Signal processing is the key component of any vibration-based structural health monitoring (SHM). The goal of signal processing is to extract subtle changes in the vibration signals in order to detect, locate and quantify the damage and its severity in the structure. This paper presents a state-of-the-art review of recent articles on signal processing techniques for vibration-based SHM. The focus is on civil structures including buildings and bridges. The paper also presents new signal processing techniques proposed in the past few years as potential candidates for future SHM research. The biggest challenge in realization of health monitoring of large real-life structures is automated detection of damage out of the huge amount of very noisy data collected from dozens of sensors on a daily, weekly, and monthly basis. The new methodologies for on-line SHM should handle noisy data effectively, and be accurate, scalable, portable, and efficient computationally.

Signal Processing Techniques for Vibration-Based Health Monitoring of Smart Structures

Blind identification of full-field vibration modes from video measurements with phase-based video motion magnification

Variational mode decomposition (VMD), a recently introduced method for adaptive data analysis, has aroused much attention in various fields. However, the VMD is formulated based on the assumption of narrow-band property of the signal model. To analyze wide-band nonlinear chirp signals (NCSs), we present an alternative method called variational nonlinear chirp mode decomposition (VNCMD). The VNCMD is developed from the fact that a wideband NCS can be transformed to a narrow-band signal by using demodulation techniques. Our decomposition problem is, thus, formulated as an optimal demodulation problem, which is efficiently solved by the alternating direction method of multipliers. Our method can be viewed as a time–frequency filter bank, which concurrently extracts all the signal modes. Some simulated and real data examples are provided showing the effectiveness of the VNCMD in analyzing NCSs containing close or even crossed modes.

Nonlinear Chirp Mode Decomposition: A Variational Method

With the emergence of industry 4.0, the oil and gas (O&G) industry is now considering a range of digital technologies to enhance productivity, efficiency, and safety of their operations while minimizing capital and operating costs, health and environment risks, and variability in the O&G project life cycles. The deployment of emerging technologies allows O&G companies to construct digital twins (DT) of their assets. Considering DT adoption, the O&G industry is still at an early stage with implementations limited to isolated and selective applications instead of industry-wide implementation, limiting the benefits from DT implementation. To gain the full potential of DT and related technological adoption, a comprehensive understanding of DT technology, the current status of O&G-related DT research activities, and the opportunities and challenges associated with the deployment of DT in the O&G industry are of paramount importance. In order to develop this understanding, this paper presents a literature review of DT within the context of the O&G industry. The paper follows a systematic approach to select articles for the literature review. First, a keywords-based publication search was performed on the scientific databases such as Elsevier, IEEE Xplore, OnePetro, Scopus, and Springer. The filtered articles were then analyzed using online text analytic software (Voyant Tools) followed by a manual review of the abstract, introduction and conclusion sections to select the most relevant articles for our study. These articles and the industrial publications cited by them were thoroughly reviewed to present a comprehensive overview of DT technology and to identify current research status, opportunities and challenges of DT deployment in the O&G industry. From this literature review, it was found that asset integrity monitoring, project planning, and life cycle management are the key application areas of digital twin in the O&G industry while cyber security, lack of standardization, and uncertainty in scope and focus are the key challenges of DT deployment in the O&G industry. When considering the geographical distribution for the DT related research in the O&G industry, the United States (US) is the leading country, followed by Norway, United Kingdom (UK), Canada, China, Italy, Netherland, Brazil, Germany, and Saudi Arabia. The overall publication rate was less than ten articles (approximately) per year until 2017, and a significant increase occurred in 2018 and 2019. The number of journal publications was noticeably lower than the number of conference publications, and the majority of the publications presented theoretical concepts rather than the industrial implementations. Both these observations suggest that the DT implementation in the O&G industry is still at an early stage.

