Purpose

The purpose of this study is to achieve an accurate intelligent fault diagnosis of rolling bearing.

Design/methodology/approach

To extract deep features of the original vibration signal and improve the generalization ability and robustness of the fault diagnosis model, this paper proposes a fault diagnosis method of rolling bearing based on multiscale convolutional neural network (MCNN) and decision fusion. The original vibration signals are normalized and matrixed to form grayscale image samples. In addition, multiscale samples can be achieved by convoluting these samples with different convolution kernels. Subsequently, MCNN is constructed for fault diagnosis. The results of MCNN are put into a data fusion model to obtain comprehensive fault diagnosis results.

Findings

The bearing data sets with multiple multivariate time series are used to testify the effectiveness of the proposed method. The proposed model can achieve 99.8% accuracy of fault diagnosis. Based on MCNN and decision fusion, the accuracy can be improved by 0.7%–3.4% compared with other models.

Originality/value

The proposed model can extract deep general features of vibration signals by MCNN and obtained robust fault diagnosis results based on the decision fusion model. For a long time series of vibration signals with noise, the proposed model can still achieve accurate fault diagnosis.

中文翻译：

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多尺度卷积神经网络与决策融合在滚动轴承故障诊断中的应用

目的

本研究的目的是实现滚动轴承的精确智能故障诊断。

设计/方法/方法

为了提取原始振动信号的深层特征，提高故障诊断模型的泛化能力和鲁棒性，提出了一种基于多尺度卷积神经网络和决策融合的滚动轴承故障诊断方法。原始振动信号经过归一化和矩阵化以形成灰度图像样本。此外，可以通过使用不同的卷积内核对这些样本进行卷积来获得多尺度样本。随后，构建了用于故障诊断的MCNN。将MCNN的结果放入数据融合模型中，以获得全面的故障诊断结果。

发现

使用具有多个多元时间序列的轴承数据集来证明该方法的有效性。提出的模型可以达到99.8％的故障诊断准确率。基于MCNN和决策融合，与其他模型相比，准确性可以提高0.7％–3.4％。

创意/价值

该模型可以通过MCNN提取振动信号的深层通用特征，并基于决策融合模型获得鲁棒的故障诊断结果。对于带有噪声的振动信号的较长时间序列，所提出的模型仍然可以实现准确的故障诊断。