A Novel Method for Early Gear Pitting Fault Diagnosis Using Stacked SAE and GBRBM

Abstract

Research on data-driven fault diagnosis methods has received much attention in recent years. The deep belief network (DBN) is a commonly used deep learning method for fault diagnosis. In the past, when people used DBN to diagnose gear pitting faults, it was found that the diagnosis result was not good with continuous time domain vibration signals as direct inputs into DBN. Therefore, most researchers extracted features from time domain vibration signals as inputs into DBN. However, it is desirable to use raw vibration signals as direct inputs to achieve good fault diagnosis results. Therefore, this paper proposes a novel method by stacking spare autoencoder (SAE) and Gauss-Binary restricted Boltzmann machine (GBRBM) for early gear pitting faults diagnosis with raw vibration signals as direct inputs. The SAE layer is used to compress the raw vibration data and the GBRBM layer is used to effectively process continuous time domain vibration signals. Vibration signals of seven early gear pitting faults collected from a gear test rig are used to validate the proposed method. The validation results show that the proposed method maintains a good diagnosis performance under different working conditions and gives higher diagnosis accuracy compared to other traditional methods.

A Review on Deep Learning Applications in Prognostics and Health Management

“…Armed with these transformation techniques and the transfer learning strategy, several pretrained CNNs, originally trained on natural images, were transferred to fault diagnosis applications using vibration data; examples include LeNet-5 [107], [109], [110], VGG-16 [106], AlexNet [95] and ResNet-50 [108]. We also found some studies using auto-encoder [105], [116], [117] and random projection [118] as a pre-posed layer before a deep neural network for the purpose of denoising and compressing.…”

Applications of machine learning to machine fault diagnosis: A review and roadmap

“…The training strategy improves the generalization performance and the rate of convergence of conventional AE-based models, and the hybrid diagnosis models have been successfully used for fault diagnosis of motor bearings [340,341] and wind turbines [342]. DBN is the other method to construct hybrid diagnosis models with stacked AE [343,344]. In the diagnosis models, stacked AE is considered to learn features from the input monitoring data, while DBN is regard to recognize the health states of machine according to the learned features.…”

Ensembled mechanical fault recognition system based on deep learning algorithm

“…Qi, et al [16] presented an ensemble empirical mode decomposition method utilizing auto regressive representation. Authors in [17] used the stacked auto encoders for fault detection and classification is done using the Softmax activation function. The fault detection accuracy computed using this method is comparatively advanced than the other current methods.…”