Sensor Selection and Optimization for Health Assessment of Aerospace Systems

Aerospace systems are developed similarly to other large-scale systems through a series of reviews, where designs are modified as system requirements are refined. For space-based systems few are built and placed into service. These research vehicles have limited historical experience to draw from and formidable reliability and safety requirements, due to the remote and severe environment of space. Aeronautical systems have similar reliability and safety requirements, and while these systems may have historical information to access, commercial and military systems require longevity under a range of operational conditions and applied loads. Historically, the design of aerospace systems, particularly the selection of sensors, is based on the requirements for control and performance rather than on health assessment needs. Furthermore, the safety and reliability requirements are met through sensor suite augmentation in an ad hoc, heuristic manner, rather than any systematic approach. A review of the current sensor selection practice within and outside of the aerospace community was conducted and a sensor selection architecture is proposed that will provide a justifiable, dependable sensor suite to address system health assessment requirements

Variable predictive models-A new multivariate classification approach for pattern recognition applications

Pattern Recognition4217-16PTNR

Locating sensors in complex chemical plants based on fault diagnostic observability criteria

Fault diagnosis is an important task for the safe ann optimal operation of chemical processes. Hence, this area has attracted considerable attention from researchers in the past few years. A variety of approaches have been proposed for solving this problem. All approaches for fault detection and diagnosis in some sense involve the comparison of the observed behavior of the process to a reference model. The process behavior is inferred using sensors measuring the important variables in the process. Hence, the efficiency of the diagnostic approach depends critically on the location of sensors monitoring the process variables. The emphasis of most of the work on fault diagnosis has been more on procedures to perform diagnosis given a set of sensors and less on the actual location of sensors for efficient identification of faults. A diagraph-based approach is proposed for the problem of sensor location for identification of faults. Various graph algorithms that use the developed digraph in deciding the location of sensors based on the concepts of observability and resolution are discussed Simple examples are provided to explain the algorithms, and a complex FCCU case study is also discussed to underscore the utility of the algorithm for large flow sheets. The significance and scope of rite proposed algorithms are highlighted

Variable Interaction Structure Based Machine Learning Technique for Cancer Tumor Classification

10.1007/978-3-540-92841-6_475IFMBE Proceedings231915-191

Effects of submerged tropical macrophytes on flow resistance and velocity profiles in open channels

International Journal of River Basin Management93-4195-20

Influence of viscosity on product distribution of fast competitive chemical reactions

The objective of this study was to determine the influence of viscosity on micromixing in turbulent flow. It was first necessary to find a suitable viscosity-raising additive. HEC (hydroxyethyl cellulose) proved to be better than previously studied additives [sorbitol and carboxymethylcellulose (CMC)]. In concentrations up to 1 wt-%, HEC solutions are almost Newtonian with viscosities independent of pH over the range 2 to 10. HEC had no effect on the reaction rate constants and the spectrophotometric analysis of the fast, competing reactions used – the diazo coupling between 1-naphthol and diazotized sulphanilic acid. The viscosity can then be raised by around an order of magnitude by adding less than 1 wt-% HEC to this reaction system. Diazo couplings were conducted in a 20 1 semi-batch tank reactor stirred by a Rushton turbine at two viscosity levels (0.9 and 6.2 mPa s). Long feed times ensured that micromixing was controlling. More bisazo dye was formed in the more viscous solution, all other conditions being unchanged, indicating more intense segregation and slower micromixing. This was also shown by visualizing the extent of neutralisation zones, with more spreading and slower micromixing being observed in viscous solution. Higher turbine speeds reduced this spreading. One feed point near and one far from the turbine were employed: the strong inhomogeneity of the turbulence led to smaller amounts of bisazo dye when the feed was added to the turbine suction, irrespective of the viscosity. All results agreed with the trends predicted by the engulfment model of micromixing. Its simplest form assigns an average energy dissipation rate to the reaction zone: the values obtained are of similar magnitude to those measured by physical techniques and were related to the spreading of the reaction zone