TY - GEN
T1 - Data-driven modeling for aviation safety diagnosis and prognosis
AU - Zhang, Xiaoge
AU - Kong, Yingxiao
AU - Subramanian, Abhinav
AU - Mahadevan, Sankaran
N1 - Funding Information:
The research reported in this paper was supported by funds from NASA University Leadership Initiative grant (Grant No. NNX17AJ86A, Project Technical Monitor: Dr. Kai Goebel) through subcontract to Arizona State University (Principal Investigator: Dr. Yongming Liu). The support is gratefully acknowledged.
Publisher Copyright:
© 2018 Prognostics and Health Management Society. All rights reserved.
PY - 2018/9/24
Y1 - 2018/9/24
N2 - The safety of the air transportation system is affected by a variety of uncertainties arising from multiple sources. This paper investigates a diagnosis and prognosis approach to detect anomalies in the flight trajectory, diagnose root causes, and then perform prognosis regarding the risk of occurrence of adverse events, in the presence of various sources of uncertainty. The proposed method is illustrated using a three-step procedure. First, using flight trajectory data, we evaluate the probabilities of system states corresponding to each failure case, from which we formulate a state-space model. Next, we perform anomaly detection for a specific flight trajectory by developing a Bayesian state estimation-based method, and subsequently identify the cause of the detected anomaly. Once the root cause is identified, prognosis is performed to predict the future state in a probabilistic manner. The proposed method is illustrated using near-ground landing data synthetically generated from an open source air traffic simulator - BlueSky. The simulation data mimicking the near-ground landing process with different initial states (e.g., aircraft altitude and speed, response delay, and brake performance) and other factors (such as wind direction) are used to demonstrate the procedures of diagnosis and prognosis.
AB - The safety of the air transportation system is affected by a variety of uncertainties arising from multiple sources. This paper investigates a diagnosis and prognosis approach to detect anomalies in the flight trajectory, diagnose root causes, and then perform prognosis regarding the risk of occurrence of adverse events, in the presence of various sources of uncertainty. The proposed method is illustrated using a three-step procedure. First, using flight trajectory data, we evaluate the probabilities of system states corresponding to each failure case, from which we formulate a state-space model. Next, we perform anomaly detection for a specific flight trajectory by developing a Bayesian state estimation-based method, and subsequently identify the cause of the detected anomaly. Once the root cause is identified, prognosis is performed to predict the future state in a probabilistic manner. The proposed method is illustrated using near-ground landing data synthetically generated from an open source air traffic simulator - BlueSky. The simulation data mimicking the near-ground landing process with different initial states (e.g., aircraft altitude and speed, response delay, and brake performance) and other factors (such as wind direction) are used to demonstrate the procedures of diagnosis and prognosis.
UR - http://www.scopus.com/inward/record.url?scp=85071468676&partnerID=8YFLogxK
U2 - 10.36001/phmconf.2018.v10i1.497
DO - 10.36001/phmconf.2018.v10i1.497
M3 - Conference article published in proceeding or book
AN - SCOPUS:85071468676
VL - 10
T3 - Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
BT - PHM 2018 - 10th Annual Conference of the Prognostics and Health Management Society
A2 - Bregon, Anibal
A2 - Orchard, Marcos
PB - Prognostics and Health Management Society
T2 - 10th Annual Conference of the Prognostics and Health Management Society, PHM 2018
Y2 - 24 September 2018 through 27 September 2018
ER -