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.004a.svgz" name="278120.fig.004a" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(a) </b>State estimation with Viterbi path for continuous data and Gamma duration distribution</td></tr><tr class="fig-image" id="b"><td><object data="https://static.hindawi.com/articles/mpe/volume-2015/278120/figures/278120.fig.004b.svgz" name="278120.fig.004b" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(b) </b>State estimation with Viterbi path for discrete data and Gaussian duration distribution</td></tr></table>

<div>Condition monitoring using the Viterbi path. HSMMs can be effective to solve condition monitoring problems in time-dependent applications due to their high accuracy in hidden state recognition.</div>

Mathematical Problems in Engineering

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Figure 4: Hidden Semi-Markov Models for Predictive Maintenance 