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Condition monitoring is an indispensable element related to the operation of rotating machinery. In this article, the monitoring system for the parallel gearbox was proposed. The novelty detection approach is used to develop the condition assessment support system, which requires data collection for a healthy structure. The measured signals were processed to extract quantitative indicators sensitive to the type of damage occurring in this type of structure. The indicator’s values were used for the development of four different novelty detection algorithms. Presented novelty detection models operate on three principles: feature space distance, probability distribution, and input reconstruction. One of the distance-based models is adaptive, adjusting to new data flowing in the form of a stream. The authors test the developed algorithms on experimental and simulation data with a similar distribution, using the training set consisting mainly of samples generated by the simulator. Presented in the article results demonstrate the effectiveness of the trained models on both data sets.

Data scarcity is a significant problem when it comes to designing machine learning systems for structural health monitoring applications, especially those based around data-hungry algorithms and methods, such as deep learning. Synthetic data generation could potentially alleviate this problem, lowering the number of measurements that need to be acquired in slow and often expensive conventional experiments. Such synthesis can be done by Generative Adversarial Networks, potentially creating unlimited synthetic samples recreating the original data distribution. While most of the research about these networks is centered around using them on image data, they have also been applied to audio waves - going as far as successfully synthesizing human speech. This suggests that these networks should also apply to synthesizing time-domain signals in various fields of structural health monitoring, guided waves in particular, as they are in many ways similar to audio wave signals. This work proposes an adaptation of style-based GAN architecture to time-domain signal generation, and presents its viability for guided waves synthesis, utilizing a database of signals collected in series of pitch-catch experiments on a composite plate.

Interpretation of the data acquired from guided-wave-based measurements often utilizes machine learning. However, creating effective machine learning models generally requires a significant amount of data - which in the case of guided waves are costly and time-consuming to acquire. This limitation significantly reduces the application perspective of many advanced machine learning algorithms, most notably deep learning. The problem of data scarcity has been partially addressed in the field of computer vision via the usage of generative adversarial neural networks. These generate synthetic data samples, matching the real data distribution. Aside from images, generative adversarial networks have also been applied to synthesize audio data - with recent advances going as far as successfully synthesizing human speech. These developments suggest that they may be applicable for generating guided waves data - as fundamentally the problem is in many ways similar to that presented by audio waves. This work explores the capabilities of generative adversarial neural networks in the area of guided-wave signal synthesis. The used database was acquired in a series of pitch-catch experiments in which various sensor locations were utilized, and is significantly extended both in terms of sensor locations and data available from each sensor pair. Lastly, the resultant synthesized data is evaluated by qualitative signal comparison.

One of the important issues related to the applications of Lamb waves for structural health monitoring is their undesired sensitivity to variation of environmental conditions. Temperature is the main factor that can affect wave propagation and hence significantly reduce performance of a SHM system. Therefore, there is a need for development of robust monitoring methods with low sensitivity to temperature variations. This paper is aimed at verification of efficiency of four methods designed for damage detection using Lamb wave measurements performed in variable environmental conditions. The methods investigated in the comparison are the following: optimal baseline selection approach, the damage index based on a signal alignment with respect to instantaneous phase, and a group measurement approach capable of distinguishing local damage-related changes from temperature-induced global ones. The fourth method relies on fusion all these solutions simultaneously. The methods’ ability to damage detection is compared using a specimen that is subjected to large temperature changes. It is found that although all the methods have their strengths and weaknesses, a cooperation of all solutions allows for significant increase of the damage detection efficiency.

Neural networks are commonly recognized tools for the classification of multidimensional data obtained in structural health monitoring (SHM) systems. Their configuration for a given scenario is, however, a challenging task, which limits the possibilities of their practical applications. In this article the authors propose using the neural network ensemble approach for the classification of SHM data generated by guided wave sensor networks. The overproduce and choose strategy is used for designing ensembles containing different types and sizes of neural networks. The proposed method allows for a significant increase of the state assessment reliability, which is illustrated by the results obtained from the practical industrial case of a full-scale aircraft test. The method is verified in the process of detecting fatigue cracks propagating in the aircraft load-carrying structure. The long-term experiments are performed in variable environmental conditions with a net of structure-embedded piezoelectric sensors.

In the paper, optical-based measurement methods for calculating the deflection and vibration of overhead lines are presented. The authors describe the state of the art in the field of non-contact examination of static and dynamic overhead transmission line characteristics, and propose concepts of vision-based measurement systems for both static and dynamic states of a structure. The system devoted to static measurements is based on a digital SLR camera and image-processing software used for the acquisition and interpretation of data. The digital image correlation method, implemented in Wiz2D software, is applied to compute the displacement of the transmission line cable with respect to a known baseline position. Dynamic characteristics of the structure are obtained using a stereo-vision system consisting of two high-speed digital cameras. Corresponding points in two video sequences of the vibrating cable are tracked using TEMA software. 3D positions in a camera frame of reference and displacements are computed using a 3D reconstruction method. The paper describes two series of experiments conducted on a lab setup and the obtained results are examined and discussed.

Summary Deflection curve can be used to detect and localize damage in civil engineering structures. In this paper, a vision-based method applied for in-plane displacement field measurement of cantilever beams is presented. The deflection curve of the analyzed structure is computed by means of the digital image correlation. Damage is introduced into the structure. Resulting deflection curves are used as an input to the novel damage detection algorithms: line segments method and voting method. The algorithms are then compared with the second derivative method and assessed through the probability of detection procedure. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper a vision-based method for measuring vertical deflection of large engineering structures under load is presented in example of crane working arm deflection measurements. The deflection is computed by means of a digital image correlation technique embedded in the vision-based system. The results of measurements performed on the vision system were compared with data obtained using a Leica Laser Tracker system. The comparison revealed proper functioning of the developed vision system in industrial conditions.

