Usually, dam monitoring systems are based on both boundary conditions (temperature, rainfall, water level, etc.) and structural responses. Statistical analysis tools are widely used to determine eventual unwanted behaviors. The main drawback of this approach is that the structural response quantities are related to the external loads using analytical functions, whose parameters do not have physical meaning. In this paper a new approach, based on

The paper presents a new approach for machine vibration analysis and health monitoring based on blind source separation (BSS). So, the problem is transferred from the original space of the measurements to the space of independent sources, where the reduced number of components is going to simplify the monitoring problem while the vibration analysis methods are going to be applied for scalar signals. The assessment of the approach on a real machine

The paper presents a new approach for machine vibration analysis and health monitoring based on blind source separation (BSS). So, the problem is transferred from the original space of the measurements to the space of independent sources, where the reduced number of components is going to simplify the monitoring problem while the vibration analysis methods are going to be applied for scalar signals. The assessment of the approach on a real machine

The main drawback of the statistical tools, applied in dam monitoring, is that the structural response quantities are related to the external loads using analytical functions, whose parameters do not have physical meaning. In this paper a new approach, based on Blind Source Separation (BSS) to find out the contributions of the external loads: air temperature and hydrostatic pressure to structure deformation and to identify the irreversible component

The objective of the paper is to develop a robust change detection and diagnosis scheme. In the first part of the paper we give the conceptual description of some classical change detection schemes based on sliding windows and likelihood techniques. Then, starting from these classical change detection schemes, a new algorithm able to discriminate between the model parameter and noise variance changes is presented. Finally, we include some Monte Carlo

The problem of change detection and data segmentation has received considerable attention in a research context and appears to be the central issue in various application areas. The change detection and segmentation model used in this paper is the simplest extension of the linear regression models to data with abruptly changing properties. In the first part of the paper we give a general view on the main techniques used in change detection and segmentation:

The paper presents an approach able to separate the vibrations induced by underground traffic from the vibrations induced by other sources, based on Second Order Blind Identification (SOBI) algorithm. The signals recorded in different locations of an instrumented building are mixed signals from different internal and external vibration sources. The blind source separation algorithm will estimate the independent vibration sources, as well as the mixing

The paper presents a new approach for machine vibration analysis and health monitoring combining blind source separation (BSS) and change detection in source signals. So, the problem is transferred from the original space of the measurements to the space of independent sources, where the reduced number of components is going to simplify the monitoring problem while the change detection methods are going to be applied for scalar signals. The assessment

Reliable earthquake waves characterization is essential for better understanding wave propagation phenomena and the characterization of the local site effects. Separate time and frequency analysis by themselves do not fully describe the nature of these dynamic loads. Significant efforts have been made in the last years to permit the temporal evolution representation of non-stationary spectral characteristics. The objective of this paper is to present

The paper presents a new approach for machine vibration analysis and health monitoring combining blind source separation (BSS) and change detection in source signals. So, the problem is translated from the space of the measurements to the space of independent sources, where the reduced number of components simplifies the monitoring problem and where the change detection methods are applied for scalar signals. The approach has been tested in simulation