Data-driven predictive maintenance research on hydraulic motor

doi:10.3785/j.issn.1006-754X.2024.14.02

2024, Vol. 31

Issue (6): 793-800 DOI: 10.3785/j.issn.1006-754X.2024.14.02

【Special Column】Achievement Exhibition of "2024’Science and Technology Festival for Construction Machinery Industry "-Innovative Technologies and Their Applications

Data-driven predictive maintenance research on hydraulic motor

Qiang LIU^1,⁴(

),Jianxin ZHU^2,³,Yuyuan CUI²

^1.School of Intelligent Manufacturing Engineering, Changsha Vocational and Technical College, Changsha 410100, China
^2.State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha 410100, China
^3.Changsha Special Engineering Equipment Industrial Technology Research Institute Co. , Ltd. , Changsha 410100, China
^4.National Enterprise Technology Center, Sunward Intelligent Equipment Co. , Ltd. , Changsha 410100, China

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Abstract

Predictive maintenance is one of the typical applications of digital twin, and data-driven is the main way to realize predictive maintenance. Aiming at the problems of difficult fault feature extraction and large deviation of prediction results in predictive maintenance, a predictive maintenance model building method based on the combination of VMD (variational mode decomposition) algorithm and HHT (Hilbert-Huang transform) algorithm was proposed. The time-domain features of the vibration signal were extracted by VMD+HHT algorithm, the data dimension was reduced by combining deep sparse auto-encoder (DSAE), support vector data description (SVDD) algorithm was used to form a health index curve, and a predictive maintenance model was established based on long short-term memory (LSTM) algorithm. The method was applied to the predictive maintenance of a hydraulic motor of a rotary drilling rig. The vibration signal of the motor housing was extracted, the predictive maintenance model of the hydraulic motor was constructed, and the validity and accuracy of the method were verified by test. The test results showed that adopting the predictive maintenance model based on DSAE+SVDD+LSTM algorithm could avoid the problems of mode aliasing and endpoint effect, the prediction accuracy could reach more than 90%, and the model had practical value. The research results can provide important reference for the construction of hydraulic component digital twin predictive maintenance application scenarios.

Key words： data-driven hydraulic motor predictive maintenance

Received: 15 April 2024 Published: 31 December 2024

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Cite this article:

Qiang LIU,Jianxin ZHU,Yuyuan CUI. Data-driven predictive maintenance research on hydraulic motor. Chinese Journal of Engineering Design, 2024, 31(6): 793-800.

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https://www.zjujournals.com/gcsjxb/10.3785/j.issn.1006-754X.2024.14.02 OR https://www.zjujournals.com/gcsjxb/Y2024/V31/I6/793

基于数据驱动的液压马达预测性维护研究

预测性维护是数字孪生典型应用之一，数据驱动是实现预测性维护的主要方式。针对预测性维护中故障特征提取困难、预测结果偏差较大的问题，提出了一种基于变分模态分解（variational mode decomposition，VMD）算法与希尔伯特-黄变换（Hilbert-Huang transform，HHT）算法相结合的预测性维护模型的构建方法。用VMD+HHT算法提取振动信号时域特征，结合深度稀疏自编码器（deep sparse auto-encoder，DSAE）进行数据降维，采用支持向量数据描述（support vector data description，SVDD）算法来形成健康度曲线，并基于长短时记忆网络（long short-term memory，LSTM）算法建立预测性维护模型。将该方法应用于一款旋挖钻机液压马达的预测性维护。提取马达外壳的振动信号，构建液压马达预测性维护模型，并通过试验来验证该方法的有效性与准确性。试验结果表明，采用基于DSAE+SVDD+LSTM算法构建的预测性维护模型可避免模态混叠及端点效应等问题，预测精度达90%以上，模型具有实用价值。研究结果可为液压元件数字孪生预测性维护应用场景的建设提供重要参考。

关键词： 数据驱动, 液压马达, 预测性维护

Fig.1 Bearing accelerated degradation test bench

Fig.2 Original vibration signal

Fig.3 Time-frequency analysis results of vibration signal

Fig.4 Marginal spectrum change based on HHT algorithm

Fig.5 Some vibration signal dimension reduction data

Fig.6 HI curves of bearings

Fig.7 Prediction results of bearing remaining life

Table 1 Prediction error of bearing remaining life

Fig.8 Power head hydraulic motor for rotary drilling rig

Fig.9 Test platform for hydraulic motor

Table 2 Test equipment for hydraulic motor

Fig.10 Tangential vibration signal of motor housing with moderately worn

Fig.11 IMF components of hydraulic motor vibration signal

Fig.12 HHT time-frequency spectrum of hydraulic motor vibration signal

Fig.13 HI curve of hydraulic motor

Fig.14 Prediction results of hydraulic motor degradation trend


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