Abstract
This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types of inputs, e.g., raw signals, and time-frequency spectra images by short time Fourier transform. In the application of regression and the estimation of machining surface roughness, the 1DCNN is utilized and the corresponding CNN structure (hyper parameters) optimization is proposed by using uniform experimental design (UED), neural network, multiple regression, and particle swarm optimization. It demonstrates the effectiveness of the proposed approach to obtain a structure with better performance. In applications of classification, bearing faults and tool wear classification are carried out by vibration signals analysis and CNN. Finally, the experimental results are shown to demonstrate the effectiveness and performance of our approach.
Highlights
Vibration signals can be applied for machine diagnosis and help discover problems during machining
Convolutional layers and pooling layers are adopted for automatic feature extraction when fully connected layers are general neural networks which play the roles of classifier or predictor
The experimental results were introduced to illustrate that the convolutional neural networks (CNNs) can be applied for both prediction and classification
Summary
Vibration signals can be applied for machine diagnosis and help discover problems during machining. The convolutional neural network (CNN) discussed in this paper is widely applied for bearing diagnosis using raw signals or spectra of signals [20,21,22,23,24,25,26]. In the on-line approach, the status of a tool can be predicted using vibration, acoustic emission, and force signals of vises and machine tools [27,28,29]. Predicting quality using machining parameters is discussed in many studies. Deep learning approaches provide automatic feature extractions; for instance, a convolutional neural network (CNN) [40]. Applications of a CNN in vibration signals are discussed in lots of research, including bearing faults diagnosis, tool wear classification and machining roughness estimation.
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