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浙江大学学报(工学版)
浙江大学学报(工学版)  2020, Vol. 54 Issue (3): 467-474    DOI: 10.3785/j.issn.1008-973X.2020.03.006
机械工程     
基于一维卷积神经网络的钻杆故障诊断
金列俊1(),詹建明1,2,*(),陈俊华1,2,王涛2
1. 浙江大学 机械工程学院，浙江 杭州 310027
2. 浙江大学宁波理工学院 机电与能源工程学院，浙江 宁波 315100
Drill pipe fault diagnosis method based on one-dimensional convolutional neural network
Lie-jun JIN1(),Jian-ming ZHAN1,2,*(),Jun-hua CHEN1,2,Tao WANG2
1. Institute of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
2. School of Mechanical and Energy Engineering, Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, China
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摘要：

为了在钻杆故障早期诊断出钻杆的故障类型，提出一种基于一维卷积神经网络的钻杆故障诊断模型，对模型的结构和参数进行详细地设计与分析. 参考现有的卷积神经网络模型，结合钻杆的工作特性以及感受野的原理，设计模型的卷积层和池化层的层数、卷积核的大小以及滑动步长. 该模型省去了对故障信号特征提取的过程，比先前的钻杆故障诊断有更高的诊断准确率. 该模型在不同转速工况下和不同土质工况下均具有较强的适应性和抗噪能力.
关键词： 钻杆故障诊断;  一维卷积神经网络;  感受野;  适应性;  抗噪能力    
Abstract:

A drill pipe fault diagnosis model based on one-dimensional convolutional neural network was proposed in order to diagnose the fault type of drill pipe failure early; the structure as well as the parameter of the model were designed and analyzed in detail. Referring to the existing convolutional neural network model, the layer number of the convolutional layer as well as the pooling layer of the model, the size of the convolution kernel and the sliding step length were designed combining with the working characteristics of the drill pipe and the principle of the receptive field. The model eliminates the process of extracting fault signal features and has higher diagnostic accuracy than previous drill stem fault diagnosis. Meanwhile, the model has strong adaptability and anti-noise ability under different speed conditions and different soil conditions.
Key words: drill pipe failure detection    one-dimensional convolutional neural network    receptive field    adaptability    anti-noise ability
收稿日期: 2019-02-25 出版日期: 2020-03-05
CLC:  TH 165.3  
基金资助: 国家自然科学基金重点资助项目（U1813223）；浙江省自然科学基金资助项目（LY17E050012）
通讯作者: 詹建明     E-mail: 21725203@zju.edu.cn;zhanjm@nit.zju.edu.cn
作者简介: 金列俊（1995—），男，硕士生，从事机械设备故障诊断、机器学习算法研究. orcid.org/0000-0002-3270-5246. E-mail： 21725203@zju.edu.cn
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引用本文:

金列俊,詹建明,陈俊华,王涛. 基于一维卷积神经网络的钻杆故障诊断[J]. 浙江大学学报(工学版), 2020, 54(3): 467-474.

Lie-jun JIN,Jian-ming ZHAN,Jun-hua CHEN,Tao WANG. Drill pipe fault diagnosis method based on one-dimensional convolutional neural network. Journal of ZheJiang University (Engineering Science), 2020, 54(3): 467-474.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2020.03.006    或    http://www.zjujournals.com/eng/CN/Y2020/V54/I3/467

