Gaoliang Peng

A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals

Intelligent fault diagnosis techniques have replaced time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning models can improve the accuracy of intelligent fault diagnosis with the help of their multilayer nonlinear mapping ability. This paper proposes a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in the first convolutional layer for extracting features and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform the state-of-the-art DNN model which is based on frequency features under different working load and noisy environment conditions.

Bearing Fault Diagnosis Using Fully-Connected Winner-Take-All Autoencoder

Intelligent fault diagnosis of bearings has been a heated research topic in the prognosis and health management of rotary machinery systems, due to the increasing amount of available data collected by sensors. This has given rise to more and more business desire to apply data-driven methods for health monitoring of machines. In recent years, various deep learning algorithms have been adapted to this field, including multi-layer perceptrons, autoencoders, convolutional neural networks, and so on. Among these methods, autoencoder is of particular interest for us because of its simple structure and its ability to learn useful features from data in an unsupervised fashion. Previous studies have exploited the use of autoencoders, such as denoising autoencoder, sparsity aotoencoder, and so on, either with one layer or with several layers stacked together, and they have achieved success to certain extent. In this paper, a bearing fault diagnosis method based on fully-connected winner-take-all autoencoder is proposed. The model explicitly imposes lifetime sparsity on the encoded features by keeping only

ACDIN: bridging the gap between artificial and real bearing damages for bearing fault diagnosis

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An automated assembly technology for large mobile radar antenna

% largest activations of each neuron across all samples in a mini-batch. A soft voting method is implemented to aggregate prediction results of signal segments sliced by a sliding window to increase accuracy and stability. A simulated data set is generated by adding white Gaussian noise to original signals to test the diagnosis performance under noisy environment. To evaluate the performance of the proposed method, we compare our methods with some state-of-the-art bearing fault diagnosis methods. The experiments result show that, with a simple two-layer network, the proposed method is not only capable of diagnosing with high precision under normal conditions, but also has better robustness to noise than some deeper and more complex models.

Rolling Element Bearings Fault Intelligent Diagnosis Based on Convolutional Neural Networks Using Raw Sensing Signal

Abstract Data-driven algorithms for bearing fault diagnosis have achieved much success. However, it is difficult and even impossible to collect enough data containing real bearing damages to train the classifiers, which hinders the application of these methods in industrial environments. One feasible way to address the problem is training the classifiers with data generated from artificial bearing damages instead of real ones. In this way, the problem changes to how to extract common features shared by both kinds of data because the differences between the artificial one and the natural one always baffle the learning machine. In this paper, a novel model, deep inception net with atrous convolution (ACDIN), is proposed to cope with the problem. The contribution of this paper is threefold. First and foremost, ACDIN improves the accuracy from 75% (best results of conventional data-driven methods) to 95% on diagnosing the real bearing faults when trained with only the data generated from artificial bearing damages. Second, ACDIN takes raw temporal signals as inputs, which means that it is pre-processing free. Last, feature visualization is used to analyze the mechanism behind the high performance of the proposed model.

A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load

Purpose Large-scale mobile radars are still erected manually by using lifting equipment, which often fails to meet the requirements on precision, quality and efficiency in the erecting process. This paper aims to introduce techniques for automatic assembly of large mobile radar antenna. Design/methodology/approach A large-scale metrology system for accurate identification of the positions and orientation of the radar antenna components is presented. A novel three-degree-of-freedom parallel mechanism is designed to realize orientation adjustment of three axes synchronous, and, thus guarantees the efficiency and accuracy of positioning process. Findings The system described in this paper is practicable in outdoor environment and provides a holistic solution that gives full consideration of the operation conditions and the environmental influences. In performance evaluation tests, the measured absolute accuracy is less than ±1 mm and repeatability is less than ±0.5 mm in the positioning task for 10 × 3 m large antenna. Originality/value This paper presents a new concept of an automatic assembly technology for the large radar antenna application.

Application of the fiber-optic distributed temperature sensing for monitoring the liquid level of producing oil wells

Vibration signals captured by the accelerometer carry rich information for rolling element bearing fault diagnosis. Existing methods mostly rely on hand-crafted time-consuming preprocessing of data to acquire suitable features. In contrast, the proposed method automatically mines features from the RAW temporal signals without any preprocessing. Convolutional Neural Network (CNN) is used in our method to train the raw vibration data. As powerful feature exactor and classifier, CNN can learn to acquire features most suitable for the classi_cation task by being trained. According to the results of the experiments, when fed in enough training samples, CNN outperforms the exist methods. The proposed method can also be applied to solve intelligent diagnosis problems of other machine systems.