Aiming at the problem of low probability of occurrence events such as coal mill failures that are difficult to extract and used for machine learning classification

resulting in low fault diagnosis accuracy

a PCA-FINCH high-precision fault diagnosis method for small samples is proposed. Firstly

based on principal component analysis PCA

fault detection is carried out on the historical data that characterizes the operating state of the equipment

and the occurrence of faults is detected and the fault samples are identified through the T2 control limit and the Q control limit

and the fault samples are extracted to form a small sample fault set; Secondly

based on the FINCH classifier

the obtained small sample fault set is accurately classified to realize the fault diagnosis of the equipment. Finally

the method is verified using a historical data set containing coal mill faults. The results show that the PCA-FINCH fault diagnosis method proposed can achieve high-precision classification of small-sample faults

and its accuracy is 2.61 percentage points

1.74 percentage points and 1.85 percentage points higher than that of decision tree CART

random forest RF and support vector machine SVM

respectively

and its convergence speed is excellent.