This work focuses on humidity effects of turbofan engines, in order to identify the magnitude of the error in operational conditions and the implications on maintenance decision support. More specifically, this paper employs a set of different methods, including semi-empirical corrections used in engine test beds, performance simulation models and analysis of historical data, in order to investigate the effects of humidity. We show that varying humidity can have a noticeable influence on the performance of the engine. These discrepancies cannot be currently quantified by health monitoring systems. Simulation and test bed correlations indicate a decrease of EGT of 0.35% per 1wt% of absolute humidity, which varies worldwide between 0 and 3wt%. Consequently, deviations in EGTM can be up to 1%, a figure which can be up to 12K for a modern civil turbofan. In practice, variations in ambient humidity have the potential to conceal possible deterioration in engine components. Following, the flight historical data were corresponded to historical humidity data. The two methods were identified to provide comparable results, indicating a higher EGTM for increasing ambient humidity. Overall, it was concluded that EGTM corrections for ambient humidity is an area of significant interest, especially for newer engine types where accurate diagnostics are of increasing importance.
Ambient intelligence technologies are a means to support ageing-in-place by monitoring clients in the home. In this study, monitoring is applied for the purpose of raising an alarm in an emergency situation, and thereby, providing an increased sense of safety and security. Apart from these technological solutions, there are numerous environmental interventions in the home environment that can support people to age-in-place. The aim of this study was to investigate the needs and motives, related to ageing-in-place, of the respondents receiving ambient intelligence technologies, and to investigate whether, and how, these technologies contributed to aspects of ageing-in-place. This paper presents the results of a qualitative study comprised of interviews and observations of technology and environmental interventions in the home environment among 18 community-dwelling older adults with a complex demand for care.
Ambient activity monitoring systems produce large amounts of data, which can be used for health monitoring. The problem is that patterns in this data reflecting health status are not identified yet. In this paper the possibility is explored of predicting the functional health status (the motor score of AMPS = Assessment of Motor and Process Skills) of a person from data of binary ambient sensors. Data is collected of five independently living elderly people. Based on expert knowledge, features are extracted from the sensor data and several subsets are selected. We use standard linear regression and Gaussian processes for mapping the features to the functional status and predict the status of a test person using a leave-oneperson-out cross validation. The results show that Gaussian processes perform better than the linear regression model, and that both models perform better with the basic feature set than with location or transition based features. Some suggestions are provided for better feature extraction and selection for the purpose of health monitoring. These results indicate that automated functional health assessment is possible, but some challenges lie ahead. The most important challenge is eliciting expert knowledge and translating that into quantifiable features.
