Jia Pengpeng, Wang Chaoge, Zhou Funa, Hu Xiong
School of Logistic Engineering, Shanghai Maritime University, Shanghai 201306, China.
Entropy (Basel). 2023 Jan 28;25(2):242. doi: 10.3390/e25020242.
Deep learning can be applied in the field of fault diagnosis without an accurate mechanism model. However, the accurate diagnosis of minor faults using deep learning is limited by the training sample size. In the case that only a small number of noise-polluted samples is available, it is crucial to design a new learning mechanism for the training of deep neural networks to make it more powerful in feature representation. The new learning mechanism for deep neural networks model is accomplished by designing a new loss function such that accurate feature representation driven by consistency of trend features and accurate fault classification driven by consistency of fault direction both can be secured. In such a way, a more robust and more reliable fault diagnosis model using deep neural networks can be established to effectively discriminate those faults with equal or similar membership values of fault classifiers, which is unavailable for traditional methods. Validation for gearbox fault diagnosis shows that 100 training samples polluted with strong noise are adequate for the proposed method to successfully train deep neural networks to achieve satisfactory fault diagnosis accuracy, while more than 1500 training samples are required for traditional methods to achieve comparative fault diagnosis accuracy.
深度学习可以在没有精确机理模型的情况下应用于故障诊断领域。然而，使用深度学习对微小故障进行精确诊断受到训练样本大小的限制。在只有少量受噪声污染的样本可用的情况下，设计一种新的学习机制来训练深度神经网络，使其在特征表示方面更强大，这一点至关重要。深度神经网络模型的新学习机制是通过设计一个新的损失函数来实现的，这样既能确保由趋势特征一致性驱动的精确特征表示，又能确保由故障方向一致性驱动的精确故障分类。通过这种方式，可以建立一个更强大、更可靠的使用深度神经网络的故障诊断模型，以有效地区分那些故障分类器隶属度值相等或相似的故障，这是传统方法无法做到的。齿轮箱故障诊断的验证表明，对于所提出的方法，100个受强噪声污染的训练样本足以成功训练深度神经网络以实现令人满意的故障诊断精度，而传统方法需要1500多个训练样本才能达到相当的故障诊断精度。
