Although structural health monitoring (SHM) technologies using sensors have dramatically been developed recently, their capability should be evaluated from the perspective of the maintenance industry. As a first step toward utilizing sensors, the objective of the paper is to investigate the possibility of using sensors for inspecting the entire fuselage during C-check. First, we reviewed various sensors for their detection range, detectable damage size, and installed weight, which revealed that the piezoelectric wafer active sensor (PWAS) is the most promising sensor for aircraft SHM. Second, we performed a case study of inspecting the fuselage of Boeing-737NG using PWAS. To maintain the same detecting capability of manual inspection in C-check, we estimated the total number of sensors required. It turned out that utilizing sensors can reduce the maintenance downtime and thus, maintenance cost. However, even with a very conservative estimate, the lifetime cost was significantly increased due to the weight of sensor systems. The cost due to the weight increase was an order of magnitude higher than the cost saved by using SHM. We found that a large number of sensors were required to detect damage at unknown locations, which was the main cause of the weight increase. We concluded that to make SHM cost-effective, it would be necessary either to improve the current sensor technologies so that a less number of sensors are used or to modify the aircraft design concept for SHM.

In this article, a generalized optimality criteria method is proposed for topology optimization with arbitrary objective function and multiple inequality constraints. This algorithm uses sensitivity information to update both the Lagrange multipliers and design variables. Different from the conventional optimality criteria method, the proposed method does not satisfy constraints at every iteration. Rather, it improves the Lagrange multipliers and design variables such that the optimality criteria are satisfied upon convergence. The main advantages of the proposed method are its capability of handling multiple constraints and computational efficiency. In numerical examples, the proposed method was found to be more than 100 times faster than the optimality criteria method and more than 1000 times faster than the method of moving asymptotes.

This chapter quantifies the advantages of condition-based maintenance on the safety and lifetime cost of an airplane fuselage. The lifecycle of an airplane is modeled as blocks of crack propagation due to pressurization interspersed with inspection and maintenance. The Paris-Erdogan model with uncertain parameters is used to model fatigue crack growth. The fuselage skin is modeled as a hollow cylinder, and an average thickness is calculated to achieve a probability of failure in the order of 1 in 10 million with scheduled maintenance. Condition-based maintenance is found to improve the safety of an airplane over scheduled maintenance and will also lead to savings in lifecycle cost. The main factor of the savings stems from the reduced net revenue lost due to shortened downtime for maintenance. There are also other factors such as work saved on inspection and removing/installing surrounding structures for manual inspection. In addition to cost savings, some potential advantages of condition-based maintenance are discussed such as avoiding damage caused by removing/installing surrounding structures, more predictable maintenance, and improving the safety issues of same aircraft model by posting the frequently occurred damages into Airworthiness Directives, Service Bulletins, or Service Letters.

An efficient Bayesian-based algorithm is presented for physics-based prognostics, which combines a physical model with observed health monitoring data. Unknown model parameters are estimated using the observed data, from which the remaining useful life (RUL) of the system is predicted. This paper focuses on the Bayesian method for parameter estimation of a damage degradation model where epistemic uncertainty in model parameters is reduced with the observed data. Markov-chain Monte Carlo sampling is used to generate samples from the posterior distribution, which are then propagated through the physical model to estimate the distribution of the RUL. A MATLAB script of 76 lines is included in this paper with detailed explanations. A battery degradation model and crack growth model are used to explain the process of parameter estimation, the evolution of degradation and RUL prediction. The code presented in this paper can easily be altered for different applications. This code may help beginners to understand and use Bayesian method-based prognostics.

In physics-based prognostics, model parameters are estimated by minimizing the error or maximizing the likelihood between model predictions and measured data. When multiple model parameters are strongly correlated, it is challenging to identify individual parameters by measuring degradation data, especially when the data have noise. This paper first presents various correlations that occur during the process of model parameter estimation and then introduces two methods of identifying the accurate values of individual parameters when they are strongly correlated. The first method can be applied when the correlation relationship evolves as damage grows, while the second method can be applied when the operating (loading) conditions change. Starting from manufactured data using the true parameters, the accuracy of identified parameters is compared with various levels of noise. It turned out that the proposed method can identify the accurate values of model parameters even with a relatively large level of noise. In terms of the marginal distribution, the standard deviation of a model parameter is reduced from 0.125 to 0.03 when different damage states are used. When the loading conditions change, the uncertainty is reduced from 0.3 to 0.05. Both are considered as a significant improvement.