Automated and real-time anomaly indexing for district heating maintenance decision support system

Faults are estimated to cause a significant amount of energy consumption and waste in district heating systems (DHS), and lowering this is critical to reduce costs and energy waste, but also to increase the security of supply. There are multiple examples of data-driven fault detection and diagnosis methods being employed in DHS to detect faults, however, it has been observed that a great number of faults are detected and it is not sustainable to investigate all of them. This paper, therefore, proposes implementing Chernoff bound, which is a method that can determine the fault probability level of detected faults to filter out insignificant faults. This methodology is integrated into a general continuous fault detection maintenance framework which can utilize multiple fault detection and diagnosis methods. The Chernoff bound methodology is modified for better generalization and implementation, with regard to the input data and other methods employed. The methodology has been applied to a DHS case and has proven to be able to filter out insignificant faults while still retaining a known fault, and identifying other anomalies in the operation. This framework and the introduction of Chernoff bound as a means to rank the confidence of the various detection of faults, can help maintenance crews allocate their time and effort in a better way, to only focus on the most potential critical faults. The data used as a case study is of 1-hour frequency, and some, but not all faults can be observed in such data. The optimal data is hypothesized to be of higher frequency, increasing the capabilities of the framework.