The extraction and application of principle component representations were studied for machine noise monitoring. On the base of the time- and frequency-domain statistical features extracted from the machine noise signal

the principle component representations of machine noise pattern were explored

the ability of principle component representations to represent the characteristic of machine condition by introducing the idea of correlation was analyzed

and the machine noise monitoring scheme using the effective low-dimensional principle component representations was proposed. Through previously designing four wearing faults on the connecting rod bearing of EQ6100 model gasoline engine

experimental analysis shows that the low-dimensional principle component representations of engine noise can be conveniently and effectively used for representing the machine conditions. Finally

the excellent result with the accuracy of 100% was obtained by monitoring the conditions of the testing samples

which demonstrates the availability of principle component representations for machine noise monitoring.