Centrifugal pump fault diagnosis based on wavelet pack decomposition and random forest

doi:10.3785/j.issn.1006-754X.2024.03.410

2024, Vol. 31

Issue (6): 741-749 DOI: 10.3785/j.issn.1006-754X.2024.03.410

【Special Column】Key Technologies of Design, manufacture, operation and maintenance for New Energy Equipment and Their Applications under the Carbon Peaking and Carbon Neutrality Goals

Centrifugal pump fault diagnosis based on wavelet pack decomposition and random forest

Fei MA¹(

),Liguang SHAO¹,Jun XU¹,Mengqiu TAO¹,Pei YUAN¹(

),Bingtao HU²

^1.Hangzhou Boomy Intelligent Technology Co. , Ltd. , Hangzhou 310053, China
^2.School of Mechanical Engineering, Zhejiang University, Hangzhou 310028, China

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Abstract

Aiming at the difficulties of on-line fault diagnosis of centrifugal pumps in nuclear power plants, a fault diagnosis method based on wavelet pack decomposition and random forest is proposed. Firstly, the wavelet pack decomposition was used to decompose the vibration signal in the radial vertical direction of the centrifugal pump motor drive end into three layers, and the sub-band energy features were extracted. Then, the time-domain statistical features were extracted based on the waveform data of centrifugal pump vibration signal, and combined with wavelet packet energy features as inputs for the random forest model. Finally, the random forest model was trained with centrifugal pump vibration dataset collected from vibration test, and the centrifugal pump fault diagnosis model was formed. This model was compared with machine learning models such as support vector machine, logistic regression, K-nearest neighbor and Gaussian Naive Bayes on the same centrifugal pump vibration dataset. The results showed that the constructed model could accurately identify different operating states of the centrifugal pump, such as normal operation, impeller damage, impeller blockage and motor bearing fault, and exhibited better classification performance. The fault diagnosis method based on wavelet packet decomposition and random forest can effectively extract features from vibration signals and realize fault classification, which has certain feasibility and effectiveness for on-line fault intelligent diagnosis of centrifugal pumps in nuclear power plants.

Key words： centrifugal pump fault diagnosis vibration signal wavelet pack decomposition random forest

Received: 20 December 2023 Published: 31 December 2024

CLC:

TH 311

Corresponding Authors: Pei YUAN E-mail: maf@boomy.cn;yuanp@boomy.cn

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	Fei MA
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Cite this article:

Fei MA,Liguang SHAO,Jun XU,Mengqiu TAO,Pei YUAN,Bingtao HU. Centrifugal pump fault diagnosis based on wavelet pack decomposition and random forest. Chinese Journal of Engineering Design, 2024, 31(6): 741-749.

URL:

https://www.zjujournals.com/gcsjxb/10.3785/j.issn.1006-754X.2024.03.410 OR https://www.zjujournals.com/gcsjxb/Y2024/V31/I6/741

基于小波包分解与随机森林的离心泵故障诊断

针对核电厂离心泵在线故障诊断困难的问题，提出了一种基于小波包分解与随机森林的故障诊断方法。首先，利用小波包分解对离心泵电机驱动端径向垂直方向的振动信号进行3层分解并提取子频带能量特征。然后，基于离心泵振动信号的波形数据提取时域统计特征，并与小波包能量特征相结合作为随机森林模型的输入。最后，通过由振动试验得到的离心泵振动数据集对随机森林模型进行训练，形成离心泵故障诊断模型，并对该模型与支持向量机、逻辑斯蒂回归、K近邻、高斯朴素贝叶斯等机器学习模型在相同的离心泵振动数据集上进行了对比测试。结果表明，所构建的模型能够准确识别离心泵正常、叶轮破损、叶轮堵塞、电机轴承故障等运行状态，并表现出更优的分类性能。基于小波包分解与随机森林的故障诊断方法可以有效地从振动信号中提取特征并实现故障分类，对于核电厂离心泵在线故障智能诊断具有一定的可行性和有效性。

关键词： 离心泵, 故障诊断, 振动信号, 小波包分解, 随机森林

Fig.1 Framework of centrifugal pump fault diagnosis system

特征量	计算式
有效值	$x r m s = 1 M ∑ m = 1 M x m 2$
均值	$x ˉ = 1 M ∑ m = 1 M x m$
标准差	$σ x = 1 M - 1 ∑ m = 1 M x m - x ˉ 2$
峰值因子	$C f = m a x x m x r m s$
波形因子	$S f = x r m s 1 M ∑ m = 1 M x m$
峭度	$K r = ∑ m = 1 M x m - x ˉ 4 M σ x 4$
偏度	$S k = ∑ m = 1 M x m - x ˉ 3 M σ x 3$
扰动因子	$P f = σ x x ˉ$

Table 1 Time-domain statistical features of vibration signal

Fig.2 Three-layer wavelet packet decomposition structure

Fig.3 Construction process of random forest model

Table 2 Specification parameters of centrifugal pump

Fig.4 Schematic of centrifugal pump vibration test bench

Fig.5 Physical map of centrifugal pump vibration test bench

Fig.6 Defective parts of test centrifugal pump

Fig.7 Original vibration signal of centrifugal pump

Fig.8 Wavelet packet decomposition coefficient of vibration signal of centrifugal pump under normal state

Fig.9 Time-domain waveform and spectrogram of original vibration signal and reconstructed signal of centrifugal pump under normal state

Fig.10 Normalized value of wavelet packet energy of centrifugal pump vibration signal

Fig.11 Fault diagnosis results of centrifugal pump based on random forest

Fig.12 Prediction results of different fault diagnosis models

Table 3 Comparison of prediction effect of different fault diagnosis models


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