Vibration control by means of piezoelectric actuators shunted with LR impedances: Performance and robustness analysis

This paper deals with passive monomodal vibration control by shunting piezoelectric actuators to electric impedances constituting the series of a resistance and an inductance. Although this kind of vibration attenuation strategy has long been employed, there are still unsolved problems; particularly, this kind of control does suffer from issues relative to robustness because the features of the electric impedance cannot be adapted to changes of the system. This work investigates different algorithms that can be employed to optimise the values of the electric components of the shunt impedance. Some of these algorithms derive from the theory of the tuned mass dampers. First a performance analysis is provided, comparing the attenuation achievable with these algorithms. Then, an analysis and comparison of the same algorithms in terms of robustness are carried out. The approach adopted herein allows identifying the algorithm capable of providing the highest degree of robustness and explains the solutions that can be employed to resolve some of the issues concerning the practical implementation of this control technique. The analytical and numerical results presented in the paper have been validated experimentally by means of a proper test setup

A Damage Detection Approach for Axially Loaded Beam-like Structures Based on Gaussian Mixture Model

Axially loaded beam-like structures represent a challenging case study for unsupervised learning vibration-based damage detection. Under real environmental and operational conditions, changes in axial load cause changes in the characteristics of the dynamic response that are significantly greater than those due to damage at an early stage. In previous works, the authors proposed the adoption of a multivariate damage feature composed of eigenfrequencies of multiple vibration modes. Successful results were obtained by framing the problem of damage detection as that of unsupervised outlier detection, adopting the well-known Mahalanobis squared distance (MSD) to define an effective damage index. Starting from these promising results, a novel approach based on unsupervised learning data clustering is proposed in this work, which increases the sensitivity to damage and significantly reduces the uncertainty associated with the results, allowing for earlier damage detection. The novel approach, which is based on Gaussian mixture model, is compared with the benchmark one based on the MSD, under the effects of an uncontrolled environment and, most importantly, in the presence of real damage due to corrosion

Damage detection based on strain transmissibility for beam structure by using distributed fiber optics

Structural damage identification is a coral and challenging research topic. Research mainly focuses on identification and detection of linear damage in structures by using modal parameters such as change of natural frequency, frequency response function, mode shape, etc. Transmissibility is conventionally defined as the spectra ratio of two measurement points, which has been utilized for damage identification as a powerful damage indicator. In this paper, strain transmissibility, defined as ratio of strain response spectra, is proposed as a new damage indicator. In order to achieve more precise sensing information, distributed fiber optics has been applied to damage detection on a beam structure, which adds new capability of sensing with its combination of high spatial density sensing and dynamic acquisition over a single optical fiber sensor. A numerical simulation has been conducted to investigate the feasibility of strain transmissibility for damage detection which has revealed a better performance compared to traditional transmissibility. The applicability of the proposed method has been confirmed by applying distributed fiber optics on a clamped-clamped beam. Both simulation and experiment validate the effectiveness of damage detection approach based on strain transmissibility by using distributed fiber optics

Experimental evaluation of environmental effects on a polymer-coated aluminium structure: a time-series analysis and pattern recognition approach

Temperature variation is an important issue that needs to be considered when trying to develop a reliable Structural Health Monitoring (SHM) strategy. In the case that a data-based approach is chosen for damage detection, environmental fluctuations could be erroneously regarded as an abnormal condition of the structure and could mask the presence of damage. One of the objectives of the current work is to examine a statistical pattern recognition approach for novelty detection under different temperature conditions. A second important issue that could hinder the reliability of a SHM strategy is any kind of nonlinear behaviour, not associated with damage, in a system. For the purposes of this paper, the dynamic behaviour of a polymer-coated aluminium structure with ribs fixed with bolts is examined. The autoregressive parameters are the damage sensitive features and later, it is performed Principal Component Analysis (PCA) for robust novelty detection that takes into account the temperature variation

Bridge pier scour measurement by means of Bragg grating arrays

Abstract. This paper deals with a new method to measure scour level at bridge piers. The proposed technique is based on an array of Bragg grating temperature sensors, heated by an electrical circuit. The Bragg gratings in water sense a lower temperature than those buried in the river bed, because of the different heat scattering principles in the two situations. Furthermore the response of each sensor is slower if it is buried in the bed, with respect to the case it is in water. The paper presents laboratory tests, showing the method effectiveness and reliability, and it explains the advantages with respect to other more traditional methodologies to measure scour level

Neurological assessment with validated tools in general ICU : multicenter, randomized, before and after, pragmatic study to evaluate the effectiveness of an e-learning platform for continuous medical education

BACKGROUND: International guidelines recommend systematic assessment of pain, agitation/sedation and delirium with validated scales for all ICU patients. However, these evaluations are often not done. We have created an e-learning training platform for the continuous medical education, and assessed its efficacy in increasing the use of validated tools by all medical and nursing staff of the participating ICUs during their daily practice. METHODS: Multicenter, randomized, before and after study. The eight participating centers were randomized in two groups, and received training at different times. The use of validated tools (Verbal Numeric Rating or Behavioral Pain Scale for pain; Richmond Agitation-Sedation Scale for agitation; Confusion Assessment Method for the ICU for delirium) was evaluated from clinical data recorded in medical charts during a week, with follow-up up to six months after the training. All the operators were invited to complete a questionnaire, at baseline and after the training. RESULTS : Among the 374 nurses and physicians involved, 140 (37.4%) completed at least one of the three courses. The assessment of pain (38.1 vs. 92.9%, P<0.01) and delirium (0 vs. 78.6%, P<0.01) using validated tools significantly increased after training. Observation in the follow-up showed further improvement in delirium monitoring, with no signs of extinction for pain and sedation/agitation measurements. CONCLUSIONS: This e-learning program shows encouraging effectiveness, and the increase in the use of validated tools for neurological monitoring in critically ill patients lasts over time.BACKGROUND: International guidelines recommend systematic assessment of pain, agitation/sedation and delirium with validated scales for all ICU patients. However, these evaluations are often not done. We have created an e-learning training platform for the continuous medical education, and assessed its efficacy in increasing the use of validated tools by all medical and nursing staff of the participating ICUs during their daily practice. METHODS: Multicenter, randomized, before and after study. The eight participating centers were randomized in two groups, and received training at different times. The use of validated tools (Verbal Numeric Rating or Behavioral Pain Scale for pain; Richmond Agitation-Sedation Scale for agitation; Confusion Assessment Method for the ICU for delirium) was evaluated from clinical data recorded in medical charts during a week, with follow-up up to six months after the training. All the operators were invited to complete a questionnaire, at baseline and after the training. RESULTS : Among the 374 nurses and physicians involved, 140 (37.4%) completed at least one of the three courses. The assessment of pain (38.1 vs. 92.9%, P<0.01) and delirium (0 vs. 78.6%, P<0.01) using validated tools significantly increased after training. Observation in the follow-up showed further improvement in delirium monitoring, with no signs of extinction for pain and sedation/agitation measurements. CONCLUSIONS: This e-learning program shows encouraging effectiveness, and the increase in the use of validated tools for neurological monitoring in critically ill patients lasts over time