Automated energy performance diagnosis of HVAC systems by the 4S3F method

Arie Taal, L.C.M. Itard

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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In practice, faults in building installations are seldom noticed because automated systems to diagnose such faults are not common use, despite many proposed methods: they are cumbersome to apply and not matching the way of thinking of HVAC engineers. Additionally, fault diagnosis and energy performance diagnosis are seldom combined, while energy wastage is mostly a consequence of component, sensors or control faults. In this paper new advances on the 4S3F diagnose framework for automated diagnostic of energy waste in HVAC systems are presented. The architecture of HVAC systems can be derived from a process and instrumentation diagram (P&ID) usually set up by HVAC designers. The paper demonstrates how all possible faults and symptoms can be extracted on a very structured way from the P&ID, and classified in 4 types of symptoms (deviations from balance equations, operational states, energy performances or additional information) and 3 types of faults (component, control and model faults). Symptoms and faults are related to each other through Diagnostic Bayesian Networks (DBNs) which work as an expert system. During operation of the HVAC system the data from the BMS is converted to symptoms, which are fed to the DBN. The DBN analyses the symptoms and determines the probability of faults. Generic indicators are proposed for the 4 types of symptoms. Standard DBN models for common components, controls and models are developed and it is demonstrated how to combine them in order to represent the complete HVAC system. Both the symptom and the fault identification parts are tested on historical BMS data of an ATES system including heat pump, boiler, solar panels, and hydronic systems. The energy savings resulting from fault corrections are estimated and amount 25%. Finally, the 4S3F method is extended to hard and soft sensor faults. Sensors are the core of any FDD system and any control system. Automated diagnostic of sensor faults is therefore essential. By considering hard sensors as components and soft sensors as models, they can be integrated into the 4S3F method.
Original languageEnglish
Title of host publicationCLIMA 2022 - 14th REHVA HVAC World Congress
Subtitle of host publicationEye on 2030, Towards digitalized, healthy, circular and energy efficient HVAC
PublisherTU Delft OPEN
Number of pages7
Publication statusPublished - 2022
EventCLIMA 2022 - 14th REHVA HVAC World Congress: Towards digitalized, healthy, circular and energy efficient HVAC - Rotterdam, Netherlands
Duration: 22 May 202225 May 2022


ConferenceCLIMA 2022 - 14th REHVA HVAC World Congress
Internet address


  • 4S3F
  • FDD
  • Energy performance
  • DBN
  • P&ID


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