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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2019, Vol. 53 Issue (9): 1663-1673    DOI: 10.3785/j.issn.1008-973X.2019.09.004
Mechanical Engineering     
Intelligent recognition method for working-cycle state of hydraulic excavator
Jie HUANG1,2(),Dong WANG1,3,*(),Xin-qing WANG1,Qin YIN1,Fa-ming SHAO1
1. College of Field Engineering, Army Engineering University of PLA, Nanjing 210007, China
2. Engineering University of PAP, Urumqi Campus, Urumqi 830049, China
3. Second Institute of Engineering Research and Design,Southern Theatre Command, Kunming 650222, China
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Abstract  

A novel intelligent recognition method based on the combination of PCA-SVM and feature extraction with sliding time-window was proposed to recognize each state in working cycle stages of hydraulic excavator. The pressure signals of two pumps were taken as recognizing object, and small waveform segments of each state in a working cycle of hydraulic excavator were intercepted by adopting sliding time-window method. The corresponding time domain and frequency domain parameters of all waveform segments were extracted and the eigenvalue normalization was carried out. The normalized set was used as input feature set after reduction of dimensionality with principal component analysis (PCA), then state classification was conducted by adopting the SVM algorithm, and the effects of sliding time-window width and overlap rate to the recognition accuracy were discussed, respectively. Besides, a real-time correction strategy was introduced to automatically check and correct the direct recognition results; the misjudgment results against the logic rule of excavator operation was verified, and the recognition accuracy was improved from 80.85% to 89.36%. Results prove that the proposed method can realize the state recognition of each stage in a working cycle of hydraulic excavator effectively and accurately.

Key words： hydraulic excavator      working cycle      state recognition      SVM      real-time correction strategy     
Received: 22 July 2018      Published: 12 September 2019
CLC:  TU 621  
Corresponding Authors: Dong WANG     E-mail: huangjie051501@126.com;dyhkxydfbb@163.com
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Jie HUANG
Dong WANG
Xin-qing WANG
Qin YIN
Fa-ming SHAO
Cite this article:

Jie HUANG,Dong WANG,Xin-qing WANG,Qin YIN,Fa-ming SHAO. Intelligent recognition method for working-cycle state of hydraulic excavator. Journal of ZheJiang University (Engineering Science), 2019, 53(9): 1663-1673.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2019.09.004     OR     http://www.zjujournals.com/eng/Y2019/V53/I9/1663


液压挖掘机作业循环状态智能识别方法

为实现液压挖掘机作业循环阶段状态的自动识别，提出采用时窗滑移特征提取与PCA-SVM相结合的方法. 以液压挖掘机2个主泵压力信号为研究对象，采用时窗滑移截取各阶段状态的小段波形，提取所有截取波段相应的时频参数并进行特征值归一化，经PCA降维处理后作为输入特征集，采用SVM进行状态分类，并分别讨论时窗宽度与重叠率对识别准确率的影响. 引入即时校正策略，对直接识别结果进行自动检验校正，纠正不符合挖掘机循环作业逻辑规则的误判结果，从而使识别准确率由80.85%提高到89.36%. 实验结果表明，所提方法能准确有效地实现液压挖掘机作业循环各阶段状态的自动识别.


关键词： 液压挖掘机,  作业循环,  状态识别,  支持向量机（SVM）,  即时校正策略 
Fig.1 Major movements of hydraulic excavator
Fig.2 Installation diagram of two pump pressure sensors
Fig.3 Diagram of hydraulic excavator’s working cycle
Fig.4 Control schematic of hydraulic system
Fig.5 Pressure waveform of digging stage
Fig.6 Pressure waveform of lifting stage
Fig.7 Identification process of working cycle state of hydraulic excavator
Fig.8 Schematic diagram of time-window sliding process
Fig.9 Feature set disposed by PCA
$\Delta t$/s Q1/ % Q2/ % $\Delta t$/s Q1/ % Q2/ %
0.1 45.05 45.33 0.6 76.56 82.79
0.2 70.31 77.82 0.7 75.64 79.11
0.3 73.41 77.75 0.8 76.88 84.78
0.4 73.36 74.55 0.9 76.67 81.86
0.5 80.22 86.40 1.0 74.76 81.97
Tab.1 Comparison of detailed results of direct recognition and correction when time-window width changing
$\varphi $ Q1/ % Q2/ % $\varphi $ Q1/ % Q2/ %
0 80.22 86.40 0.500 76.64 82.57
0.125 80.72 88.01 0.625 75.28 82.69
0.250 81.21 88.50 0.750 76.14 82.69
0.375 78.74 85.66 ? ? ?
Tab.2 Detailed results of direct recognition and correction when overlap changing
实际状态 Q $/$% N Nr
S1 86.86 175 152
S2 77.42 62 48
S3 78.43 102 80
S4 62.32 69 43
S5 53.20 94 50
S6 88.62 167 148
S7 97.14 140 136
总计 81.21 809 657
Tab.3 Direct recognition details of optimal values
w/Ne 误判状态
S1 S2 S3 S4 S5 S6 S7
实
际
状
态 		S1 ? 0.057 1/10 ? ? ? 0.068 6/12 0.005 7/1
S2 0.012 9/8 ? ? 0.080 6/5 ? 0.016 1/1 ?
S3 ? ? ? ? 0.215 7/22 ? ?
S4 0.029 0/2 0.275 4/19 ? ? ? 0.058 0/4 0.014 5/1
S5 0.053 2/5 ? 0.414 9/39 ? ? ? ?
S6 0.060 0/10 ? 0.018 0/3 0.006 0/1 ? ? 0.030 0/5
S7 ? ? ? ? ? 0.028 6/4 ?
总计 0.030 9/25 0.035 8/29 0.051 9/42 0.007 4/6 0.027 2/22 0.025 9/21 0.008 6/7
Tab.4 Misjudgment details of optimal values
Fig.10 Direct recognition result and real state of typical working cycle
Fig.11 Real-time correction results and real state
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