Preprocessing-Free Gear Fault Diagnosis Using Small Datasets With Deep Convolutional Neural Network-Based Transfer Learning

Abstract

Early fault diagnosis in complex mechanical systems such as gearbox has always been a great challenge, even with the recent development in deep neural networks. The performance of a classic fault diagnosis system predominantly depends on the features extracted and the classifier subsequently applied. Although a large number of attempts have been made regarding feature extraction techniques, the methods require great human involvements are heavily depend on domain expertise and may thus be non-representative and biased from application to application. On the other hand, while the deep neural networks based approaches feature adaptive feature extractions and inherent classifications, they usually require a substantial set of training data and thus hinder their usage for engineering applications with limited training data such as gearbox fault diagnosis. This paper develops a deep convolutional neural network-based transfer learning approach that not only entertains pre-processing free adaptive feature extractions, but also requires only a small set of training data. The proposed transfer learning architecture consists of two parts; the first part is constructed with a piece of a pre-trained deep neural network that serves to extract the features automatically from the input, the second part is a fully connected stage to classify the features that needs to be trained using gear fault experiment data. The proposed approach performs gear fault diagnosis using preprocessing free raw accelerometer data and experiments with various sizes of training data were conducted. The superiority of the proposed approach is revealed by comparing the performance with other methods such as locally trained convolution neural network and angle-frequency analysis based support vector machine. The achieved accuracy indicates that the proposed approach is not only viable and robust, but also has the potential to be readily applicable to other fault diagnosis practices.

Survey on Deep Learning applied to predictive maintenance

“…In [16] present a transfer learning approach based on a pretrained deep convolutional neural network that is used to automatically extract input characteristics. They then go through a fully connected step to classify the characteristics obtained using experimental data composed of gear defects.…”

Bearing Fault Diagnosis under Variable Rotational Speeds Using Stockwell Transform-Based Vibration Imaging and Transfer Learning

“…With a 1D signal, it is very difficult to observe identical patterns for different health types. Moreover, in recent studies [33,34], preprocessing-free transfer learning is claimed. Even with the limited amount of data for mechanical machines, network learning for a transferring scenario remains questionable.…”

“…One more reason for considering CNN as the deep neural network for this study is that a large-scale CNN has the potential to be the most effective of the deep learning and classical methods. Moreover, using the transferred knowledge obtained from TL, with only a small set of training data, the large-scale CNN can achieve excellent performance in the considered scenarios [33,34,38].…”

“…In this study, as TL and CNN are designed together, the learning of the well-trained u layers are passed to the v layers of the target network, where u < v, v = u + 1, and layer number u + 1 denotes the output layer or last layer. Thus, the last layer of the target network is trained, providing an accuracy of output based on the learning from the source network [34].…”

Rolling Bearing Fault Diagnosis Based on Wavelet Packet Transform and Convolutional Neural Network

“…Generally, in a deep learning-based method, the features are extracted by multiple stacked layers, and then, output probabilities are calculated based on the last high-level abstraction data through the non-linear fitting operation of classification layer. Popular deep learning methods, such as deep neural networks (DNNs) [16] and auto-encoder [17], have been investigated, and they have shown their promising capability to capture representative features from input data by the linear and non-linear fitting operations. Sun et al [18] developed an end-to-end fault diagnosis method based on sparse auto-encoder and DNN to address the fault identification problem, in which the representative features were extracted automatically from condition signals by the auto-encoder.…”