【作者】 姬广伟；

【导师】 王奎升；

【作者基本信息】 北京化工大学 ， 机械设计及理论， 2005， 硕士

【摘要】 抽油机井故障诊断是采油工程中的重要研究内容,它对于提高整个抽油系统的工作效率和自动化水平具有非常重要的意义。目前对抽油机井装置的故障诊断主要是利用示功图对抽油井装置的各种工作状况进行分析和判断,根据地面示功图,利用数学模型推算出井下示功图。 计算机图像处理技术和模式识别技术的发展在很多科学和技术领域中得到了广泛的重视,推动了人工智能系统的发展。 人工神经网络技术的进步则为此提供了一个很有利的契机。它的并行处理和自组织自学习能力、高度的稳健性和容错性、高度的映射能力以及分类计算能力,为故障诊断提供了全新的理论方法和实现手段。 本文参照人工进行示功图的获取途径和研究成果,提出了利用计算机图像处理技术对示功图进行模式识别的思路。对经过扫描之后获取的示功图进行一系列图像处理过程,最后转换为适合人工神经网络输入的特征向量,为人工神经网络的应用提供了基础。 选用BP网络,参照前人提出的理论和经验,结合本文实际情况,确定了网络结构。采用动态变步长、样本批处理等一系列方法,在一定程度上改善了网络结构,提高了网络的训练精度。更多还原

【Abstract】 The fault diagnoses of the pumping unit is an important part of the research in the field of petroleum production. It is of much importance to enhance the efficiency and automatization level of the whole pumping system. At present, The principle of this technology is to analyze and judge the condition of the pumping unit utilizing its dynagraphs. Based on the dynagraphs of ground and the mathematic model, was the downhole dynagraphs calculated.The computer image processing technology and pattern recogniting technology have achieved much progress in lots of fields and been paid much more attention as time going on. They have promoted the development of imitated intelligence.The development of artificial neural network(ANN) provides a valuable chance to change this situation. It is distinguished by parallel processing, self-study ability, high stability, accommodation and reflection ability and it can be calculated respectively. All of these virtues can providean new theory and method for fault diagnoses.The combination of the technology of the computer image process, pattern recognition and ANN have greatly improved the ability to recognize the dynagraphs of the whole pumping unit.Based on the technology of computer image processing, A method to execute the process of pattern recognition to the dynagraph is put forward in this thesis. The dynagraphs are dealt with a series of image processing, after they are achieved and converted to the proper vector which will be input to the ANN. A basis for the practical use of the ANN has been offered.The BP network is adopted based on the theory and experience of some researchers. The structure of the network is decided based on the practical situation of this thesis. A series of methods of dynamic alterable step and disposal of the specimens of are used. The structure of the network enhanced as well as the training efficiency of the network.A practical example by changing the training number to a dynagraph has been given. They have been showed the correctness of the choosing of the type of network and the error of training can satisfy the need.The image processing is designed for all the colorful images. For all the dynagraphs, they would deal with following processes: the grayscale transformation, smoothing, sharpening, unity of size, binary and plot detaching.更多还原