1. Introduction
Rolling bearings are extensively used in manufacturing, hydropower, transportation, and other industries [
1]. Rolling bearings are susceptible to various failures when operating at high speeds and heavy loads, affecting the performance and efficiency of the entire mechanical system and eventually causing economic losses, environmental pollution, casualties, and other serious effects [
2]. Therefore, the early and accurate detection of rolling bearing faults is essential to ensure efficient production and avoid catastrophic accidents in industrial applications.
Recently, as a result of industrial automation, data-driven fault diagnosis technology, which can discover the potential relationship between collected data and bearing status, has attracted increasing attention [
3]. Traditional fault diagnosis technology includes two main steps: feature extraction and fault classification [
4]. The collected fault signals were first analyzed using signal analysis techniques and statistical calculations to identify features related to the machine’s operation state. The obtained features were then input into the pattern recognition algorithm for fault diagnosis [
5]. For example, Wang et al. [
6] extracted multiscale features using generalized composite multiscale weighted permutation entropy technology. They used a support vector machine (SVM) optimized using the marine predators algorithm to diagnose and identify bearing faults according to the extracted features. Vashishtha et al. [
7] extracted fault features using a filter-based relief technique and then realized intelligent recognition using an extreme learning machine (ELM). They also verified that ELM is superior to SVM in terms of fault diagnosis. Owing to its few training parameters and short running time, it is a promising model for fast fault diagnosis.
Traditional fault diagnosis methods extract features mainly through engineering experience, which significantly affects their diagnostic performance [
8]. Many data features are often extracted to improve fault diagnosis accuracy, easily leading to data redundancy and calculation waste. To realize adaptive feature extraction, convolutional neural networks (CNNs) [
9], inspired by deep learning, automatically extract fault features by alternately using convolution and pooling operations, and they have been extensively used in the field of mechanical fault diagnosis [
10]. CNNs have been shown to perform fault diagnosis more effectively than conventional techniques using shallow structures, such as SVM and artificial neural networks [
11]. Sun et al. [
12] combined a symmetrical dot pattern image with a CNN and realized an accurate rolling mechanical diagnosis based on an optimal CNN. Khodja et al. [
13] used a CNN and vibration spectrum imaging to classify bearing faults, which exhibited excellent performance in terms of accuracy and robustness.
Traditional CNNs have some flaws, such as information loss during feature extraction, which reduces detection accuracy. Their generalization ability and accuracy are greatly improved by increasing the depth of the network structure. However, blindly increasing the network depth causes a waste of computing resources and overfitting [
14]. Therefore, extracting features from multiple convolution kernels of different sizes makes the network wider. The realization of multiscale feature extraction can provide a solution to this problem [
15]. For example, Deng et al. [
16] designed a novel multi-scale feature fusion block to fuse sensitive fault features. At the same time, CNNs have shortcomings such as many parameters and a long running time. Some scholars have found that CNNs with randomly generated parameters exhibit good feature extraction abilities [
17]. Pinto and Cox [
18] extracted features by randomly generating a large number of complex, nonlinear, and multilayered CNNs combined with standard machine learning techniques, and they achieved good results in the visual system. Jarrett et al. [
19] adopted a CNN with random weights, which could perform the object recognition task well without training, thus avoiding a time-consuming learning process. The feature extraction ability of multi-scale convolutional neural networks (MSCNN) with randomly generated parameters is yet to be studied. This topic is studied in this paper.
Aiming at the problems of the feature extraction of the bearing fault not being sufficient and the time required to adjust the parameters of the deep learning network, we proposed a novel mechanical fault diagnosis method based on an MSCNN-ELM. It includes three consecutive steps: signal preprocessing, feature extraction based on the MSCNN, and fault classification based on the ELM. The main contributions of this study are as follows:
(1) A new fault diagnosis model composed of an MSCNN and ELM was proposed. The MSCNN was used to extract multiscale features from images obtained using the continuous wavelet transform (CWT). The ELM was used as a classifier to determine the potential relationship between the fault features and the labels.
(2) The parameters of the MSCNN in the model were randomly generated with a Gaussian probability distribution, which reduced the calculation time for adjusting the parameters compared with a deep neural network. The best parameters were found using the grid search method to improve the fault diagnosis accuracy.
(3) The self-made idler dataset and bearing dataset of Case Western Reserve University (CWRU) were used in the fault diagnosis experiments, and the effectiveness of the proposed method was confirmed. Using accuracy and running time as the evaluation indicators, the fault diagnosis performance of the MSCNN-ELM was thoroughly examined in comparison to other methods.
The following summarizes the structure of the paper:
Section 2 introduces the basic principles of the MSCNN and the ELM.
Section 3 introduces the algorithm flow chart based on the MSCNN-ELM. Two other experimental datasets are described to further substantiate the advantages of the MSCNN-ELM in
Section 4.
Section 5 summarizes the conclusions and prospects of this research.