基于频谱包络曲线的稀疏自编码算法及在齿轮箱故障诊断的应用

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振动与冲击 ›› 2018, Vol. 37 ›› Issue (4) : 249-256.

论文

张绍辉罗洁思

作者信息 +

Sparse autoencoder algorithm based on spectral envelope curve and Its application in gearbox fault diagnosis

Zhang Shaohui Luo Jiesi

Author information +

文章历史 +

摘要

直接将时域或者频域作为低层输入信息构建深度学习故障诊断模型，可以有效的削弱人为因素的干扰，进一步提高人工智能在故障诊断领域的发展。然而，低层输入的时域信号长度难以划定，而频域信号的数据长度较大，导致模型的计算效率降低。针对该问题，提出预先对低层频域信号提取包络线，得到表征频域变化态势的信息成分，接着再与稀疏自编码结合构建稀疏自编码的故障诊断模型。齿轮箱故障诊断实验证明，与原始频域输入相比，所提方法能够在保证诊断效果的同时，降低计算复杂度和所需要的存储空间。

Abstract

Build fault diagnosis model based on deep learning by time domain or frequency domain as a low-level input information directly can effectively weaken the interference of man-made factors and improve the development of artificial intelligence in mechanical fault diagnosis. However, time domain signal length is difficult to draw, while frequency domain signalis too length lossing computation efficiency. Aiming at the problem, put forward to extract frequency domain signal envelope, which would get the trend of frequency information, then combined with sparse autoencoder to constructs the fault diagnosis mode. Gearbox fault diagnosis experiments indicate that, comparing with the original input frequency domain, the proposed method can effectively speed up the computation process and decrease the memory space, while keeping the ability of condition recognition.

导出引用

张绍辉罗洁思. 基于频谱包络曲线的稀疏自编码算法及在齿轮箱故障诊断的应用[J]. 振动与冲击, 2018, 37(4): 249-256

Zhang Shaohui Luo Jiesi . Sparse autoencoder algorithm based on spectral envelope curve and Its application in gearbox fault diagnosis[J]. Journal of Vibration and Shock, 2018, 37(4): 249-256

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