Patent ID: 11886311
Assignee: SOOCHOW UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G  Y | IPC G

Claim 1:
2. The fault diagnosis method for a rolling bearing under variable working conditions based on a convolutional neural network and transfer learning according to claim 1, wherein Step 1 comprises:
Step 1.1: establishing data sets of different workloads, wherein each data set is named after a workload of the data set, and data in the data sets conforms to different distributions; and
Step 1.2: segmenting samples with N consecutive sampling points as one sample length to make a data set, and using Ds={(xis, yis)}ns−1 (i=0), wherein yis∈{0, 1, 2, . . . , C−1} represents a source domain formed by samples with C different types of labels, xis represents an ith sample in the source domain, yis represents a label of the ith sample in the source domain, and ns is a total quantity of samples in the source domain; using Dt={xjt}nt−1 (j=0) to represent a target domain formed by the unlabeled samples, wherein xjt represents a jth sample in the target domain, and nt is a quantity of all samples in the target domain; acquiring data of the source domain in a probability distribution Ps, and acquiring data of the target domain in a probability distribution Pt, wherein Ps≠Pt, and the data of the source domain and the data of the target domain conform to different distributions.