Towards automated fault detection and diagnosis in district heating customers: generation and analysis of a labeled dataset with ground truth

This study is built upon the current research in fault detection and diagnosis (FDD) in district heating (DH). Presently the main studies in this field are on interviewing DH companies regarding their FDD practices or developing algorithms to automatically detect faults in DH substations. Particularly, Gadd and Werner (2015) present a framework based on three indicators to detect anomalous patterns in the DH customer substations operation by analyzing energy and temperature data from smart heat meters (SHM). These indicators are unexpected heat load patterns according to the building's typology (I1), low annual average in supply and return temperature difference (I2), and poor correlation between energy usage and outdoor temperature (I3).Today, some DH companies apply these indicators for automated fault detection while the diagnosis process is still carried out by a human user. This is due to the lack of measurements, properly labeled system data, or infrastructure to work with advanced FDD systems.This study attempts to investigate this matter by:a) Coupling approximately 300 fault reports written by technicians with measurements from SHM data.b) Providing an overview of typical faults occurring in DH customers.c) Testing the abovementioned indicators to assess their efficacy for FDD purposes.In several European countries like Denmark, more than 50% of their building stock is connected to the DH grid. Moreover, countries outside Europe, like Russia and China, are also integrating this system into their cities. Even if many nations are implementing this solution, research shows that the DH sector must evolve, termed 4th generation district heating (4GDH), by lowering the supply temperature of the heated fluid.One of the measures to lower the supply temperature is effectively and frequently detecting and fixing the existing and future faults in the DH systems. According to Gadd and Werner (2015), on a small buildings sample, 74% of buildings displayed indicators that might imply a faulty substation, meaning a vast possibility to decrease the supply temperatures by improving the network's heat systems.The relevance of this study is, to the best of the authors' knowledge, the first large labeled faults dataset that goes to the component level in the DH substations coupled with energy measurements. Analyzing such datasets allows for developing future automated FDD algorithms, reducing the large customer's energy usage over time, decreasing the diagnosis cost related to technician inspections, and detecting anomalies before the system's complete failure.All analyzed buildings had low supply and return temperature differences (<15⁰C), thus the reason for being visited by the DH company technicians. However, some buildings displayed unexpected heating patterns (I1), while others had a poor correlation between energy demand and outdoor temperature (I3). According to Gadd and Werner (2015), I1 is mainly caused by human factors and wrong building controls. In contrast, I3 is primarily produced by a fault in the substation. Specifically in this dataset, most faults in the present dataset are due to valve problems (e.g., incorrect regulation or broken) and incorrect user heating practices (improper radiators and towel dryers' usage), significantly decreasing the temperature difference (I2).Also, during the assessment visit, the wrong settings in the system were adjusted, increasing the temperature differences significantly (15-25⁰C more), and the customers received advice to use their heating systems efficiently (e.g., opening windows with heating systems turned off). The results of these interventions helped increase the DH system's efficiency and lowered customers' heating expenses.This demonstrates that having detailed fault assessments in the DH sector benefits heating provider companies and their customers. Additionally proving, that assessing and correcting faults in the DH grid is a present action measure to move towards 4GDH systems.