DIAGNOSIS KETIDAKLURUSAN (MISALIGNMENT) POROS MENGGUNAKAN METODE MULTICLASS SUPPORT VECTOR MACHINE (SVM)

<pre><span lang="EN-US">Misalignment is a condition where the centerlines of two coupled shafts do not coincide. Misalignment is the commonly fault in rotating machinery. Detection and diagnosis of shaft misalignment is crucial to achieve its optimal performance. The purpose of research is to diagnose shaft misalignment using multiclass support vector machine (SVM). </span><span lang="EN-US">The time-domain vibration signals of a shaft alignment rig with normal, parallel misalignment and angular misalignment of shaft conditions were obtained from vibration measurement signals. The accelerometer was used to measure vibration with </span><span lang="EN-US">a sampling frequency of 20 kHz at the constant speed operation of 1000 rpm</span><span lang="EN-US">. The features of median, RMS, crest factor, variance, kurtosis, shape factor, impulse factor, skewness, range, standard deviation and maximum were extracted from the vibration signal. The </span><span lang="EN-US">Principal Component Analysis (</span><span lang="EN-US">PCA) was applied for reduce the number of variables for data input to principal components with lower dimension. The multiclass SVM with </span><span lang="EN-US">One Against One (OAO) method</span><span lang="EN-US">and linear kernel</span><span lang="EN-US"> were used for classification. The results </span><span lang="EN-US">show that SVM </span><span lang="EN-US">for diagnosis of shaft misalignment show a good performance with an accuracy of 100%.</span></pre>