Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

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The deployment cost of the structural health monitoring (SHM) system is the major argument against the more widespread use of the structural health monitoring techniques. Optimization of sensor pla...

Optimization of sensor placement for structural health monitoring: a review:

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.

https://www.mdpi.com/2226-4310/5/3/87/pdf

Cost-Effectiveness of Structural Health Monitoring in Fuselage Maintenance of the Civil Aviation Industry †

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.

/pdf/deepshm-a-deep-learning-approach-for-structural-health-4sko5jn36j.pdf

DeepSHM: a deep learning approach for structural health monitoring based on guided Lamb wave technique

Perception modelling by invariant representation of deep learning for automated structural diagnostic in aircraft maintenance: A study case using DeepSHM

In this paper, we investigated transducer placement strategies for detecting cracks in primary aircraft structures using ultrasonic Structural Health Monitoring (SHM). The approach developed is for an expected damage location based on fracture mechanics, for example fatigue crack growth in a high stress location. To assess the performance of the developed approach, finite-element (FE) modelling of a damage-tolerant aluminum fuselage has been performed by introducing an artificial crack at a rivet hole into the structural FE model and assessing its influence on the Lamb wave propagation, compared to a baseline measurement simulation. The efficient practical sensor position was determined from the largest change in area that is covered by reflected and missing wave scatter using an additive color model. Blob detection algorithms were employed to determine the boundaries of this area and to calculate the blob centroid. To demonstrate that the technique can be generalized, the results from different crack lengths and from tilted crack are also presented.

Transducer Placement Option of Lamb Wave SHM System for Hotspot Damage Monitoring

The near-term artificial intelligence, commonly referred as ‘weak AI’ in the last couple years was achieved thanks to the advances in machine learning (ML), particularly deep learning, which has currently the best in-class performance outperforming other machine learning algorithms. In the deep learning framework, many natural tasks such as object, image, and speech recognition that were impossible to be performed by classical ML algorithms in the previous decades can now be be done by typical home personal computer. Deep learning requires large amount of data that has to be rapidly collected (also known as ‘big data’) in order to create robust model parameters that are able to predict future occurrences of certain event. In some domains, a large dataset such as CIFAR-10, MNIST, or Kaggle exist already. However, in many other domains such as aircraft visual inspection, such a large dataset is not easily available and this clearly restricts deep learning to perform well to recognize material damage in aircraft structures. As many computer science researchers believe, we also think that in order to achieve a performance similar to human-level intelligence, AI could and should not start from scratch. Introducing an inductive bias into deep learning might be one solution to achieve that humanlevel intelligence. In this paper, we give an example how to incorporate aerospace domain knowledge into the development of deep learning algorithms. We performed a relatively simple procedure: we conducted fatigue testing of an aluminum plate that is typically used in aircraft fuselage and build a deep convolutional neural network that classifies crack length according to crack propagation curve obtained from fatigue test. The results of this network are then compared to the results of the same network that was not injected by domain knowledge

Incorporating Inductive Bias into Deep Learning : A Perspective from Automated Visual Inspection in Aircraft Maintenance

In this paper, we review two transducer placement options to locate and quantify damage in primary aircraft structures using ultrasonic Structural Health Monitoring (SHM). The first placement approach concerns a known expected damage location, for example a fatigue crack growth from rivet hole. The location of such a damage can already be predicted by fracture mechanics and therefore the focus of this SHM system design is to determine the damage size. For this approach, we have developed our previous work in finite-element (FE) modelling of a damage tolerant aluminum fuselage by introducing an artificial crack into the structural FE model and assessed its influence on the Lamb wave propagation. Image processing was performed by subtracting the wave propagation image of the damaged from the undamaged structure. A second category of damage occurs at locations that cannot be predicted by fracture mechanics, such as impact damage from hail. This type of damage requires the SHM system to both locate and assess the size of the damage and this is heavily influenced by the positioning of the transducers. Optimal sensor placement (OSP) techniques tend to rely on assessment using the probability of detection (POD) parameter. In this work, we propose an alternative placement method which maximizes the detectability of the transducer coverage area based on the pulse-echo technique without relying on the POD parameter, by determining the fitness function based on sensor coverage area for single and multiple sensors and random damage locations. Results from both these approaches are compared in this paper, with a perspective towards the overall design of SHM systems.

Vincentius Ewald

Papers

DeepSHM: a deep learning approach for structural health monitoring based on guided Lamb wave technique

Perception modelling by invariant representation of deep learning for automated structural diagnostic in aircraft maintenance: A study case using DeepSHM

Transducer Placement Option of Lamb Wave SHM System for Hotspot Damage Monitoring

Incorporating Inductive Bias into Deep Learning : A Perspective from Automated Visual Inspection in Aircraft Maintenance

Transducer Placement Option for Ultrasonic Lamb Wave Structural Health Monitoring (SHM) on Damage Tolerant Aircraft Substructure