Hidden Semi-Markov Models for Predictive Maintenance

<table class="figure-group"><tr class="fig-image" id="a"><td><object data="https://static.hindawi.com/articles/mpe/volume-2015/278120/figures/278120.fig.0010a.svgz" name="278120.fig.0010a" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(a) </b>RUL estimation for Bearing1_7</td></tr><tr class="fig-image" id="b"><td><object data="https://static.hindawi.com/articles/mpe/volume-2015/278120/figures/278120.fig.0010b.svgz" name="278120.fig.0010b" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(b) </b>RUL estimation for Bearing2_6</td></tr></table>

<div>By obtaining a low average absolute prediction error, the proposed parametric HSMM is effective for estimating the Remaining Useful Lifetime of bearings.</div>

Mathematical Problems in Engineering

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Figure 10

Figure 10: Hidden Semi-Markov Models for Predictive Maintenance 