采用参数化解调的变转速下柱塞泵故障诊断方法

徐孜; 潮群; 高浩寒; 陶建峰; 刘成良; 孟成文

doi:10.7652/xjtuxb202110003

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采用参数化解调的变转速下柱塞泵故障诊断方法

专题智能诊断 | 更新时间：2025-07-09

- 采用参数化解调的变转速下柱塞泵故障诊断方法
- A Fault Diagnosis Method for Piston Pump Under Variable Speed Conditions Using Parameterized Demodulation
- 西安交通大学学报 2021年55卷第10期页码：19-29
- 作者机构：
  
  上海交通大学机械与动力工程学院,上海,200240
- 作者简介：
- 基金信息：
  
  : 2021-03-27。作者简介: 徐孜(1997—),女,硕士生;陶建峰(通信作者),男,副教授,博士生导师。基金项目: 国家重点研发计划资助项目(2020YFB2007202);中国博士后科学基金资助项目(2019M660086);博士后创新人才支持计划资助项目(BX20200210)
- DOI：10.7652/xjtuxb202110003
  中图分类号： TH165.3;TH322
- 网络首发：2021-10-10，
  
  纸质出版：2021
- 稿件说明：
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徐孜, 潮群, 高浩寒, 等. 采用参数化解调的变转速下柱塞泵故障诊断方法[J]. 西安交通大学学报, 2021,55(10):19-29.

A Fault Diagnosis Method for Piston Pump Under Variable Speed Conditions Using Parameterized Demodulation[J]. 2021, 55(10): 19-29.
徐孜, 潮群, 高浩寒, 等. 采用参数化解调的变转速下柱塞泵故障诊断方法[J]. 西安交通大学学报, 2021,55(10):19-29. DOI： 10.7652/xjtuxb202110003.

A Fault Diagnosis Method for Piston Pump Under Variable Speed Conditions Using Parameterized Demodulation[J]. 2021, 55(10): 19-29. DOI： 10.7652/xjtuxb202110003.

摘要

针对柱塞泵在变转速工况下运行时

存在因时变信号特征提取不易和噪声抑制困难导致的故障诊断准确度低的问题

提出一种以参数化解调为基础的柱塞泵空化故障诊断方法。首先

采用粒子群算法对泵出口时变压力信号重点分量的相位参数进行估计

再根据估计得的参数将信号解调至平稳化并滤波

接着对滤波后的信号进行反解调来获取单一信号分量; 然后迭代执行参数化解调的上述步骤以提取所有重点分量并实现信号重构; 最后对重构信号进行切片来构建数据集

再将数据集输入一维卷积-长短期记忆神经网络(1DCNN-LSTM)中提取局域特征并学习长期时序信息

从而实现准确地识别柱塞泵空化等级。流体仿真实验验证了参数化解调在信号分量提取上的有效性。使用实测信号进行空化故障诊断实验

结果表明:相比未经参数化解调的诊断方法

该方法的空化等级识别准确率提高了6.5%

达到95.4%

且具有更好的泛化性能; 在信噪比为0 dB的强噪声环境下

准确率维持在90%以上。

Abstract

In order to solve the problem of low fault diagnosis accuracy of piston pump under variable speed conditions

caused by the difficulty of time-varying signal features extraction and noise suppression

a piston pump cavitation detection method based on parameterized demodulation is proposed. Firstly

a particle swarm optimization algorithm is used to estimate the phase parameters of the key component of the pump outlet time-varying pressure signal

and the signal is demodulated to be stabilized and filtered according to the estimated parameters. Then the filtered signal is recovered to obtain a single signal component. The above steps of parameterized demodulation are iteratively performed to extract all key components and realize signal reconstruction. Finally

the reconstructed signal is sliced to construct the dataset

which is then fed into one dimensional convolutional long short term memory neural network(1DCNN-LSTM)to extract local features and learn long-term time series information

so as to realize the accurate identification of the piston pump cavitation degrees. The fluid simulation signals validate the effectiveness of the parameterized demodulation in extracting signal components. Real signals are used to conduct cavitation fault diagnosis experiments. The results show that

compared with the diagnostic method without the parameterized demodulation

the identification accuracy increases by 6.5%

and achieves to 95.4%

and the proposed model has better generalization performance. In the strong noise environment with SNR of 0 dB

the accuracy rate of diagnosis is maintained above 90%.

关键词

Keywords

references

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