applied  
sciences  
Editorial for the Special Issue on Novel Approaches for  
Structural Health Monitoring II  
[PERSON]  
Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, 10129 Turin, Italy;  
[EMAIL_ADDRESS]  
The emphasis of this Special Issue is on showcasing the most recent advancements in the field of Structural Health Monitoring (SHM), accounting for all its applications in mechanical systems and civil structures or infrastructures. The eight papers presented here represent relevant contributions in their specific aspects. In the work of [PERSON], [PERSON] and [PERSON] [1], transmission maintenance for the condition monitoring of gearboxes was addressed. Specifically, the researchers considered a back-to-back test rig consisting of two parallel axes gearboxes connected by means of two shafts in a closed mechanical loop. They also focused on surface damage (namely, pitting), numerically simulating several types and severities of damage. The proposed Machine Learning (ML) approach, based on a multilayer perceptron, is able to perform damage detection, localization and quantification. The training phase of the Artificial Neural Network (ANN) was performed resorting to signal examples generated by a hybrid analytical-numerical model. [PERSON] et al. [2] presented a synergistic non-destructive method for the automated inspections of aircraft composite structures. Specifically, a sensor fusion procedure was outlined, combining two image-based non-destructive evaluation (NDE) techniques: phased-array ultrasonic testing and infrared thermography. The approach was experimentally validated on an aircraft-grade painted composite material skin panel with stringers. Importantly, the miniaturized sensor systems tested and validated here were intended to be integrated in a vortex-robotic platform inspector, in the framework of the Horizon-2020 'Complinova' project. The work of [PERSON] and [PERSON] [3] concerned the well-known Stochastic Subspace Identification (SSI) technique, widely used for the output-only system identification of a target structure or mechanical system from ambient vibration testing. This falls into the field of Operational Modal Analysis (OMA). Importantly, two main variants of the SSI algorithm exist: the covariance-driven SSI (SSI-COV) and the data-driven SSI (SSI-DATA). In brief, the second option (SSI-DATA) operates directly on measured output response data with no further processing. Conversely, SSI-COV utilizes the covariance functions for the purpose of modal parameter estimation; these need to be estimated in advance from raw output time histories. In this context, the authors introduced a procedure to solve the system matrix in SSI-COV in conjunction with SSI-DATA, allowing modal estimation to be well implemented. [PERSON] and [PERSON] [4] discussed an application of Instantaneous Spectral Entropy (ISE) for the real-time condition monitoring of a faulty three-stage gearbox. In particular, the case study came from a 2.5 MW Nordex N100 wind turbine located in Northern Sweden. The proposed algorithm employs the instantaneous formulation of Shannon Spectral Entropy (SSE), which was proven to be damage-sensitive in previous studies on masonry buildings and steel pipelines, in combination with Continuous Wavelet Transform (CWT). The Generalized Morse Wavelet (GMW) was proposed as the best choice for the CWT mother wavelet. A sensitivity analysis was performed on the two GMW parameters(symmetry \(\gamma\) and compactness \(\beta\)), suggesting the use of \(3\leq\gamma\leq 4\) and \(20\leq\beta\leq 40\) to increase fault detectability. The work of [PERSON] et al. [5] explored a remote sensing approach to SHM, based on interferometric data and focusing on representation techniques that can be adopted to highlight their advantages for the field. In the paper, the authors analyzed Line-of-Sight displacement records from the urban area of Rome (Italy). They analyzed an area subject to the construction of a new subway line.