Liming W. Salvino

Hybrid wireless hull monitoring system for naval combat vessels

There is increasing interest by the naval engineering community in permanent monitoring systems that can monitor the structural behaviour of ships during their operation at sea. This study seeks to reduce the cost and installation complexity of hull monitoring systems by introducing wireless sensors into their architectural designs. Wireless sensor networks also provide other advantages over their cable-based counterparts such as adaptability, redundancy, and weight savings. While wireless sensors can enhance functionality and reduce cost, the compartmentalised layout of most ships requires some wired networking to communicate data globally throughout the ship. In this study, 20 wireless sensing nodes are connected to a ship-wide fibre-optic data network to serve as a hybrid wireless hull monitoring system on a high-speed littoral combat vessel (FSF-1 Sea Fighter). The wireless hull monitoring system is used to collect acceleration and strain data during unattended operation during a one-month period at sea. The key findings of this study include that wireless sensors can be effectively used for reliable and accurate hull monitoring. Furthermore, the fact that they are low-cost can lead to higher sensor densities in a hull monitoring system thereby allowing properties, such as hull mode shapes, to be accurately calculated.

Detecting impact damage in experimental composite structures: an information-theoretic approach

This work describes a procedure for detecting the presence of damage-induced nonlinearities in composite structures using only the structures vibrational response. Damage is assumed to change the coupling between different locations on the structure from linear to nonlinear. Utilizing concepts from the field of information theory, we are able to deduce the form of the underlying structural model (linear/nonlinear), and hence detect the presence of the damage. Because information theoretics are model independent they may be used to capture both linear and nonlinear dynamical relationships. We describe two such metrics, the time delayed mutual information and time delayed transfer entropy, and show how they may be computed from time series data. We make use of surrogate data techniques in order to place the question of damage in a hypothesis testing framework. Specifically, we construct surrogate data sets from the original that preserve only the linear relationships among the data. We then compute the mutual information and the transfer entropy on both the original and surrogate data and quantify the discrepancy in the results as a measure of nonlinearity in the structure. Thus, we do not require the explicit measurement of a baseline data set. The approach is demonstrated to be effective in diagnosing the presence of impact damage in a thick composite sandwich plate. We also show how the approach can be used to detect impact damage in a composite UAV wing subject to ambient gust loading.

Health monitoring of one-dimensional structures using empirical mode decomposition and the Hilbert-Huang transform

This paper discusses a new signal processing tool involving the use of empirical mode decomposition and its application to health monitoring of structures. Empirical mode decomposition is a time series analysis method that extracts a custom set of basis sets to describe the vibratory response of a system. In conjunction with the Hilbert Transform, the empirical mode decomposition method provides some unique information about the nature of the vibratory response. In this paper, the method is used to process time series data from a variety of one-dimensional structures with and without structural damage. Derived basis sets are then processed through the Hilbert-Huang Transform to obtain phase and damping information. This phase and damping information is later processed to extract the underlying incident energy propagating through the structure. This incident energy is also referred to as the dereverberated response of a structure. Using simple physics based models of one-dimensional structures, it is possible to determine the location and extent of damage by tracking phase properties between successive degrees of freedom. This paper presents results obtained on a civil building model. Results illustrate that this new time-series method is a powerful signal processing tool that tracks unique features in the vibratory response of structures.

EMD AND INSTANTANEOUS PHASE DETECTION OF STRUCTURAL DAMAGE

In this chapter, a new structural health-monitoring and damage-detection method is presented. A general time-frequency data analysis technique (empirical mode decomposition and the Hilbert-Huang spectrum) in conjunction with a wavemechanics-based concept is developed to provide a diagnostic tool for detecting and interpreting adverse changes in a structure. Sets of simple basis function components, known as intrinsic mode functions (IMF), are extracted adaptively from the measured structural response time series data. These IMFs are amplitudeand phase-modulated signals and are used to define the instantaneous phases of structural waves. The state of a structure is evaluated, and damage is identified based on these instantaneous phase features. Furthermore, fundamental relationships are developed connecting the instantaneous phases to a local physics-based structural representation in order to infer damage in terms of physical parameters, such as structural mass, stiffness, and damping. Damage-detection applications are investigated by using numerical simulations and a variety of laboratory experiments with simple structures. Several different types of excitation mechanisms are used for dynamic input to the structures. The time series output of the structural response is then analyzed by using the new method. The instantaneous phase relationships are extracted and examined for changes which may have occurred due to damage. These results are compared to those from other newly developed detection methods, such as an algorithm based on the geometric properties of a chaotic attractor. The studies presented here show that our method, without linear-system or stationary-process assumptions, can identify and locate structural damage and permit the further development of a reliable real-time structural health-monitoring and damage-detection system.

