The use of pseudo-faults for damage location in SHM: An experimental investigation on a Piper Tomahawk aircraft wing

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.The application of pattern recognition-based approaches in damage localisation and quantification will eventually require the use of some kind of supervised learning algorithm. The use, and most importantly, the success of such algorithms will depend critically on the availability of data from all possible damage states for training. It is perhaps well known that the availability of damage data through destructive means cannot generally be afforded in the case of high value engineering structures outside laboratory conditions. This paper presents the attempt to use added masses in order to identify features suitable for training supervised learning algorithms and then to test the trained classifiers with damage data, with the ultimate purpose of damage localisation. In order to test the approach of adding masses, two separate cases of a dual-class classification problem, representing two distinct locations, and a three-class problem representing three distinct locations, are examined with the help of a full-scale aircraft wing. It was found that an excellent rate of correct classification could be achieved in both the dual-class and three-class cases. However, it was also found that the rate of correct classification was sensitive to the choices made in training the supervised learning algorithm. The results for the dual-class problem demonstrated a comparatively high level of robustness to these choices with a substantially lower robustness found in the three-class case

On the usage of active learning for SHM

The key element of this work is to demonstrate a strategy for using pattern recognition algorithms to investigate correlations between feature variables for Structural Health Monitoring (SHM). The task will take advantage of data from a bridge. An informative chain of artificial intelligence tools will allow an active learning interaction between the unfolded shapes of the manifold of online data by characterising the physical shape between variables. In many data mining and machine learning applications, there is a significant supply of unlabelled data but an important undersupply of labelled data. Semi-supervised active learning, which combines both labelled and unlabelled data can offer serious access to useful information and may be the crucial element in successful decision making, regarding the health of structures

Machine learning-based rock characterisation models for rotary-percussive drilling

This is the final version. Available on open access from Springer via the DOI in this recordData accessibility: The data sets generated and analysed during the current study are available from the corresponding author on reasonable request.Vibro-impact drilling has shown huge potential of delivering better rate of penetration, improved tools lifespan and better borehole stability. However, being resonantly instigated, the technique requires a continuous and quantitative characterisation of drill-bit encountered rock materials in order to maintain optimal drilling performance. The present paper introduces a non-conventional method for downhole rock characterisation using measurable impact dynamics and machine learning algorithms. An impacting system that mimics bit-rock impact actions is employed in this present study, and various multistable responses of the system have been simulated and investigated. Features from measurable drill-bit acceleration signals were integrated with operated system parameters and machine learning methods to develop intelligent models capable of quantitatively characterising downhole rock strength. Multilayer perceptron, support vector regression and Gaussian process regression networks have been explored. Based on the performance analysis, the multilayer perceptron networks showed the highest potential for the real-time quantitative rock characterisation using considered acceleration features.Petroleum Technology Development Fund (PTDF) of Nigeri

Investigation of a multi-sensor data fusion technique for the fault diagnosis of gearboxes

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordGearbox is the key functional unit in a mechanical transmission system. As its operating condition being complex and the interference transmitting from diverse paths, the vibration signals collected from an individual sensor may not provide a fully accurate description on the health condition of a gearbox. For this reason, a new method for fault diagnosis of gearboxes based on multi-sensor data fusion is presented in this paper. There are three main steps in this method. First, prior to feature extraction, two signal processing methods, i.e. the energy operator and time synchronous averaging, are applied to multi-sensor vibration signals to remove interference and highlight fault characteristic information, then the statistical features are extracted from both the raw and preprocessed signals to form an original feature set. Second, a coupled feature selection scheme combining the distance evaluation technique and max-relevance and min-redundancy is carried out to obtain an optimal feature set. Finally, the deep belief network, a novel intelligent diagnosis method with a deep architecture, is applied to identify different gearbox health conditions. As the multi-sensor data fusion technique is utilized to provide sufficient and complementary information for fault diagnosis, this method holds the potential to overcome the shortcomings from an individual sensor that may not accurately describe the health conditions of gearboxes. Ten different gearbox health conditions are simulated to validate the performance of the proposed method. The results confirm the superiority of the proposed method in gearbox fault diagnosis.National Natural Science Foundation of Chin

Enhancing structural health monitoring with machine learning and data surrogates: a TCA-based approach for damage detection and localisation

This is the final version. Available from DEStech Publications via the DOI in this recordStructural health monitoring (SHM) involves constantly monitoring the condition of structures to detect any damage or deterioration that might develop over time. Machine learning methods have been successfully used in SHM, however, their effectiveness is often limited by the availability of data for various damage cases. Such data can be especially hard to obtain from high-value structures. In this paper, transfer component analysis (TCA) with domain adaptation is utilised in conjunction with high-fidelity nu merical models to generate surrogates for damage identification without the requirement for high volumes of data from various damaged states of the structure. The approach is demonstrated on a laboratory structure, a nonlinear Brake-Reuß beam, where damage scenarios correspond to different torque settings on a lap joint. It is shown that, in a three-class scenario, machine learning algorithms can be trained using numerical data and tested successfully on experimental data

