Behavioral influences of proximal alarms

Confidence in and responses to an unreliable test alarm were studied in the presence of nearby unspecified alarms. The test alarm's reliability rate was represented as averaging "true" only 50% or 60% of the time. Confidence or response rates ranged proportionately from 23% to 97% with the number of active alarms within 5, 6, 7, or 9 annunciator arrays. Adjacent alarms resulted in confidence estimates that were higher (by about 10%) than those with the same number of active alarms spaced up to 3 positions away. Simultaneously activated alarms resulted in a more than 20% increase in "true" responses compared with the same number of alarms offset in time by up to 32 s, regardless of which came first. Active alarms "known" to be functionally related to, or independent of, the test alarm substantially raised or lowered responding but did not completely overcome prior effects. These findings indicate that presumptions that operators' responses are not influenced by nearby alarms, regardless of their function, may be unwarranted. Applications of this research include suggestions to improve responding and training recommendations.     

A Semiautomatic Approach to Deriving Turbine Generator Diagnostic Knowledge

Condition monitoring of turbine generators, housed at British Energy nuclear power stations throughout the U.K., is implemented to diagnose incipient faults at an early stage, so corrective action can be taken to avoid the associated high costs of an unplanned shutdown. A prototype expert system has been developed that provides decision support to condition monitoring experts who monitor British Energy turbine generators. The expert system automatically interprets data from strategically positioned sensors and transducers on the turbine generator by applying expert knowledge in the form of heuristic rules. This paper reviews the application domain and describes the work undertaken in developing the prototype expert system. The paper also outlines a learning module design that uses an approach based on an analytical symbolic machine learning technique, explanation-based generalization, to semiautomatically derive heuristic rules for turbine generator fault diagnosis. The approach adopted by the learning module is explained in detail and a worked example demonstrates how the learning module can derive a fault heuristic from a single training example. The modular approach to capturing the causal fault and behavioral models is described, and the method in which the module will be integrated with the existing expert system has been outlined. A preliminary evaluation of the learning module design is discussed.     

Optimal traffic data archive scheme

An optimal traffic data archive scheme where the maximum information of the original data can be preserved using less storage space has been described. Compared with traditional equal-width methods where compact data are obtained by aggregating source data at fixed intervals, the optimal scheme uses varying intervals to aggregate data at different levels based on the variations within the source data. The resultant scheme is optimal in terms of information conservation, that is, the errors between the source data and the optimal compact data are the smallest. Operational traffic data have been used to test three proposed optimisation schemes: single-variable, multi-variable and heuristic schemes. It was found that, compared with traditional equal-width schemes, the size of the archived data can be reduced by six times if the single-variable optimisation scheme, or by three times if the multi-variable optimisation scheme is employed. The heuristic scheme using a combination of single-variable and multi-variable optimisations can then reduce storage space by three to six times     

Capacity estimation of a minihydro plant based on time series forecasting

A procedure to estimate the capacity of minihydro plants based on water flow time series forecasting is presented. First, the classic method used for this purpose is introduced and then a set of methodologies to assess the feasibility of minihydro generation and to determine system capacity based on time series forecast is described. The water flow time series is processed to determine the theoretical power generation and to assess if a minihydro plant can be installed. Finally the characteristics of each of the electro-mechanical components for the proposed minihydro plant are selected.     

Writing in Engineering Courses

By incorporating writing in engineering courses, we can move toward several important educational goals. Writing allows students to develop and use critical thinking skills. It enhances active learning and addresses the needs of students with different learning styles. It is a uniquely powerful tool for assessing student understanding. Writing becomes particularly useful in engineering education when demonstrated as a process. Similarities between the writing and design processes can be used to highlight the fact that there is often no single “correct” solution in either and that feedback and revision are often crucial to both. Finally, the status of our profession is enhanced when engineering graduates can communicate effectively. This paper provides specific examples of how we have employed writing in our courses over the last three years and how it has served to enhance outcomes in these courses.