Simultaneous-Fault Diagnosis of Automotive Engine Ignition Systems Using Prior Domain Knowledge and Relevance Vector Machine

<div class="floats-full-page floats-full-page-other"><h2>Table 8</h2><div class="floats-full-page-caption">Single-fault diagnosis under <svg height="11.175" id="M371" style="vertical-align:-0.0pt" version="1.1" viewbox="0 0 10.7 11.175" width="10.7" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
<g transform="matrix(.017,-0,0,-.017,.062,11.113)"><path d="M599 650l-18 -160l-30 -2q-2 70 -11 94q-5 16 -26 24t-75 8h-94q-27 0 -33.5 -6.5t-12.5 -32.5l-43 -226h108q52 0 72.5 5t31 18t28.5 52h28l-40 -200h-28q-1 42 -6 56.5t-24.5 21t-70.5 6.5h-108l-32 -177q-13 -65 1 -80.5t88 -22.5l-8 -28h-279l6 28q61 5 77.5 20.5&#10;t29.5 81.5l72 387l7.5 40.5t1 26.5t-4 18t-15.5 10t-24 6t-37 4l8 28h461z" id="x1D439"></path></g>
</svg>-measure (with DK+WPT+PCA, <svg height="14.675" id="M372" style="vertical-align:-5.56921pt" version="1.1" viewbox="0 0 23.387501 14.675" width="23.387501" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
<g transform="matrix(.017,-0,0,-.017,.062,7.675)"><path d="M387 375q0 -16 -17 -31t-29 -15q-9 0 -12 13q-13 74 -80 74q-36 0 -61 -24.5t-25 -56.5t24 -51.5t68 -19.5q33 0 47 2l2 -7l-32 -42q-20 2 -54 2q-45 0 -74 -21.5t-29 -60.5q0 -41 28 -65.5t73 -24.5q78 0 145 67l17 -23q-33 -47 -84.5 -75t-111.5 -28q-70 0 -114.5 35.5&#10;t-44.5 92.5q0 46 38 78t95 45v2q-35 10 -54.5 33t-19.5 52q0 55 53 88.5t122 33.5q67 0 98.5 -23.5t31.5 -49.5z" id="x1D700"></path></g>
<g transform="matrix(.012,-0,0,-.012,7.037,11.762)"><path d="M257 449q92 0 154 -65t62 -158q0 -112 -67 -175t-150 -63q-98 0 -158.5 66.5t-60.5 154.5q0 59 21 106.5t54.5 75.5t71 43t73.5 15zM244 416q-48 0 -81 -47t-33 -128q0 -96 38 -158t99 -62q51 0 82 43.5t31 139.5q0 91 -36 151.5t-100 60.5z" id="x6F"></path></g><g transform="matrix(.012,-0,0,-.012,12.975,11.762)"><path d="M169 380l92 53q26 16 49 16q80 0 128.5 -56t48.5 -133q0 -108 -72.5 -180.5t-176.5 -91.5q-38 1 -69 18v-131q0 -57 13 -70t71 -18v-27h-236v27q49 5 61 17t12 61v452q0 48 -9.5 58.5t-56.5 16.5v24q66 11 145 43v-79zM169 346v-270q37 -39 94 -39q63 0 101.5 50&#10;t38.5 134q0 78 -38.5 123t-95.5 45q-45 0 -100 -43z" id="x70"></path></g><g transform="matrix(.012,-0,0,-.012,19.079,11.762)"><path d="M298 36l-68 -35q-25 -13 -42 -13q-98 0 -98 119v290h-69l-4 11l28 29h45v72l65 67l14 -2v-137h113q8 -8 6 -21t-12 -19h-107v-256q0 -96 59 -96q29 0 61 17z" id="x74"></path></g>
</svg> = 0.7147).</div><div class="floats-full-page-content"><table class="table-group" id="tab8"><tr><td><table class="table"><tr><td class="thead-hr" colspan="4"><hr /></td></tr><tr class="thead"><td align="left">Test dataset <svg height="15.7" id="M373" style="vertical-align:-3.27605pt" version="1.1" viewbox="0 0 48 15.7" width="48" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
<g transform="matrix(.017,-0,0,-.017,.062,11.55)"><path d="M649 676l-22 -187l-33 -2q3 56 -12 94q-8 20 -31.5 26.5t-86.5 6.5h-74l-90 -491q-4 -23 -6 -36.5t1 -25t7 -16.5t18 -9t27 -5t41 -3l-6 -28h-286l4 28q68 5 84 18.5t28 76.5l94 491h-55q-74 0 -100.5 -6t-41.5 -23q-24 -29 -54 -98l-32 1q32 98 53 188h22q7 -18 15 -22&#10;t37 -4h417q23 0 33.5 5t25.5 21h23z" id="x1D447"></path></g><g transform="matrix(.017,-0,0,-.017,11.384,11.55)"><path d="M609 650l-19 -162l-30 -2q2 69 -14 94q-9 18 -28.5 26t-74.5 8h-88q-30 0 -37 -6.5t-12 -36.5l-41 -212h103q48 0 69 5.5t32 19t26 50.5h29l-40 -198h-30q2 58 -11.5 70.5t-85.5 12.5h-99l-30 -167q-17 -87 3 -101q18 -15 111 -15q59 0 89 7t54 27q8 7 15.5 16t16 21.5&#10;l14 20.5t15 23.5t13.5 21.5l29 -10q-52 -129 -71 -163h-500l6 28q66 4 83 17.5t28 73.5l77 409q11 61 -0.5 75.5t-78.5 18.5l10 28h467z" id="x1D438"></path></g><g transform="matrix(.017,-0,0,-.017,22.127,11.55)"><path d="M457 488l-30 -3q-17 148 -131 148q-53 0 -84.5 -34.5t-31.5 -82.5q0 -42 25.5 -72t74.5 -62l33 -22q63 -42 95 -85t32 -102q0 -84 -67 -137t-163 -53q-58 0 -113 22t-70 43l-4 152l27 4q4 -32 15 -62.5t31 -59.5t53.5 -47t76.5 -18q56 0 92 35t36 96q0 39 -25 70t-78 68&#10;l-31 22q-32 23 -53.5 41.5t-45 57t-23.5 77.5q0 82 58 132.5t156 50.5q46 0 101 -17l18.5 -6t17 -6t8.5 -3q-4 -55 0 -147z" id="x1D446"></path></g><g transform="matrix(.017,-0,0,-.017,30.286,11.55)"><path d="M649 676l-22 -187l-33 -2q3 56 -12 94q-8 20 -31.5 26.5t-86.5 6.5h-74l-90 -491q-4 -23 -6 -36.5t1 -25t7 -16.5t18 -9t27 -5t41 -3l-6 -28h-286l4 28q68 5 84 18.5t28 76.5l94 491h-55q-74 0 -100.5 -6t-41.5 -23q-24 -29 -54 -98l-32 1q32 98 53 188h22q7 -18 15 -22&#10;t37 -4h417q23 0 33.5 5t25.5 21h23z" id="x1D447"></path></g>
<g transform="matrix(.012,-0,0,-.012,41.612,15.637)"><path d="M384 0h-275v27q67 5 81.5 18.5t14.5 68.5v385q0 38 -7.5 47.5t-40.5 10.5l-48 2v24q85 15 178 52v-521q0 -55 14.5 -68.5t82.5 -18.5v-27z" id="x31"></path></g>
</svg> <br />(single faults)</td><td align="center">PNN</td><td align="center">RVM</td><td align="center">PCRVM</td></tr><tr><td class="thead-hr" colspan="4"><hr /></td></tr><tr><td align="left">Fault 1</td><td align="center">0.9185</td><td align="center">0.9342</td><td align="center">0.9781</td></tr><tr><td align="left">Fault 2</td><td align="center">0.9094</td><td align="center">0.9118</td><td align="center">0.9224</td></tr><tr><td align="left">Fault 3</td><td align="center">0.9773</td><td align="center">0.9357</td><td align="center">0.9911</td></tr><tr><td align="left">Fault 4</td><td align="center">0.9087</td><td align="center">0.9272</td><td align="center">0.9287</td></tr><tr><td align="left">Fault 5</td><td align="center">0.9368</td><td align="center">0.9458</td><td align="center">0.9489</td></tr><tr><td align="left">Fault 6</td><td align="center">0.8902</td><td align="center">0.9010</td><td align="center">0.9020</td></tr><tr><td align="left">Fault 7</td><td align="center">0.9722</td><td align="center">0.9812</td><td align="center">0.9923</td></tr><tr><td align="left">Fault 8</td><td align="center">0.9453</td><td align="center">0.9477</td><td align="center">0.9513</td></tr><tr><td align="left">Fault 9</td><td align="center">0.9042</td><td align="center">0.9126</td><td align="center">0.9242</td></tr><tr><td align="left">Fault 10</td><td align="center">0.9035</td><td align="center">0.9133</td><td align="center">0.9454</td></tr><tr class="table-tr"><td colspan="4"><hr class="tbody-hr" /></td></tr></table></td></tr></table></div></div>

Table 8 | Simultaneous-Fault Diagnosis of Automotive Engine Ignition Systems Using Prior Domain Knowledge and Relevance Vector Machine

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Table 8: Simultaneous-Fault Diagnosis of Automotive Engine Ignition Systems Using Prior Domain Knowledge and Relevance Vector Machine

description

Table 8

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