A PROBABILISTIC MODEL UPDATING ALGORITHM FOR FATIGUE DAMAGE DETECTION IN ALUMINUM HULL STRUCTURES

The use of aluminum alloys in the design of naval structures offers the benefit of light-weight ships that can travel at high-speed. However, the use of aluminum poses a number of challenges for the naval engineering community including higher incidence of fatigue-related cracks. Early detection of fatigue induced cracks enhances maintenance of the ships and is critical for preventing the catastrophic failure of the hull. Furthermore, monitoring the integrity of the aluminum hull can provide valuable information for estimating the residual life of hull components. This paper presents a model-based damage detection methodology for fatigue assessment of hulls that are instrumented with a long-term hull monitoring system. At the core of the data driven damage detection approach is a Bayesian model updating algorithm enhanced with systematic enumeration and pruning of candidate solutions. The Bayesian model updating approach significantly reduce the computational effort by systematically narrowing the search space using errors functions constructed using the estimated modal properties associated with the condition of the structure. This study proposes the use of the Bayesian model updating technique to detect damage in an aluminum panel modeled using high-fidelity finite element models. The performance of the proposed damage detection method is tested through simulation of a progressively growing fatigue crack introduced in the vicinity of a welded stiffener element. An experimental study verifies the accuracy of the proposed damage detection method using an aluminum plate excited with a controlled excitation in the laboratory.

Predictability in time series

Abstract We introduce a technique to characterize and measure predictability in time series. The technique allows one to formulate precisely a notion of the predictable component of given time series. We illustrate our method for both numerical and experimental time series data.

Bayesian Model Updating Approach for Systematic Damage Detection of Plate-Type Structures

This paper presents a model-based monitoring framework for the detection of fatigue-related crack damages in plate-type structures commonly seen in aluminum ship hulls. The monitoring framework involves vibration-based damage detection methodologies and finite element modeling of continuum plate structures. A Bayesian-based damage detection approach is adopted for locating probable damage areas. Identifying potential damage locations by evaluating all possible combinations of finite elements in the model is computationally infeasible. To reduce the search space and computational efforts, initial knowledge of the probable damage zones and a heuristic-based branch-and-bound scheme are systematically included in the Bayesian damage detection framework. In addition to an overview of the model-based monitoring framework, preliminary results from numerical simulations and experimental tests for a plate specimen with a welded stiffener are presented to illustrate the Bayesian damage detection approach and to demonstrate the potential application of the approach to detect fatigue cracks in metallic plates.

Fatigue life monitoring of metallic structures by decentralized rainflow counting embedded in a wireless sensor network

Fatigue is one of the most widespread damage mechanisms found in metallic structures. Fatigue is an accumulated degradation process that occurs under cyclic loading, eventually inducing cracking at stress concentration points. Fatigue-related cracking in operating structures is closely related with statistical loading characteristics, such as the number of load cycles, cycle amplitudes and means. With fatigue cracking a prevalent failure mechanism of many engineered structures including ships, bridges and machines, among others, a reliable method of fatigue life estimation is direly needed for future structural health monitoring systems. In this study, a strategy for fatigue life estimation by a wireless sensor network installed in a structure for autonomous health monitoring is proposed. Specifically, the computational resources available at the sensor node are leveraged to compress raw strain time histories of a structure into a more meaningful and compressed form. Simultaneous strain sensing and on-board rainflow counting are conducted at individual wireless sensors with fatigue life prediction made using extracted amplitudes and means. These parameters are continuously updated during long-term monitoring of the structure. Histograms of strain amplitudes and means stored in the wireless sensor represent a highly compressed form of the original raw data. Communication of the histogram only needs to be done by request, dramatically reducing power consumption in the wireless sensing network. Experimental tests with aluminum specimens in the laboratory are executed for verification of the proposed damage detection strategy.

Detection and diagnosis of dynamics in time series data: Theory of noise reduction

We describe a general four‐step approach to chaotic noise reduction: embedding, data state vector alteration, disembedding and iteration. In this way, a noise reduction algorithm may be regarded as a repeated application of an operator A:v(t)→v(t) on a space of scalar time series. We suggest that systematics of the response of a time series to iteration of A can be studied to estimate quantitatively optimal algorithm parameters, such as best embedding trial dimension, d=dpk, and number of iterations, nM=nM(d), and other quantities, parameters depending on A, to achieve maximum improvement.

Detecting structural damage using adaptive feature extraction from transient signals

The focus of this work is on damage detection in transient structural response time series data recorded during an underwater shock experiment. A unique data-driven approach where damage features are extracted, evaluated, and determined based on the instantaneous phases of structural waves was applied to detect damage for a large composite structure. Measured time series data was first decomposed adaptively into a set of basis functions, known as Intrinsic Mode Functions (IMFs), using the method of Empirical Mode Decomposition. Instantaneous phases are then defined based on the IMFs, which can be used to represent nonlinear and non-stationary signals. Damage features are then formulated and tracked in order to determine the state of a structure. This approach was developed based on a previously introduced fundamental relationship connecting the instantaneous phases of a measured time series to structural mass and stiffness parameters. A simple damage index based on the instantaneous phase relationship is used to show the effectiveness of this method for structural health monitoring applications.