The present application relates to the technical field of
mechanical equipment fault diagnosis, and particularly relates to a kind of based on online ferrography's
abrasive particle group
feature extraction method, it is characterized by: including the following steps: obtaining segmented online
abrasive particle image;
Abrasive particle group
feature extraction;
Abrasive particle group feature representation;
Abrasive particle group
feature selection.The present application extracts the
abrasive particle number, maximum abrasive particle
chain length, maximum abrasive particle chain width, abrasive particle chain width mean value, abrasive particle
chain length mean value, abrasive particle area mean value feature parameters based on pixel significance, calculates fractal dimension and abrasive particle coverage area index, the abrasive particle features extracted are characterized and verified by typical online abrasive particle image, eight kinds of characteristic
time series under the whole life wear are constructed, the abrasive
particle number distribution of different area levels under
time series is proposed, and finally abrasive particle coverage area index, abrasive
particle number, maximum abrasive particle chain width are selected as the representation parameters of abrasive particle group, which provides important reference index for next step of wear
state recognition and prediction.