Alessandro Fasana

According to international standards such as the NASA-STD-7003A, space equipment must be designed to withstand strong shocks related to launch vehicles characte

Machinery diagnostics in the industrial field have assumed a fundamental role for both technical, economic and safety reasons. The use of sensors, data collection and analysis has increasingly advanced to investigate the health of machinery, predict the presence of faults and recognize their nature. The amount of data necessary for this purpose means that it is often necessary to implement dimension reduction methods to pre-process the useful features for the classification. Furthermore, the use of a multi-class dataset could involve data clustering in its multi-dimensional space. This study proposes a novel dimensionality reduction method, consisting of the combination of two different techniques. It aims at improving the quality of the features and, consequently, the classification performance with high-dimension clustered datasets. In addition, a case study is analyzed thanks to the data published by the Prognostics and Health Management Europe (PHME) society on the Data Challenge 2021. The results show an excellent recognition of the machine state of health both in terms of damage detection and identification. The performance indices also show an improvement in classification compared to other dimension reduction methods.

In the field of Diagnostics, the fundamental task of detecting damage is basically a binary classification problem, which is addressed in many cases via Novelty Detection (ND): an observation is classified as novel if it differs significantly from reference, healthy data. ND is practically implemented summarizing a multivariate dataset with univariate distance information called Novelty Index. As many different approaches are possible to produce NIs, in this analysis, the possibility of implementing a simple classifier in a reduced-dimensionality space of NIs is studied. In addition to a simple decision-tree-like classification method, the process for obtaining the NIs can result as a dimension reduction method and, in turn, the NIs can be used for other classification algorithms. In addition, a case study will be analyzed thanks to the data published by the Prognostics and Health Management Europe (PHME) society, on the occasion of the Data Challenge 2021.

Envelope demodulation of vibration signals is surely one of the most successful methods of analysis for highlighting diagnostic information of rolling element bearings incipient faults. From a mathematical perspective, the selection of a proper demodulation band can be regarded as an optimization problem involving a utility function to assess the demodulation performance in a particular band and a scheme to move within the search space of all the possible frequency bands {f, Δf} (center frequency and band size) towards the optimal one. In most of cases, kurtosis-based indices are used to select the proper demodulation band. Nevertheless, to overcome the lack of robustness to non-Gaussian noise, different utility functions can be found in the literature. One of these is the kurtosis of the unbiased autocorrelation of the squared envelope of the filtered signal found in the autogram. These heuristics are usually sufficient to highlight the defect spectral lines in the demodulated signal spectrum (i.e., usually the squared envelope spectrum (SES)), enabling bearings diagnostics. Nevertheless, it is not always the case. In this work, then, posteriori band indicators based on SES defect spectral lines are proposed to assess the general envelope demodulation performance and the goodness of traditional indicators. The Case Western Reserve University bearing dataset is used as a test case.

This paper describes the design procedure to enhance the damping properties of a multimaterial lightweight suspension arm for a C-segment vehicle. An innovative viscoelastic material has been used to join carbon fiber with steel that has a function of passive constrained layer damper and adhesive simultaneously. Therefore, the hybrid technology applied has been focused on reducing the LCA mass, diminishing the steel thickness, and adding a CFRP tailored cover without compromising the global mechanical performance. Particular attention has been paid to the investigation of the dynamic response in terms of vibration reduction, especially in the range of structure-borne frequencies of 0–600 Hz. Two different viscoelastic materials have been evaluated in such a way to compare their stiffness, damping, and dynamic properties. The experimental test results have been virtually correlated with a commercial FEM code to create the respective material card and predict the real behavior of the LCAs (original and hybrid). The experimental modal analysis has been performed and compared on both the arms highlighting a very good correlation between virtual and experimental results. In particular, the hybrid LCA allows an interesting improvement of damping ratio, about 3,5 times higher for each eigenmode than in the original solution.

Vibration Monitoring is a particular kind of Condition Monitoring meant to infer the state of health of a machine from accelerometric measurements. From a practical point of view, the scope is then to extract from the acceleration data some valuable diagnostic information which could be used to detect the presence of possible damages (i.e., to produce knowledge about the state of health). When the monitoring is implemented online, in a continuous way, the raw accelerometric data sets can be very large and complex to be dealt with, as usually involve multiple channels (i.e., multiple locations and directions) and high sample rates (i.e., order of ksps - 10 ³ samples per second), but the final knowledge about the state of health can, in principle, be summarized by a single binary information (i.e., healthy - 0 vs damaged - 1). This is commonly called Damage Detection. In this work, the big data management challenge is tackled from the point of view of statistical signal processing, so as to aggregate the multivariate data and condense them into single information of distance with respect to a healthy reference condition (i.e., the Novelty). When confounding influences (such as the work condition or the environmental condition) can be disregarded, the novelty information has a direct correspondence to the health information, so that an alarm indicating the detection of damage can be triggered upon exceeding a selected threshold for the limit novelty. Many different ways of solving such a binary classification problem can be found in the literature. Starting from the simplest, some of the more effective are compared in the present analysis, to finally select a reliable procedure for the big data management in vibration monitoring.