Sensor dynamics in high dimensional phase spaces via nonlinear transformations: Application to helicopter loads monitoring

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Proceedings titleIEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings
Conference5th IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2014, 9 December 2014 through 12 December 2014
Article number7008691
Pages365372; # of pages: 8
SubjectAircraft control; Data mining; Delay control systems; Genetic algorithms; Helicopter services; Helicopters; Mathematical transformations; Phase space methods; Time delay; Computational intelligence methods; High dimensional phase space; Machine learning methods; Maintenance schedules; Measurement methods; Non-linear transformations; Nonlinear mappings; Quantification analysis; Flight control systems
AbstractAccurately determining component loads on a helicopter is an important goal in the helicopter structural integrity field, with repercussions on safety, component damage, maintenance schedules and other operations. Measuring dynamic component loads directly is possible, but these measurement methods are costly and are difficult to maintain. While the ultimate goal is to estimate the loads from flight state and control system parameters available in most helicopters, a necessary step is understanding the behavior of the loads under different flight conditions. This paper explores the behavior of the main rotor normal bending loads in level flight, steady turn and rolling pullout flight conditions, as well as the potential of computational intelligence methods in understanding the dynamics. Time delay methods, residual variance analysis (gamma test) using genetic algorithms, unsupervised non-linear mapping and recurrence plot and quantification analysis were used. The results from this initial work demonstrate that there are important differences in the load behavior of the main rotor blade under the different flight conditions which must be taken into account when working with machine learning methods for developing prediction models.
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AffiliationNational Research Council Canada; Information and Communication Technologies; Aerospace
Peer reviewedYes
NPARC number21275822
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Record identifier4a05eb86-7321-4b1f-b307-a562f2cde208
Record created2015-07-14
Record modified2016-05-09
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