Fault Diagnosis of Gearbox in Multiple Conditions Based on Fine-Grained Classification CNN Algorithm

The use of the convolutional neural network for fault diagnosis has been a common method of research in recent years. Since this method can automatically extract fault features, it has played a good role in some research studies. However, this method has a clear drawback that the signals will be significantly affected by working conditions and sample size, and it is difficult to improve diagnostic accuracy by directly learning faults, regardless of working conditions. It is therefore a research orientation worthy of a diagnosis of high precision defect in various working conditions. In this article, using a fine-grained classification algorithm, the operating conditions of the object system are considered an approximate classification. A specific failure in different working conditions is considered a beautiful classification. Samples of different faults in different working conditions are learned uniformly and the common characteristics are extracted from the convolutional network so that different faults of different working conditions can simultaneously be identified on the basis of the entire sample. Experimental results show that the method effectively uses the set of samples of the working conditions of the variables to obtain the dual recognition of defects and specific working conditions and the accuracy of the recognition is significantly higher than the method of learning regardless of working conditions.