指针式仪表智能识别技术研究

指针式仪表目前广泛存在于电力、工业制造等领域。指针式仪表的正确识读,对于保障系统的安全生产运行具有重要意义。传统指针式仪表识别采用人工读取的方式,较为费时费力,容易出现错检、漏检的问题。与之相比,采用机器视觉技术来进行指针式仪表识别,可以有效降低人工成本,提升检测效率,具有较为明显的优势。为了降低人工读取指针式仪表的工作量,提升指针式仪表的智能化识别水平,该研究提出一种指针式仪表识智能识别方法。该方法首先通过图像处理中的灰度化、高斯滤波、自适应阈值、细化处理、边缘检测、霍夫直线检测等方法得到指针的指向角度。之后基于深度学习的文字识别方法得到指针式仪表的关键字的坐标,将关键字的坐标转化为关键字的角度。最后通过角度法,计算指针的指向角度与关键字的角度关系得到检测结果。从实验结果来看,该识别方法读取的数据与人工读取相比,相对误差均在5%以内,可行且实用。在具有一定程度遮挡、角度偏移、光照影响下,也能够有效识别,具有较强的鲁棒性。

Mesomechanics coal experiment and an elastic-brittle damage model based on texture features

To accurately describe damage within coal, digital image processing technology was used to determine texture parameters and obtain quantitative information related to coal meso-cracks. The relationship between damage and mesoscopic information for coal under compression was then analysed. The shape and distribution of damage were comprehensively considered in a defined damage variable, which was based on the texture characteristic. An elastic-brittle damage model based on the mesostructure information of coal was established. As a result, the damage model can appropriately and reliably replicate the processes of initiation, expansion, cut-through and eventual destruction of microscopic damage to coal under compression. After comparison, it was proved that the predicted overall stress-strain response of the model was comparable to the experimental result.