DCNN With Explicable Training Guide and its Application to Fault Diagnosis of the Planetary Gearboxes

Abstract

The diagnosis performance of Deep Convolutional Neural Network (DCNN) method is closely related to the generalization ability of the training model. An empirical training strategy is to randomly disperse the training samples and train the model with mini-batch training samples. But there are still two problems in the empirical method that need to be solved urgently. Firstly, what is the theoretical basis for random discretization of samples? Secondly, how to scientifically quantify batch division? Aiming at these two problems, the theoretical basis of sample random discretization has been deduced and proved, furthermore, a scientific quantitative batch division method is proposed based on the proved thesis. The fault diagnosis results of the planetary gearbox show that: (1) The model obtained by the training guide proposed in this paper has stronger generalization ability; (2) The DCNN with the training guide can accurately and effectively diagnose the faults of planetary gearbox and obtain ideal diagnosis results.

Effective Convolutional Transformer for Highly Accurate Planetary Gearbox Fault Diagnosis

“…To prove that our proposed ECT model is effective and highly accurate for fault diagnosis of planetary gearbox, our method is compared with the state-of-the-art classification methods for fault diagnosis, including the 1DCNN model which has the same number of layers as in [16], the WDCNN in [17], the MSCNN in [18] and the convolutional Bi-LSTM network like in [15], and the Transformer-based models as ViT in [24] and CCT like in [28].…”

“…CNNs have been widely applied in fault diagnosis because of their locality and translation equivariance, which enable CNNs with extraordinary capability to learn the local features and easy to be trained with small datasets [10][11][12]. Luo et al [16] trained the deep convolutional neural network (DCNN) with an explicable training guide for fault diagnosis of planetary gearbox and obtained ideal diagnosis results. Zhang et al [17] proposed a deep convolutional neural network with wide first-layer kernels (WDCNN) for extracting features of raw vibration signals and suppressing high-frequency noise.…”

Adaptive Fisher-Based Deep Convolutional Neural Network and Its Application to Recognition of Rolling Element Bearing Fault Patterns and Sizes

“…e aforementioned applications show that the DCNN is a potential tool in dealing with fault diagnosis of rolling element bearing. While, as a diagnosis model based on the training samples, DCNN is influenced by the number of training samples as well [25]. Here comes the problem, the experimental vibration samples with labels cannot be always sufficient.…”