The authors provide a method based on ensembles of artificial neural networks (ANNs) that, fed with supervised training data, are able to estimate rotational speed of the machine under investigation. Ensembles of various ANNs trained using different algorithms provide reliability of the method and protection from unfortunate initial weights distribution. The method is validated on the basis of data gathered in large amount of experiments performed in epicyclic gearbox test bed

SUMMARY This paper presents a novel approach to the problem of health monitoring of aircraft structures using Lamb waves. Piezoelectric sensors, embedded in the aircraft sheathing, generate Lamb waves with the aim to monitor the structural integrity of complex structure parts. The ultrasonic signals obtained from the sensor pairs arranged in pitch-catch configuration are used for the calculation of a number of different damage indices. The damage indices are then used as inputs for a classifier employing an artificial neural network (ANN) that is trained to perform structure condition assessment. Efficiency of the ANN classifier trained on artificial data generated from the numerical simulations performed using linear interaction simulation approach is investigated. The resulting classification results are compared with those obtained for the ANN trained on experimental data from the real specimens. Copyright © 2014 John Wiley & Sons, Ltd.

Lamb waves (LW) are used for damage detection and health monitoring due to the long range propagation ability and sensitivity to structural integrity changes as well as their robustness in different applications. However, due to the dispersive character and multimode nature of LWs, analysis of the acquired ultrasonic signals is very complex. It becomes even more difficult when applied to a complex structure, for instance, an aircraft component with riveted joints and stringers characterized by difficult geometries. Therefore, numerous approaches to the evaluation of damage indices have been proposed in the literature. In this paper, comparison a number of damage indices applied to LWs testing in aircraft aluminum panels. The damage indices, known from the literature have been selected from the application point of view. Artificial neural network has been used for the classification of fatigue cracks and artificial damages induced in the specimens taken from a real aircraft structure. Article presents results of simulation, data analysis and data classification obtained using selected and dedicated neural network. The main aim of the presented research was to develop signal processing and signal classification methods for an aircraft health monitoring system. The article presents a part of the research carried out in the project named SYMOST.

This paper presents an approach to damage growth monitoring and early damage detection in the structure of PZL - 130 ORLIK TC II turbo-prop military trainer aft using the statistical models elaborated by the Polish Air Force Institute of Technology (AFIT) and the network of the sensors attached to the structure. Drawing on the previous experiences of the AFIT and AGH in structural health monitoring, the present research will deploy an array of the PZT sensors in the structure of the PZL -130 Orlik TC II aircraft. The aircraft has just started Full Scale Fatigue Test (FSFT) that will continue up to 2013. The FSFT of the structure is necessary as a consequence of the structure modification and the change of the maintenance system - the transition to Condition Based Maintenance. In this paper, a novel approach to the monitoring of the aircraft hot-spots will be presented. Special attention will be paid to the preliminary results of the statistical models that provide an automated tool to infer about the presence of damage and its size. In particular, the effectiveness of the selected signal characteristics will be assessed using dimensional reduction methods (PCA) and the so-called averaged damage indices will be delivered. Moreover, the results of the signal classification based on the neural network will be presented alongside the numerical model of the wave propagation. The work contains selected information about the project scope and the results achieved at the preliminary stage of the project

A large number of commercial systems for condition monitoring of most common planetary gearboxes used in wind turbines and mining machinery have been developed for years. However nowadays, multistage constructions are encountered in industries. These are not necessarily planetary, but generally epicyclic. Current state of the art, according to the authors knowledge, does not give general equations for a case where multistage systems are considered, where some of the gears consist all moving parts. Hence, currently available CMS systems are not suitable for condition monitoring of these kinds of systems. The paper presents a new general equation, which allows calculating the characteristic frequencies of any kind of multistage gear sets, as a result of theoretical investigation. Illustrated solution does not assume a fixed speed of any element. Moreover, presented equation takes into account corrected teeth, making developed equations most general from all available in tribology science. Presented scientific development is currently implemented in a modern European CMS.

The paper illustrates a general equation in a new form, which allows calculating the characteristic frequencies of any kind of epicyclic gear sets with a ring, a sun, and planets. Moreover, presented equation takes into account corrected teeth (i.e. where the equality 2P+S=R is not fulfilled). This happens when gearboxes contain gears where corrected teeth procedure was adopted during designing stage. Presented solution can refine the configuration modules of the Condition Monitoring Systems (CMS) in such a way that allows to configure systems into larger groups than now available, i.e. multistage gear sets systems with epicyclic gears. Such CMS are capable of early mechanical faults detection, which prevents from costly critical repairs. For instance, fault detection of wind turbines is typically based on vibration and process signals analysis. Illustrated possible enhancement of configuration module is the basis for determining the energy bands in the spectra and envelope spectra in the process of identifying characteristic frequencies caused by gear defects.

Overhead transmission power lines are still one of the crucial elements of electro-energetic system. There are obvious advantages of using overhead transmission in the distribution of electricity. The amount of energy transported through a power line is determined by the distance between the wire and the ground or other objects placed beneath it (eg. trees). This distance is not fixed and depends on the overhang of the wire. This, in turn, is determined by many factors such as ambient temperature, humidity, precipitation, the value of current flowing through the wire. In order to optimize the wires electrical load, the monitoring of that overhang is required. One way to measure it is the non-contact measurement by vision system. It has the advantage, that using high-speed cameras respectively it also allows for vibration measurement and analysis of dynamic performance. That is very important while the wires are susceptible to the influence of wind, and the resulting vibrations interfere with the correct measurement of the overhang. The paper presents the results of vision measurements of the system vibrations and modal analysis carried out on their basis. The study was conducted on a specially made laboratory stand.