Signal Status Recognition Based on 1DCNN and Its Feature Extraction Mechanism Analysis

Abstract

In this paper, we construct a one-dimensional convolutional neural network (1DCNN), which directly takes as the input the vibration signal in the mechanical operation process. It can realize intelligent mechanical fault diagnosis and ensure the authenticity of signal samples. Moreover, due to the excellent interpretability of the 1DCNN, we can explain the feature extraction mechanism of convolution and the synergistic work ability of the convolution kernel by analyzing convolution kernels and their output results in the time-domain, frequency-domain. What’s more, we propose a novel network parameter-optimization method by matching the features of the convolution kernel with those of the original signal. A large number of experiments proved that, this optimization method improve the diagnostic accuracy and the operational efficiency greatly.

Mapping Tree Species Composition Using OHS-1 Hyperspectral Data and Deep Learning Algorithms in Changbai Mountains, Northeast China

“…The building blocks of the Conv1D used in this study are illustrated in Figure 3, including an input layer, two convolution layers with rectified linear unit (ReLU) as nonlinear activation function, a maxpool layer, a flatten layer, and two fully connected layers. The convolutional layer and pooling layer perform as hierarchical feature extractors [41], while the fully connected layer acts as a classifier that produces the predictive probabilities of all the object categories in the input data [20,41].…”

“…In the one-dimensional convolutional neural network model, the size of the convolution kernel has a great influence on the classification accuracy [34,41]. The smaller the kernel size is, the more detailed the extracted features will be, but relevant input information will be lost [35].…”

“…The smaller the kernel size is, the more detailed the extracted features will be, but relevant input information will be lost [35]. On the contrary, for large-size kernels, relevant information will be retained but will result in the absence of detailed information [35,41]. We have tried several convolution kernels of different sizes, among which the convolution kernels with the highest classification accuracy are selected for classification.…”

“…For one-dimensional convolutional neural network (Conv1D) classifiers, in addition to adding all spectral features, we also added texture features of various bands as a low-level feature but did not add vegetation indices. In the convolution process of a one-dimensional convolutional neural network, the front and rear nodes have strong correlation and continuity, the single vegetation index feature cannot improve accuracy and has certain interference [35,41]. Based on the same training samples, we also adopted random forest (RF) classifiers as a comparison.…”

1DCNN Fault Diagnosis Based on Cubic Spline Interpolation Pooling

“…Various layers of the CNN are introduced below. e convolutional layer uses a one-dimensional convolution kernel to perform the convolution calculation for the local region of the input signal to produce the corresponding one-dimensional feature map, and different convolution kernels extract different features in the input signals [27]. Each convolution kernel detects specific features at all locations on input feature maps to achieve the weight sharing on the same input feature map.…”