Abstract
The conventional pooling method for processing one-dimensional vibration signals may lead to certain issues, such as weakening and loss of feature information. The present study proposes the cubic spline interpolation pooling method. The method is appropriate for processing one-dimensional signals. The proposed method can transform the pooling problem into a linear fitting problem, use the cubic spline interpolation method with outstanding fitting effects, and calculate the fitting function of the input signals. Moreover, the values of the interpolation points are sequentially taken as the feature value output. Furthermore, the network using the conventional pooling method and the pooling network model proposed in the present study are compared, tested, and analyzed on the constructed simulation signals and the measured bearing dataset. It is concluded that the proposed pooling method can reduce the data dimension while improving the network feature extraction capability and is more appropriate for pooling one-dimensional signals.
Highlights
With the increasing development of the deep learning in the last few decades, it has extensively attracted many communities so that remarkable achievements have been achieved in many fields [1,2,3]
When calculating the feature value of a certain part of the input signal, it is necessary to analyze and count the features of the signal and to use the new feature to represent the total features of the signal. is segment signal is called the pooling domain, and the process is called pooling. e use of pooling operations can improve the expressive ability of features and reduce the data dimension, which effectively avoids the overfitting phenomenon caused by excessive parameters and complicated structure in the network training
The conventional pooling algorithms need to be selected according to the features of the input signals, which can obviously reduce the efficiency and versatility of the 1DCNN for fault diagnosis. erefore, considering the limitations of the abovementioned conventional pooling algorithms, an improved pooling algorithm is proposed in the present study named the cubic spline interpolation pooling, which makes the feature information fully extracted and mined while considering the time continuity of input signals
Summary
With the increasing development of the deep learning in the last few decades, it has extensively attracted many communities so that remarkable achievements have been achieved in many fields [1,2,3]. Many scholars have applied the deep-expanded CNN to the fault diagnosis and achieved reasonable results. Peng and Liu [10] applied the 1DCNN to diagnose the HSTs’ wheel-to-bearing vibration signals and achieved reasonable results. Matthew [19, 20] used a simple and effective stochastic pooling method to prevent the overfitting during the CNN training and achieved reasonable results. E abovementioned pooling methods mostly focus on two-dimensional inputs and solve problems such as the image recognition. Erefore, when the one-dimensional state signal is pooled, the commonly used pooling algorithms in the image recognition are applied, which may lead to weakening or even losing certain important features of the signal information. Erefore, the present article proposes a pooling algorithm for the feature extraction of one-dimensional state signals.
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