针对抽油机示功图人工目测准确性低，神经网络自动识别网络训练复杂、计算速度慢的现状，研究了基于分形盒维数的示功图自动识别技术。验证了分形盒维数理论用于示功图识别的可行性，提供自动识别步骤，并对无杆抽油机正常、供液不足、游动阀泄漏以及油管漏失4种工况下的示功图进行识别。结果表明：基于分形盒维数的示功图自动识别技术能对以上4种工况下的示功图进行准确有效的识别，自动识别效果在低噪声时更为准确，噪声较高时，为保证自动识别的准确性需要先进行降噪处理。研究提出了无杆抽油机示功图自动识别的新方法，对于提升无杆抽油机运行状态监测的自动化、智能化分析水平具有重要意义。%In view of the status of low visual inspection accuracy for pumping unit indicator diagram, complex training in automatic identiifcation neutral network and low computation speed, this studied the automatic identiifcation technique for indicator diagram based on fractal box dimensions, veriifed the viability of indicator diagram by fractal box dimensions theory, provided automatic identiifcation steps and identiifed the indicator diagram under four working conditions:normal and insufifcient liquid supply by rodless pumping unit, leaking at traveling valve and tubing leaking. The research result shows that the indicator identiifcation technique based on fractal box dimensions can accurately identify the indicator diagrams under the above four working conditions and more accurately when at low noise level;when the noise level is relatively high, noise reduction processing needs to be performed in order to ensure accurate auto-matic identiifcation. The research presents a new method for automatic identiifcation of pumping unit indicator diagram and is of great signiifcance to improve the level of automation of pumping unit running monitoring and intelligent analysis.
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