Research Article

Fault Diagnosis of Rotating Machinery Based on Multisensor Information Fusion Using SVM and Time-Domain Features

Table 2

Diagnostic results of gear by using different features for fusion.

Feature The best parameterDiagnostic accuracy (%)
NormalChipped toothMissing toothAll testing samples

Mean26.521598828488.00
Peak212898866683.33
Amplitude square2122098828889.33
Root mean square2142398788687.33
Root amplitude211.5211.5100887688.00
Standard deviation27.529.598788687.33
Skewness282−198305862.00
Kurtosis22.52−394684067.33
Waveform factor2−1.52992423055.33
Peak factor20.52−194929493.33
Pulse factor2−0.52−296646073.33
Margin factor202−396575569.33