Indirect Fault Diagnosis of Spool Sticking in Fine Blanking Press Directional Control Valve Based on FBG Sensor

doi:10.3969/j.issn.1004-132X.2026.09.004

Abstract

Abstract:

As high-end manufacturing equipment， the fine blanking press was directly affected by the health of its hydraulic valves in terms of service life and machining accuracy. Current research on diagnosing spool sticking faults has primarily relied on manually constructed testing platforms， lacking validation in industrial environments. Therefore， a fault diagnosis method was proposed for spool sticking in the rapid approach system directional control valve of the fine blanking press， based on FBG sensor and 1D convolutional neural network （1DCNN）. Using the HFB-500 fine blanking press as the test subject， AMESim simulations were conducted to investigate the impact of spool sticking on the slide and valve， thereby determining the measurement points and data acquisition methods for the FBG sensor. Subsequently， experimental data were processed through Savitzky-Golay（SG） filtering and baseline drift removal. The results demonstrate that the FBG sensor may accurately capture the characteristic increase in rapid approach time during spool sticking faults. Finally， a classification accuracy of 99.9% was achieved by 1DCNN for spool sticking faults. The superior performance of 1DCNN was verified through comparative analyses with support vector machine（SVM） and long short-term memory（LSTM） models， demonstrating its capability to accurately identify spool sticking faults.

Key words: fiber Bragg grating（FBG） sensor, fine blanking press, directional valve, spool sticking

摘要：

液压式精冲机作为高端制造装备，其液压阀的健康与否关乎其寿命与加工精度。目前对阀芯卡滞故障诊断研究主要采用人工搭建的平台开展试验，未在工业场景得到验证。为此，提出了一种基于光纤布拉格光栅（FBG）传感器与一维卷积神经网络（1DCNN）的精冲机快进系统换向阀阀芯卡滞故障诊断方法。以HFB-500精冲机为对象，通过AMESim仿真研究阀芯卡滞对滑块与阀的影响进而确定FBG传感器的测点与数据采集方法，而后将试验数据通过Savitzky-Golay（SG）滤波以及去除基线漂移处理，结果表明FBG传感器能够准确捕获阀芯卡滞故障下快进时间会增加这一特征。最后利用1DCNN对阀芯卡滞故障分类，准确率达99.9%，通过与支持向量机（SVM）及长短时记忆网络（LSTM）分类性能的对比验证了1DCNN的优异性能，能够准确识别阀芯卡滞故障。

关键词: 光纤光栅传感器, 精冲机, 换向阀, 阀芯卡滞

CLC Number:

TG385

WEI Wenting, ZHANG Shuzhao, LIU Yanxiong, HUA Lin. Indirect Fault Diagnosis of Spool Sticking in Fine Blanking Press Directional Control Valve Based on FBG Sensor[J]. China Mechanical Engineering, 2026, 37(9): 2115-2123.

魏文婷, 张书照, 刘艳雄, 华林. 基于光纤光栅传感器的精冲机换向阀阀芯卡滞间接故障诊断[J]. 中国机械工程, 2026, 37(9): 2115-2123.

Figures/Tables 20

Fig.1 Hydraulic system of HFB-500 hydraulic fine blanking press

Fig.2 Three-position four-way solenoid directional valve for rapid traverse system

Fig.3 Data acquisition system

Fig.4 SG filter parameter selection and denoisingtime-domain plot

Fig.5 Time-domain waveforms of the signal with SGfiltering and baseline correction

Fig.6 Architecture diagram of 1DCNN

Tab.1 Architecture and parameters of 1DCNN

网络层	参数
卷积层C1	卷积核个数为32，卷积核尺寸为3，步长为1，激活函数为ReLU
最大池化层M1	池化窗口大小为2
卷积层C2	卷积核个数为64，卷积核尺寸为3，步长为1，激活函数为ReLU
最大池化层M2	池化窗口大小为2
卷积层C3	卷积核个数为128，卷积核尺寸为3，步长为1，激活函数为ReLU
最大池化层M3	池化窗口大小为2
展平层F1	将池化层多维的输出向量转换为一维的向量
全连接层D1	神经元个数为128，激活函数为ReLU
全连接层D2	神经元个数为3，激活函数为softmax

Fig.7 Simulation curve of single-stroke slide displacement and fine blanking phase division

Fig.8 Effect of different sticking conditions on slider

Tab.2 Time of each fine blanking phase under different spool sticking conditions obtained from simulation

阀芯卡滞状态	快进时间	检测和精冲时间	快退时间
正常	1.1	1.1	1.3
轻微卡滞	1.2	1.1	1.3
严重卡滞	1.3	1.1	1.3

Fig.9 Effect of different spool sticking conditions on valve

Fig.10 Single-stroke time-domain diagram acquired by FBG sensor

Tab.3 Time of each fine blanking phase under different spool sticking conditions obtained from FBG sensor

阀芯卡滞状态	快进时间	检测与精冲时间	快退时间
正常	1.2	1.2	1.4
轻微卡滞	1.3	1.2	1.4
严重卡滞	1.4	1.2	1.4

Tab.4 Errors between FBG sensor measurements and simulation results at various stages

阀芯卡滞状态	快进误差	检测与精冲误差	快退误差
正常	8.33	8.33	7.14
轻微卡滞	7.69	8.33	7.14
严重卡滞	7.14	8.33	7.14

Tab.5 Sample and label counts

故障类型	训练样本数	测试样本数	标签
正常	4000	1000	0
轻微卡滞	4000	1000	1
严重卡滞	4000	1000	2

Fig.11 Training loss and accuracy curves of 1DCNN in the final fold

Fig.12 Average accuracy and standard deviation of each model under 5-fold cross-validation

Fig.13 Test confusion matrix

Fig.14 Classification metrics for each model

Fig.15 The t-SNE clustering visualization results of each model

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