Copyright © 2007 by ASME.