Online Health Management for Complex Nonlinear Systems Based on Hidden Semi-Markov Model Using Sequential Monte Carlo Methods

<table class="table-group" id="tab4"><tr><td><table class="table"><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr class="thead"><td align="left">Actual RUL</td><td align="center">Computed RUL</td><td align="center">Relative error (%)</td></tr><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr><td align="left">32.0000</td><td align="center">30.3521</td><td align="center">5.1498</td></tr><tr><td align="left">31.0000</td><td align="center">30.0746</td><td align="center">2.9851</td></tr><tr><td align="left">30.0000</td><td align="center">29.3980</td><td align="center">2.0067</td></tr><tr><td align="left">29.0000</td><td align="center">29.2262</td><td align="center">0.7801</td></tr><tr><td align="left">28.0000</td><td align="center">27.7989</td><td align="center">0.7181</td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

Prognostic and actual RUL (5 observation time points).

Mathematical Problems in Engineering

tab4

Table 4

Table 4: Online Health Management for Complex Nonlinear Systems Based on Hidden Semi-Markov Model Using Sequential Monte Carlo Methods 