As a prerequisite for effective prognostics, the goodness of the features affects the complexity of the
prognostic methods. Comparing to features quality evaluation in diagnostics, features evaluation for prognostics
is a new problem. Normally, the monotonic tendency of feature series can be used as the visual representation
of equipment damage cumulation so that forecasting its future health states is easy to implement. Through
introducing the concept of ranking mutual information in ordinal case, a monotonicity evaluation method of
monitoring feature series is proposed. Finally, this method is verified by the simulating feature series and the
results verify its effectivity. For the specific application in industry, the evaluation results can be used as the
standard for selecting prognostic feature.
ZHAO Chun-yu1* (赵春宇), LIU Jing-jiang1 (刘景江), MA Lun2 (马伦), ZHANG Wei-jun3 (张伟君)
. Monotonicity Evaluation Method of Monitoring Feature Series Based on Ranking Mutual Information[J]. Journal of Shanghai Jiaotong University(Science), 2015
, 20(3)
: 380
-384
.
DOI: 10.1007/s12204-015-1641-8
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