Predictive maintenance, as one of the core components of Industry 4.0, takes a proactive approach to maintain machines and systems in good order to keep downtime to a minimum and the airline maintenance industry is not an exception to this. To achieve this goal, practices in Structural Health Monitoring (SHM) complement the existing Non-Destructive-Testing (NDT) have been established in the last decades. Recently, the increasing computational capability such as utilization of a graphical processing unit (GPU) in combination with advanced machine learning techniques such as deep learning has been one of the main drivers in the advancement of predictive analytics in condition monitoring. In our previous work, we proposed a novel approach using deep learning for guided wave based structural health called DeepSHM. As a study case, we treated an ultrasonic signal from guided Lamb wave SHM with a convolutional neural network (CNN). In that work, we only considered a single central frequency excitation. This led to a single governing wavelength which is normally good for the detection of a single damage size. In classical signal processing, applying a broader excitation frequency poses an analysis and interpretation nightmare because it contains more complex information and thus is difficult to understand. This problem can be overcome with deep learning; however, it creates another problem: while deep learning typically results in a more accurate result prediction, it is specifically made for solving only certain types of tasks. While many papers have already introduced deep learning for diagnostics, many of these works are only proposing novel predictive techniques, however the mathematical formalization is lacking, and we are not informed about why we should treat acoustic signal with deep learning. So, the basis of ‘explainable AI’ for SHM and NDT is currently lacking. For this reason, in this paper, we would like to extend our previous work into a more generalized. Rather than focusing on a novel technique, we propose a plausible theoretical perspective inspired from neuroscience for signal representation of deep learning framework to model machine perception in structural health monitoring (SHM), especially because SHM typically involves multiple sensory input from different sensing locations. To do this, we created a set of artificial data from a finite element model (FEM) and represented DeepSHM in two different ways: 1). Perpetual representation of observation and 2). Hierarchical structure of entities that is decomposable in a smaller sub-entity. Consequently, we assume two plausible models for DeepSHM: 1). Either it behaves as a single deciding actor since the observation is regarded as perpetual, and 2). Or it acts as a multiple actor with independent outputs since multiple sensors can form different output probabilities. These artificial data were split into several different input representations, classified into several damage scenarios and then trained with commonly used deep learning training parameters. We compare the performance metrics of each perception model to describe the training behavior of both representations.

The concept of structural health monitoring has been introduced to ensure structural integrity during the design lifetime of a structure. The main objectives of structural health monitoring are to detect, locate, quantify, and predict any damage that occurs during this lifetime of the structure so that effective and efficient maintenance and repair procedures can be performed. The location of structural damage events can be discretized as deterministic and probabilistic. A deterministic location specifies that the damage occurs in high-stress regions or other regions that can be predicted by the structural design, such as the most probable location for a fatigue crack. A probabilistic damage event is one where the location of the damage is independent of structural design parameters, such as hail impact, bird strike, and impact from ground vehicles. A structural health monitoring system should be able to handle both these damage occurrences. In our previous work, we optimized the transducer placement in Lamb wave–based structural health monitoring for the detection of a fatigue crack that emerges from a rivet hole. In this article, we demonstrate a combination of that method with a different sensor placement optimization method to add the capability to detect probabilistic damage location. First, we considered the ultrasonic wave attenuation in the structure and based on this attenuation, we created a fitness function. Since this fitness function is difficult to solve due to its combinatorial nature, we compared three common metaheuristic stochastic strategies: global random search, greedy algorithm, and genetic algorithm, for solving this problem. The results of this analysis were then integrated with the previously described deterministic approach, making a global structural health monitoring sensor placement strategy that balances the need to detect both pre-determined and random damage location occurrences. The analytical result of the study presented is validated by experiment.

In our previous work, we demonstrated how to use inductive bias to infuse a convolutional neural network (CNN) with domain knowledge from fatigue analysis for aircraft visual NDE. We extend this concept to SHM and therefore in this paper, we present a novel framework called DeepSHM which involves data augmentation of captured sensor signals and formalizes a generic method for end-to-end deep learning for SHM. The study case is limited to ultrasonic guided waves SHM. The sensor signal response from a Finite-Element-Model (FEM) is pre-processed through wavelet transform to obtain the wavelet coefficient matrix (WCM), which is then fed into the CNN to be trained to obtain the neural weights. In this paper, we present the results of our investigation on CNN complexities that is needed to model the sensor signals based on simulation and experimental testing within the framework of DeepSHM concept.