Al-Nasser, HebaHebaAl-NasserAl-Zuriqat, ThamerThamerAl-ZuriqatDragos, KosmasKosmasDragosChillón Geck, CarlosCarlosChillón GeckSmarsly, KayKaySmarsly2024-11-012024-11-012024-07-19Smart Materials and Structures 33 (8): 085026 (2024)https://hdl.handle.net/11420/49891Fault diagnosis (FD), comprising fault detection, isolation, identification and accommodation, enables structural health monitoring (SHM) systems to operate reliably by allowing timely rectification of sensor faults that may cause data corruption or loss. Although sensor fault identification is scarce in FD of SHM systems, recent FD methods have included fault identification assuming one sensor fault at a time. However, real-world SHM systems may include combined faults that simultaneously affect individual sensors. This paper presents a methodology for identifying combined sensor faults occurring simultaneously in individual sensors. To improve the quality of FD and comprehend the causes leading to sensor faults, the identification of combined sensor faults (ICSF) methodology is based on a formal classification of the types of combined sensor faults. Specifically, the ICSF methodology builds upon long short-term memory (LSTM) networks, i.e. a type of recurrent neural networks, used for classifying ‘sequences’, such as sets of acceleration measurements. The ICSF methodology is validated using real-world acceleration measurements from an SHM system installed on a bridge, demonstrating the capability of the LSTM networks in identifying combined sensor faults, thus improving the quality of FD in SHM systems. Future research aims to decentralize the ICSF methodology and to reformulate the classification models in a mathematical form with an explanation interface, using explainable artificial intelligence, for increased transparency.en0964-172620248Institute of Physics Publ.https://creativecommons.org/licenses/by/4.0/classification modelsfault diagnosisidentification of combined sensor faultslong short-term memory networkssensor faultsstructural health monitoringMLE@TUHHTechnology::624: Civil Engineering, Environmental EngineeringComputer Science, Information and General Works::006: Special computer methodsJournal Article10.15480/882.1360210.1088/1361-665X/ad61a410.15480/882.13602Journal Article