CNG复合材料气瓶长期处于交变载荷作用, 易于发生疲劳损伤, 形成内部缺陷, 造成强度下降, 影响使用安全。含内部缺陷的复合材料气瓶在宏观上无明显形变, 表观上难以直接进行缺陷检测。在目前的气瓶检测项目中, 缺乏快速有效的缠绕层内部缺陷检测手段, 可能造成含有内部缺陷气瓶的漏检。本文针对 CNG复合材料气瓶检测存在的关键问题, 结合现有气瓶检测标准和工艺, 提出了一种基于气瓶内部蒸汽冲洗过程表面热像的缺陷检测方案。该方案以气瓶冲洗过程的蒸汽为气瓶的内部热激励, 基于红外热像仪采集的气瓶表面瞬态温度分布, 利用人工神经网络实现气瓶缠绕层缺陷的定位和定量识别。实验研究表明, 人工神经网络能够精确地进行气瓶缠绕层缺陷的定位和定量识别且识别效率较高, 适于气瓶的在线检测。

CNG composite gas cylinders are subject to alternating loads for a long time, making them prone to fatigue damage and internal defect formation. These defects decrease strength and cause safety concerns during use. Composite gas cylinders containing internal defects show no obvious macroscopic deformation, and thus, it is difficult to directly detect them. The current gas cylinder testing program lacks an effective means to rapidly detect internal defects in the winding layer, which may result in missed inspections of cylinders containing internal defects. This paper proposes a defect detection scheme based on a combination of the existing key problems associated with CNG composite gas cylinder testing and the existing gas cylinder testing standards and processes. The proposed defect detection scheme uses surface thermal imagery of the steam cleaning process inside the gas cylinder. The scheme uses the steam in the cylinder flushing process as the internal thermal excitation of the cylinder. Using the transient temperature distribution of the cylinder surface recorded by an infrared camera, an artificial neural network is applied to locate and quantitatively identify the defects in the cylinder winding layer. This experimental research shows that the artificial neural network can accurately locate and quantitatively identify the defects in the cylinder winding layer with high recognition efficiency, which is suitable for online detection of gas cylinders.