Fault Diagnosis Method Based on Gap Metric Data Preprocessing and Principal Component Analysis

<table class="figure-group"><tr class="fig-image" id="a"><td><object data="https://static.hindawi.com/articles/jcse/volume-2018/1025353/figures/1025353.fig.006a.svgz" name="1025353.fig.006a" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(a) </b>Fault detection results of InEnPCA</td></tr><tr class="fig-image" id="b"><td><object data="https://static.hindawi.com/articles/jcse/volume-2018/1025353/figures/1025353.fig.006b.svgz" name="1025353.fig.006b" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(b) </b>Contribution plot of InEnPCA</td></tr></table>

<div>InEnPCA with microfaults detection in 801~1000 samples.</div>

Journal of Control Science and Engineering

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Figure 6

Figure 6: Fault Diagnosis Method Based on Gap Metric Data Preprocessing and Principal Component Analysis 