TY - GEN
T1 - An SHM Data-Driven Methodology for the Remaining Useful Life Prognosis of Aeronautical Subcomponents
AU - Galanopoulos, Georgios
AU - Eleftheroglou, Nick
AU - Milanoski, Dimitrios
AU - Broer, Agnes
AU - Zarouchas, Dimitrios
AU - Loutas, Theodoros
PY - 2023
Y1 - 2023
N2 - Prognosis of the Remaining Useful Life (RUL) of a structure from Structural Health Monitoring data is the ultimate level in the SHM hierarchy. Reliable prognostics are key to a Condition Based Maintenance paradigm for aerospace systems and structures. In the present work, we propose a methodology for RUL prognosis of generic aeronautical elements i.e. single stringered composite panels subjected to compression/compression fatigue. Strain measurements are utilized in this direction via FBG sensors bonded to the stiffener feet. The strain data collected during the fatigue life are processed and used for the RUL prognosis. In order to accomplish this task, it is essential to produce Health Indicators (HIs) out of raw strain that can properly capture the degradation process. To create such HIs a new pre/post-processing technique is employed and a variety of different HIs are developed. The quality of the HIs can enhance the performance of the prognostic algorithms, hence a fusion methodology is proposed using genetic algorithms. The resulted fused HI is used for the RUL estimation of the SSCPs. Gaussian processes and Hidden Semi Markov Models are employed for RUL prognosis and their performance is compared. Despite the complexity the raw data we demonstrate the feasibility of successful RUL prognostics in a SHM-data driven approach.
AB - Prognosis of the Remaining Useful Life (RUL) of a structure from Structural Health Monitoring data is the ultimate level in the SHM hierarchy. Reliable prognostics are key to a Condition Based Maintenance paradigm for aerospace systems and structures. In the present work, we propose a methodology for RUL prognosis of generic aeronautical elements i.e. single stringered composite panels subjected to compression/compression fatigue. Strain measurements are utilized in this direction via FBG sensors bonded to the stiffener feet. The strain data collected during the fatigue life are processed and used for the RUL prognosis. In order to accomplish this task, it is essential to produce Health Indicators (HIs) out of raw strain that can properly capture the degradation process. To create such HIs a new pre/post-processing technique is employed and a variety of different HIs are developed. The quality of the HIs can enhance the performance of the prognostic algorithms, hence a fusion methodology is proposed using genetic algorithms. The resulted fused HI is used for the RUL estimation of the SSCPs. Gaussian processes and Hidden Semi Markov Models are employed for RUL prognosis and their performance is compared. Despite the complexity the raw data we demonstrate the feasibility of successful RUL prognostics in a SHM-data driven approach.
KW - Composite panels
KW - Fibber Bragg Gratings
KW - Health Indicators
KW - RUL prognosis
KW - Structural Health Monitoring
UR - http://www.scopus.com/inward/record.url?scp=85134307838&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-07254-3_24
DO - 10.1007/978-3-031-07254-3_24
M3 - Conference contribution
AN - SCOPUS:85134307838
SN - 9783031072536
T3 - Lecture Notes in Civil Engineering
SP - 244
EP - 253
BT - European Workshop on Structural Health Monitoring, EWSHM 2022, Volume 1
A2 - Rizzo, Piervincenzo
A2 - Milazzo, Alberto
PB - Springer Science and Business Media Deutschland GmbH
T2 - 10th European Workshop on Structural Health Monitoring, EWSHM 2022
Y2 - 4 July 2022 through 7 July 2022
ER -