Classification and Temporal Analysis of District Heating Leakages in Thermal Images

District heating pipes are known to degenerate with time and in some cities the pipes have been used for several decades. Due to bad insulation or cracks, energy or media leakages might appear. This paper presents a complete system for large-scale monitoring of district heating networks, including methods for detection, classification and temporal characterization of (potential) leakages. The system analyses thermal infrared images acquired by an aircraft-mounted camera, detecting the areas for which the pixel intensity is higher than normal. Unfortunately, the system also finds many false detections, i.e., warm areas that are not caused by media or energy leakages. Thus, in order to reduce the number of false detections we describe a machine learning method to classify the detections. The results, based on data from three district heating networks show that we can remove more than half of the false detections. Moreover, we also propose a method to characterize leakages over time, that is, repeating the image acquisition one or a few years later and indicate areas that suffer from an increased energy loss. INTRODUCTION Distribution of heat to homes and industries through district heating networks is today one of the most common heating sources in Swedish and Nordic cities. However, the pipes degenerate with time [3] and due to bad insulation or cracks, energy or media leakages might appear. Bad insulation can, for example, be caused by cracks in the outer protective shell, allowing water to enter the insulation layer thus significantly reducing the insulation effect. In addition to being expensive for the network owner, the loss of media or energy also has negative impact on the environment [4]. Therefore, it is of great interest to the owner to have efficient and reliable methods for leakage detection, especially when considering the fact that the pipes generally are placed underground, it is very expensive to dig in the wrong place. Moreover, major leakages of 50 m 3 media or more per day may also cause the ground to collapse due to erosion, whereby large amounts of media at boiling temperature are exposed. This paper presents methods for detection, classification and temporal characterization of such leakages. We have used a commercially available system for large-scale airborne thermal image acquisition, acquiring data from several Nordic cities. For detection of potential leakages, we use the method previously published by Friman et al. [1]. The method is used to analyse the acquired imagery, finding and indicating the areas for which the pixel intensity (temperature) is higher than normal. Apart from the sought-for media and energy leakages, there are several types of objects and phenomena that give rise to such detections. Examples are areas that, for some reason, are warmer than their surroundings, for example, chimneys, cars and heat leakages from buildings. In a large city, there might be several thousands of false detections. Thus, we want to reduce these false detections as much as possible while maintaining the number of true detections at a fixed level (we use 99%). In order to achieve this goal, we follow a two-step classification procedure, as proposed in [2]: 1. Extract building locations from publically available geographic information, and remove all detections located on buildings. 2. Extract image features and use a machine learning method to classify detections as true (media/energy) or false detections. Next, we propose a novel method for temporal characterization and visualization of the energy loss of the network. Long-term degradation of a pipe might not be detected as a single leakage, but by analysing larger areas and compare the radiated energy from two flights separated by one or a few years, such effects can be detected. The area covering the district heating network is divided into square cells and the comparison of energy loss is done for each cell individually.