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.

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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.

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.

With the development of concepts of industry 4.0, condition monitoring techniques are changing. Large amounts of generated data require diagnostic procedures to be automated, which drives the need for new and better methods of autonomous interpretations of vibration condition monitoring data. However, if new methods are to be operational, they need to be verified under real industrial conditions and compared with well-established expert-based diagnostic techniques. This article introduces the novel algorithm of data preprocessing for the nearest-neighbor-based anomaly detection. This approach is validated on real industrial machinery in a series of case studies. The population of over-hung centrifugal fans, employed in the same industrial process, were monitored continuously according to the proposed methodology for an extended time period. Piezoceramic accelerometers were used to register time-domain vibration data. The data were processed to extract several signal features to serve as inputs to the anomaly detection algorithm. The novel solution is compared to the well-established condition monitoring approach. Presented data include not only the intact state of machinery but also a machine breakdown case and serious deterioration of the machine condition. The influence of maintenance work is also presented in the article. Authors show the data-driven approach to condition monitoring, which can be used as one of many predictive maintenance techniques.