图 1  卷积神经网络（CNN）结构示意图
网络结构 卷积核大小 步长 卷积核数目 输出大小 填充模式
卷积层 9 2 16 512×16 相同
池化层 2 1 16 256×16 无
卷积层 9 2 32 128×32 相同
池化层 2 1 32 64×32 无
卷积层 9 2 32 32×64 相同
池化层 2 1 32 16×64 无
卷积层 9 1 64 16×128 相同
池化层 2 1 64 8×128 无
全连接 256 1 1 256×1 无
全连接 32 1 1 32×1 无
softmax分类 7 1 1 7×1 无
表 1  一维卷积神经网络参数
图 2  一维卷积神经网络结构图
图 3  钻杆模拟实验平台
图 4  钻杆破坏以及螺母松动示意图
故障类型 故障程度 标签 数据集A 数据集B 数据集C
训练集 验证集 测试集 训练集 验证集 测试集 训练集 验证集 测试集
正常 无 L1 420 140 140 420 140 140 420 140 140
松动螺母 2颗 L2 420 140 140 420 140 140 420 140 140
4颗 L3 420 140 140 420 140 140 420 140 140
6颗 L4 420 140 140 420 140 140 420 140 140
断裂切口长度 56 mm L5 420 140 140 420 140 140 420 140 140
112 mm L6 420 140 140 420 140 140 420 140 140
168 mm L7 420 140 140 420 140 140 420 140 140
合计 2 940 980 980 2 940 980 980 2 940 980 980
表 2  钻杆故障数据集样本数量分布
图 5  1DCNN模型训练精度曲线图
图 6  1DCNN模型训练损失曲线图
实际
预测 		L1 L2 L3 L4 L5 L6 L7
L1 1.000 0 0 0 0 0 0
L2 0 0.975 0 0.025 0 0 0
L3 0 0 1.000 0 0 0 0
L4 0 0.013 0 0.987 0 0 0
L5 0 0 0.038 0 0.962 0 0
L6 0 0 0 0 0 1.000 0
L7 0 0 0 0 0 0 1.000
表 3  1DCNN模型在20 r/min工况下的诊断结果
实际
预测 		L1 L2 L3 L4 L5 L6 L7
L1 1.000 0 0 0 0 0 0
L2 0 1.000 0 0 0 0 0
L3 0 0.013 0.987 0 0 0 0
L4 0 0.013 0 0.962 0 0.025 0
L5 0 0 0 0 0.987 0.013 0
L6 0 0 0 0 0 1.000 0
L7 0 0 0 0 0.025 0 0.975
表 4  1DCNN模型35 r/min工况下的诊断结果
实际
预测 		L1 L2 L3 L4 L5 L6 L7
L1 1.000 0 0 0 0 0 0
L2 0 1.000 0 0 0 0 0
L3 0 0 1.000 0 0 0 0
L4 0 0 0.013 0.987 0 0 0
L5 0.013 0 0.025 0 0.950 0 0.013
L6 0 0 0 0 0 1.000 0
L7 0 0.013 0 0 0 0.050 0.937
表 5  1DCNN模型在50 r/min工况下的诊断结果
算法 accuracy（×100%）
20 r/min 35 r/min 50 r/min
小波-SVM 85.0±2.3 87.5±3.1 91.0±2.8
粒子群-SVM 87.2±4.3 88.3±2.6 88.7±1.8
EWT-BP 80.0±5.0 83.2±3.7 81.7±3.2
EMD-BP 81.7±1.8 83.3±1.9 85.6±2.1
1DCNN 98.9±0.5 98.7±0.2 98.7±0.3
表 6  不同模型的故障诊断准确率对比
训练数据 测试数据 σ/% σave/%
A B 91.3 91.5
C 94.5
B A 90.1
C 89.2
C A 92.7
B 91.6
表 7  1DCNN模型转速适应性诊断结果
材质 σ/%
20 r/min 35 r/min 50 r/min
粗砂 98.9 99.3 98.5
C10水泥 99.1 97.8 97.4
C20水泥 97.9 98.3 98.5
C30水泥 98.9 98.7 98.7
表 8  1DCNN模型在不同土质下的故障诊断准确率对比
图 7  原始样本、噪声样本以及含噪样本示意图
SNR σ/% SNR σ/%
3 99.5 ?1 98.1
2 98.9 ?2 93.3
1 98.6 ?3 86.9
0 98.3 ?4 76.3
表 9  1DCNN模型抗噪性诊断结果
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