Liquid leakage from pipelines is a critical issue in large-scale process plants. Damage in pipelines affects the normal operation of the plant and increases maintenance costs. Furthermore, it causes unsafe and hazardous situations for operators. Therefore, the detection and localization of leakages is a crucial task for maintenance and condition monitoring. Recently, the use of infrared (IR) cameras was found to be a promising approach for leakage detection in large-scale plants. IR cameras can capture leaking liquid if it has a higher (or lower) temperature than its surroundings. In this paper, a method based on IR video data and machine vision techniques is proposed to detect and localize liquid leakages in a chemical process plant. Since the proposed method is a vision-based method and does not consider the physical properties of the leaking liquid, it is applicable for any type of liquid leakage (i.e., water, oil, etc.). In this method, subsequent frames are subtracted and divided into blocks. Then, principle component analysis is performed in each block to extract features from the blocks. All subtracted frames within the blocks are individually transferred to feature vectors, which are used as a basis for classifying the blocks. The k-nearest neighbor algorithm is used to classify the blocks as normal (without leakage) or anomalous (with leakage). Finally, the positions of the leakages are determined in each anomalous block. In order to evaluate the approach, two datasets with two different formats, consisting of video footage of a laboratory demonstrator plant captured by an IR camera, are considered. The results show that the proposed method is a promising approach to detect and localize leakages from pipelines using IR videos. The proposed method has high accuracy and a reasonable detection time for leakage detection. The possibility of extending the proposed method to a real industrial plant and the limitations of this method are discussed at the end.

管道漏液是大型加工厂的一个关键问题。管道损坏会影响工厂的正常运行并增加维护成本。此外，它会给操作员带来不安全和危险的情况。因此，泄漏的检测和定位是维护和状态监测的关键任务。最近，发现使用红外 (IR) 摄像机是一种很有前途的大型工厂泄​​漏检测方法。如果泄漏的液体温度高于（或低于）周围环境，红外热像仪可以捕获它。在本文中，提出了一种基于红外视频数据和机器视觉技术的方法来检测和定位化工厂中的液体泄漏。由于所提出的方法是基于视觉的方法，并且没有考虑泄漏液体的物理特性，因此它适用于任何类型的液体泄漏（即水、油等）。在这种方法中，后续帧被减去并分成块。然后，在每个块中进行主成分分析以从块中提取特征。块内的所有相减帧都单独转移到特征向量，用作对块进行分类的基础。这 用作对块进行分类的基础。这 用作对块进行分类的基础。这k-最近邻算法用于将块分类为正常（无泄漏）或异常（有泄漏）。最后，在每个异常块中确定泄漏的位置。为了评估该方法，考虑了具有两种不同格式的两个数据集，包括由红外摄像机捕获的实验室演示设备的视频片段。结果表明，所提出的方法是一种使用红外视频检测和定位管道泄漏的有前途的方法。所提出的方法对于泄漏检测具有较高的准确度和合理的检测时间。最后讨论了将所提出的方法扩展到实际工业工厂的可能性以及该方法的局限性。