scholarly journals Predictive airframe maintenance strategies using model-based prognostics

2018 ◽  
Vol 232 (6) ◽  
pp. 690-709 ◽  
Author(s):  
Yiwei Wang ◽  
Christian Gogu ◽  
Nicolas Binaud ◽  
Christian Bes ◽  
Raphael T Haftka ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Predictive Maintenance ◽  
Aviation Industry ◽  
Damage Growth ◽  
Maintenance Strategies ◽  
Damage Propagation ◽  
Structural Health ◽  
Model Based ◽  
The Future

Aircraft panel maintenance is typically based on scheduled inspections during which the panel damage size is compared to a repair threshold value, set to ensure a desirable reliability for the entire fleet. This policy is very conservative since it does not consider that damage size evolution can be very different on different panels, due to material variability and other factors. With the progress of sensor technology, data acquisition and storage techniques, and data processing algorithms, structural health monitoring systems are increasingly being considered by the aviation industry. Aiming at reducing the conservativeness of the current maintenance approaches, and, thus, at reducing the maintenance cost, we employ a model-based prognostics method developed in a previous work to predict the future damage growth of each aircraft panel. This allows deciding whether a given panel should be repaired considering the prediction of the future evolution of its damage, rather than its current health state. Two predictive maintenance strategies based on the developed prognostic model are proposed in this work and applied to fatigue damage propagation in fuselage panels. The parameters of the damage growth model are assumed to be unknown and the information on damage evolution is provided by noisy structural health monitoring measurements. We propose a numerical case study where the maintenance process of an entire fleet of aircraft is simulated, considering the variability of damage model parameters among the panel population as well as the uncertainty of pressure differential during the damage propagation process. The proposed predictive maintenance strategies are compared to other maintenance strategies using a cost model. The results show that the proposed predictive maintenance strategies significantly reduce the unnecessary repair interventions, and, thus, they lead to major cost savings.

scholarly journals Identification of Bridge Key Performance Indicators Using Survival Analysis for Future Network-Wide Structural Health Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6894
Author(s):  
Nicola-Ann Stevens ◽  
Myra Lydon ◽  
Adele H. Marshall ◽  
Su Taylor
Keyword(s):  
Survival Analysis ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Performance Indicators ◽  
Key Performance Indicators ◽  
Predictive Maintenance ◽  
Sensor Data ◽  
Transport Networks ◽  
Data Set ◽  
Structural Health

Machine learning and statistical approaches have transformed the management of infrastructure systems such as water, energy and modern transport networks. Artificial Intelligence-based solutions allow asset owners to predict future performance and optimize maintenance routines through the use of historic performance and real-time sensor data. The industrial adoption of such methods has been limited in the management of bridges within aging transport networks. Predictive maintenance at bridge network level is particularly complex due to the considerable level of heterogeneity encompassed across various bridge types and functions. This paper reviews some of the main approaches in bridge predictive maintenance modeling and outlines the challenges in their adaptation to the future network-wide management of bridges. Survival analysis techniques have been successfully applied to predict outcomes from a homogenous data set, such as bridge deck condition. This paper considers the complexities of European road networks in terms of bridge type, function and age to present a novel application of survival analysis based on sparse data obtained from visual inspections. This research is focused on analyzing existing inspection information to establish data foundations, which will pave the way for big data utilization, and inform on key performance indicators for future network-wide structural health monitoring.

Improving robustness of damage imaging in model-based structural health monitoring of complex structures

2015 ◽  
Vol 138 (3) ◽  
pp. 1766-1766 ◽  
Author(s):  
Patrice Masson ◽  
Nicolas Quaegebeur ◽  
Pierre-Claude Ostiguy ◽  
Peyman Y. Moghadam
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Complex Structures ◽  
Structural Health ◽  
Model Based ◽  
Damage Imaging

Integration of scheduled structural health monitoring with airline maintenance program based on risk analysis

2017 ◽  
Vol 232 (1) ◽  
pp. 92-104 ◽  
Author(s):  
Jianzhong Sun ◽  
Dan Chen ◽  
Chaoyi Li ◽  
Hongsheng Yan
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
System Level ◽  
Aviation Industry ◽  
Maintenance Cost ◽  
Safety Level ◽  
Commercial Aviation ◽  
Structural Health ◽  
Operational Concept ◽  
Maintenance Process

The aerospace industry is striving to reduce the aircraft operating costs while maintaining required safety level. Emerging technologies such as the structural health monitoring to reduce long-term maintenance cost and increase aircraft availability are promoted by the manufacturers. To successfully integrate the structural health monitoring technology into the current maintenance process of modern commercial aviation, a clear definition of the structural-health-monitoring-based maintenance operational concept and the system level requirements is required. This article proposed a structural health monitoring operational concept and the associated maintenance cost modeling and risk assessment methods for the implementation of the structural health monitoring in commercial aviation industry. The developed methodology provides a tool to determine the optimal scheduled structural health monitoring inspection interval and repair decision thresholds for approved scheduled structural health monitoring task. A simulated case study is carried out to demonstrate the structural health monitoring operational concept and how an optimal maintenance strategy can be determined using the proposed methodology. Preliminary results show that the integration of the structural health monitoring into the existing maintenance process can reduce the maintenance cost compared to that of the current practice using the traditional Non-Destructive Evaluation (NDE) techniques while maintaining the risk below an acceptable level.

Model-based structural health monitoring of naval ship hulls

2011 ◽  
Vol 200 (9-12) ◽  
pp. 1137-1149 ◽  
Author(s):  
Christopher J. Stull ◽  
Christopher J. Earls ◽  
Phaedon-Stelios Koutsourelakis
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Health ◽  
Ship Hulls ◽  
Model Based ◽  
Naval Ship

An enhanced dynamic Gaussian mixture model–based damage monitoring method of aircraft structures under environmental and operational conditions

2018 ◽  
Vol 18 (2) ◽  
pp. 524-545 ◽  
Author(s):  
Lei Qiu ◽  
Fang Fang ◽  
Shenfang Yuan ◽  
Christian Boller ◽  
Yuanqiang Ren
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Health Monitoring ◽  
Gaussian Mixture ◽  
Monitoring Method ◽  
Structural Health ◽  
Model Based ◽  
Damage Monitoring

Gaussian mixture model–based structural health monitoring methods have been studied in recent years to improve the reliability of damage monitoring under environmental and operational conditions. However, most of these methods only use the ordinary expectation maximization algorithm to construct the Gaussian mixture model but the expectation maximization algorithm can easily lead to a local optimal solution and a singular solution, which also results in unreliable and unstable damage monitoring especially for complex structures. This article proposes an enhanced dynamic Gaussian mixture model–based damage monitoring method. First, an enhanced Gaussian mixture model constructing algorithm based on a Gaussian mixture model merge-split operation and a singularity inhibition mechanism is developed to keep the stability of the Gaussian mixture model and to obtain a unique optimal solution. Then, a probability similarity–based damage detection index is proposed to realize a normalized and general damage detection. The method combined with guided wave structural health monitoring technique is validated by the hole-edge cracks monitoring of an aluminum plate and a real aircraft wing spar. The results indicate that the method is efficient to improve the reliability and the stability of damage detection under fatigue load and varying structural boundary conditions. The method is simple and reliable regarding aviation application. It is a data-driven statistical method which is model-independent and less experience-dependent. It can be used by combining with different kinds of structural health monitoring techniques.

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