Application and comparison of feature-based classification models for multistable impact motions of percussive drilling

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordData accessibility: The data sets generated and analysed during the current study are available from the corresponding author on reasonable request.Dynamics of the bit-rock interaction under percussive drilling often encounter multistability that produces coexisting impact motions for a wide range of drilling conditions. Some of them may be detrimental to its performance as it cuts through the inhomogeneous rock layers. A necessary mitigation is the ability to distinguish between coexisting impact motions in order to maintain a high-performance drilling. For this purpose, dynamical responses of a vibro-impact system mimicking the bit-rock interaction of percussive drilling were explored in this study by using machine learning techniques. As a fundamental approach of improving machine learning, hand-crafted and automatic feature extractions were carried out. Simulation results show that extracting appropriate features and using a suitable network are essential for characterising the vibro-impact motions. Extracting statistical, histogram of gradient, continuous wavelet transform and pre-trained convolutional network features are effective and less computationally intensive. With their high accuracies, they become the first point of consideration when designing the classification model for multistable vibro-impact motions of percussive drilling.Engineering and Physical Sciences Research Council (EPSRC)Petroleum Technology Development Fund (PTDF) of Nigeri

Performance monitoring of a wind turbine using extreme function theory

A power curve relates the power produced by a wind turbine to the wind speed. Usually, such curves are unique to the various types of wind turbines, so that by monitoring the power curves, one may monitor the performance of the turbine itself. Most approaches to monitoring a system or a structure at a basic level, generally aim at differentiating between a normal and an abnormal state. Typically, the normal state is represented by a model, and then abnormal, or extreme data points are identified when they are compared to that model. This comparison is very often done pointwise on scalars in the univariate case, or on vectors, if multivariate features are available. Depending on the actual application, the pointwise approach may be limited, or highly prone to false identifications. This paper presents the use of extreme functions for the performance monitoring of wind turbines. Power curves from an actual wind turbine, are assessed as whole functions, and not individual datapoints, with the help of Gaussian process regression and extreme value distributions, with the ultimate aim of the performance monitoring of the wind turbine at a weekly resolution. The approach is compared to the more conventional pointwise method, and approaches which make use of multivariate features, and is shown to be superior in terms of the number of false identifications, with a significantly lower number of false-positives without sacrificing the sensitivity of the approach

A brief introduction to recent developments in population-based structural health monitoring

This is the final version. Available from the publisher via the DOI in this record.One of the main problems in data-based Structural Health Monitoring (SHM), is the scarcity of measured data corresponding to damage states in the structures of interest. One approach to solving this problem is to develop methods of transferring health inferences and information between structures in an identified population—Population-based SHM (PBSHM). In the case of homogenous populations (sets of nominally-identical structures, like in a wind farm), the idea of the form has been proposed which encodes information about the ideal or typical structure together with information about variations across the population. In the case of sets of disparate structures—heterogeneous populations—transfer learning appears to be a powerful tool for sharing inferences, and is also applicable in the homogenous case. In order to assess the likelihood of transference being meaningful, it has proved useful to develop an abstract representation framework for spaces of structures, so that similarities between structures can formally be assessed; this framework exploits tools from graph theory. The current paper discusses all of these very recent developments and provides illustrative examplesEngineering and Physical Sciences Research Council (EPSRC

The use of pseudo-faults for damage location in SHM: An experimental investigation on a Piper Tomahawk aircraft wing

The application of pattern recognition-based approaches in damage localisation and quantification will eventually require the use of some kind of supervised learning algorithm. The use, and most importantly, the success of such algorithms will depend critically on the availability of data from all possible damage states for training. It is perhaps well known that the availability of damage data through destructive means cannot generally be afforded in the case of high value engineering structures outside laboratory conditions. This paper presents the attempt to use added masses in order to identify features suitable for training supervised learning algorithms and then to test the trained classifiers with damage data, with the ultimate purpose of damage localisation. In order to test the approach of adding masses, two separate cases of a dual-class classification problem, representing two distinct locations, and a three-class problem representing three distinct locations, are examined with the help of a full-scale aircraft wing. It was found that an excellent rate of correct classification could be achieved in both the dual-class and three-class cases. However, it was also found that the rate of correct classification was sensitive to the choices made in training the supervised learning algorithm. The results for the dual-class problem demonstrated a comparatively high level of robustness to these choices with a substantially lower robustness found in the three-class case