AI-Assisted Design & Optimization for Predictive Maintenance: A Case Study using Deep Learning and Search Metaheuristics for Structural Health Monitoring in Aviation

One of the classical solutions to maintain the aircraft structural integrity is to rely on the analysis of non-destructive testing (NDT) inspector with various inspection methods. However, it is relatively expensive in matter of time and costs to train human resources until the certification is reached. Further, in majority of the cases of aircraft scheduled and unscheduled maintenance, most of the detected damages are far below the damage tolerance limit and therefore are considered as a costly false positive because such inspections generally require additional downtime. Structural Health Monitoring (SHM) tries to reduce the wasteful resources in the maintenance, repair, and overhaul (MRO) industry by signaling such false positives during the maintenance process by becoming an integral part of the structure itself.

On the other hand, there has been an increase in using the artificial intelligence (AI) methodologies such as computational heuristics and machine learning in many areas of human civilization which includes voice and face recognition, languages translation, and automated driving. There has been a lot of interest on implementing AI to assist SHM in maintaining airworthiness while driving the cost down. Nevertheless, the maintenance of airworthiness (such as but not limited to, EASA Part 145/M and FAA CFR Part 21) is a heavily regulated area and are not easily changed.

The current state of the art was captured in the literature review. This includes recent developments of guided wave based SHM and the parameter optimization as well as recent trends and advances in artificial intelligence such as machine and deep learning. The findings from the state of the art were used as the basis to determine the research problem and to propose the solution.

The first part of the proposed solution consisted of a short review the damage growth assumption within the damage tolerance framework and the used methodology to generate and capture Lamb wave signal within Finite Element (FE) environment. This methodology is a deterministic solution that can be partially used for solving continuous optimization in deterministic sensor placement problem. It was further expanded to include a semi-stochastic approach to address nonpredictable damage location that includes some metaheuristics search such as genetic algorithm and swarm intelligence. The ultimate first part of solution was a compromise between the deterministic and semi-stochastic actuator-sensor topology.

The second part of the proposed solution was the investigation on whether deep learning can be used to treat the Lamb wave signal given the configuration obtained from the first part of the proposed solution. To do so, an assumption based on converging probability measures and generalization bound in deep learning must be taken. Then, the approach is to represent the entity of the captured Lamb wave signal in time-frequency domain either as randomly sampled spectrogram or layers of joined spectrograms. After the training, the hypothesis was validated with A/B Testing.

Then, the research was expanded to understand the scalability level of deep learning for SHM for given data size, model parameters, and restriction on physical memory. In this sense, the signal representations were trained sequentially with an example of in hybrid convolutional recurrent network. The investigation was focused on stability behavior of convoluted-recurrent modelling for variable spectrogram length and the experimental validation of the model for classification of the Lamb wave spectrogram signals.