Open AccessIntelligent Diagnosis of Rolling Bearing Fault Based on QFMAM
Author(s) -
Chun Lv,
Peilin Zhang,
Bing Li,
Daming Wu,
Yunqiang Zhang
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/782/3/032084
Subject(s) - naive bayes classifier , support vector machine , computer science , bearing (navigation) , artificial intelligence , pattern recognition (psychology) , fuzzy logic , associative property , rolling element bearing , superposition principle , mathematics , mathematical analysis , physics , quantum mechanics , pure mathematics , vibration
In order to solve the problem that the accuracy is not high when the traditional fuzzy morphological associative memories network (FMAM) is used to classify samples, based on quantum superposition and collapse, the quantum fuzzy morphological associative memories network (QFMAM) is proposed. In QFMAM, the structural element is constructed by the qubit system and the qubit probability represents corresponding membership degree to obtain adaptive structural elements. The samples are preprocessed first and then classified to ensure high classification accuracy. QFMAM, FMAM, support vector machine (SVM) and naive Bayes classifier (NBC) are used to classify simulation data and experimental signals of rolling bearings respectively. Comparing the performance of the four classifiers, it is obvious that the training time of QFMAM is far less than SVM and NBC, and the classification accuracy of the test samples by QFMAM is higher than that by the other three classifiers. QFMAM is very suitable for intelligent diagnosis of rolling bearing fault.