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Digital Twin for the Oil and Gas Industry: Overview, Research Trends, Opportunities, and Challenges

The idea that the information about a physical object can be separated from the object itself and then mirror or twin that object is a concept referred to as the Digital Twin. The Digital Twin is receiving a great deal of interest from manufacturers who make advanced products that have all the characteristics of complex systems. While the Digital Twin concept is becoming better fleshed out and understood, there is much more work to be accomplished. Specifically, the characteristics of the physical product as these become smart, connected product system (SCPS or Physical Twin) need to be defined and described. The success of the Digital Twin model will rest on the value it creates for both the manufacturers and the users of their products. There will also be new issues, security among the most important, that need to be surfaced and addressed.

Virtually Intelligent Product Systems: Digital and Physical Twins

A framework for blind modal identification using joint approximate diagonalization

In this paper a complex-valued formulation of the modal superposition equation is provided and shown to be equivalent to the original, real-valued Blind Modal IDentification (BMID) problem. The complex-valued variant involves the analytic form of the physical and modal responses. The formulation is shown to be more concise and straightforward than the original. It is noted that complex-valued mode shapes can be obtained using a complex version of the two-step Joint Approximate Diagonalization (JAD) algorithm. Using this approach the modal response pairing step of the original BMID method is eliminated. Since the development of the original BMID method, several new, one-step JAD algorithms have been devised. Many of the algorithms can be extended to identify complex mixing matrices. A complex version of the one-step JAD method known as the Weighted Exhaustive Diagonalization with Gauss itErations algorithm is utilized to solve for the complex mode shapes and modal responses. By using this simplified formul...

An analytic formulation for blind modal identification

Decomposing a signal into short-time narrow-banded modes

A modal identification algorithm is developed, combining techniques from Second Order Blind Source Separation (SOBSS) and State Space Realization (SSR) theory. In this hybrid algorithm, a set of correlation matrices is generated using time-shifted, analytic data and assembled into several Hankel matrices. Dissimilar left and right matrices are found, which diagonalize the set of nonhermetian Hankel matrices. The complex-valued modal matrix is obtained from this decomposition. The modal responses, modal auto-correlation functions and discrete-time plant matrix (in state space modal form) are subsequently identified. System eigenvalues are computed from the plant matrix to obtain the natural frequencies and modal fractions of critical damping. Joint Approximate Diagonalization (JAD) of the Hankel matrices enables the under determined (more modes than sensors) problem to be effectively treated without restrictions on the number of sensors required. Because the analytic signal is used, the redundant complex conjugate pairs are eliminated, reducing the system order (number of modes) to be identified half. This enables smaller Hankel matrix sizes and reduced computational effort. The modal auto-correlation functions provide an expedient means of screening out spurious computational modes or modes corresponding to noise sources, eliminating the need for a consistency diagram. In addition, the reduction in the number of modes enables the modal responses to be identified when there are at least as many sensors as independent (not including conjugate pairs) modes. A further benefit of the algorithm is that identification of dissimilar left and right diagonalizers preclude the need for windowing of the analytic data. The effectiveness of the new modal identification method is demonstrated using vibration data from a 6 DOF simulation, 4-story building simulation and the Heritage court tower building.

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A Modal Identification Algorithm Combining Blind Source Separation and State Space Realization

In this paper, a type of response spectra in which fatigue damage is the ordinate is reviewed. Computation of this Fatigue Damage Spectrum (FDS) in both the time and frequency domains is discussed. The method of Fatigue Damage Equivalent vibration Testing (FDET) is discussed using the FDS as the measure of environment severity. Improvements in each technique over previous work are introduced and the concepts are presented in a unified fashion. Efficient computation of the various elements involved in the FDS and FDET is discussed and examples are given.

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Scot McNeill

Papers

A framework for blind modal identification using joint approximate diagonalization

An analytic formulation for blind modal identification

Decomposing a signal into short-time narrow-banded modes

A Modal Identification Algorithm Combining Blind Source Separation and State Space Realization

Implementing the Fatigue Damage Spectrum and Fatigue Damage Equivalent Vibration Testing