Structural Health Monitoring (SHM) systems are critical for monitoring aging infrastructure (such as buildings or bridges) in a cost-effective manner. Such systems typically involve collections of battery-operated wireless sensors that sample vibration data over time. After the data is transmitted to a central node, modal analysis can be used to detect damage in the structure. In this paper, we propose and study three frameworks for Compressive Sensing (CS) in SHM systems; these methods are intended to minimize power consumption by allowing the data to be sampled and/or transmitted more efficiently. At the central node, all of these frameworks involve a very simple technique for estimating the structure's mode shapes without requiring a traditional CS reconstruction of the vibration signals; all that is needed is to compute a simple Singular Value Decomposition. We provide theoretical justification (including measurement bounds) for each of these techniques based on the equations of motion describing a simplified Multiple-Degree-Of-Freedom (MDOF) system, and we support our proposed techniques using simulations based on synthetic and real data.

Modal Analysis With Compressive Measurements

The Lamb wave phased array structural health monitoring method is effective in structural damage monitoring. In this method, the damage scattering signal can be obtained by comparing the damage structural response signal with health structural response signal, and it can be used for structural damage identification. But in the structural health monitoring based on wireless sensor networks, this method has some inevitable defects in data transmission. A large number of sampling data of damage response signal will cause huge wireless communication burden. To solve this problem, we proposed a phased array image method based on compressive sampling and data fusion for wireless structural damage monitoring. First, compressive sampling signal by compressive sampling method was collected. Then, data fusion for multi-sensor’s damage response signal was implemented in phased array. Finally, the Lamb wave phased array damage identification method based on compressive sampling and data fusion was proposed. Experimental results on carbon composite structure show that the proposed method can largely save network bandwidth and energy. This method can also realize the damage identification accurately on the aviation aluminum plate and keep the detection error within 0.82 mm.

Compressive sampling and data fusion-based structural damage monitoring in wireless sensor network

Video-based techniques for identification of structural dynamics have the advantage that they are very inexpensive to deploy compared to conventional accelerometer or strain gauge techniques. When structural dynamics from video is accomplished using full-field, high-resolution analysis techniques utilizing algorithms on the pixel time series such as principal components analysis and solutions to blind source separation the added benefit of high-resolution, full-field modal identification is achieved. An important property of video of vibrating structures is that it is particularly sparse. Typically video of vibrating structures has a dimensionality consisting of many thousands or even millions of pixels and hundreds to thousands of frames. However the motion of the vibrating structure can be described using only a few mode shapes and their associated time series. As a result, emerging techniques for sparse and random sampling such as compressive sensing should be applicable to performing modal identification on video. This work presents how full-field, high-resolution, structural dynamics identification frameworks can be coupled with compressive sampling. The techniques described in this work are demonstrated to be able to recover mode shapes from experimental video of vibrating structures when 70% to 90% of the frames from a video captured in the conventional manner are removed.

Sparse and random sampling techniques for high-resolution, full-field, bss-based structural dynamics identification from video

The acoustic emission (AE) phenomena generated by a rapid release in the internal stress of a material represent a
promising technique for structural health monitoring (SHM) applications AE events typically result in a discrete number
of short-time, transient signals The challenge associated with capturing these events using classical techniques is that
very high sampling rates must be used over extended periods of time The result is that a very large amount of data is
collected to capture a phenomenon that rarely occurs Furthermore, the high energy consumption associated with the
required high sampling rates makes the implementation of high-endurance, low-power, embedded AE sensor nodes
difficult to achieve The relatively rare occurrence of AE events over long time scales implies that these measurements
are inherently sparse in the spike domain The sparse nature of AE measurements makes them an attractive candidate for
the application of compressed sampling techniques Collecting compressed measurements of sparse AE signals will relax
the requirements on the sampling rate and memory demands The focus of this work is to investigate the suitability of
compressed sensing techniques for AE-based SHM The work explores estimating AE signal statistics in the compressed
domain for low-power classification applications In the event compressed classification finds an event of interest, ι1
norm minimization will be used to reconstruct the measurement for further analysis The impact of structured noise on
compressive measurements is specifically addressed The suitability of a particular algorithm, called Justice Pursuit, to
increase robustness to a small amount of arbitrary measurement corruption is investigated

/pdf/the-application-of-compressed-sensing-to-long-term-acoustic-4xefba7mf7.pdf

The application of compressed sensing to long-term acoustic emission-based structural health monitoring

The development of in-situ structural health monitoring (SHM) techniques represents a challenge for offshore wind turbines (OWTs) in order to reduce the cost of the operation and maintenance (O&M) of safety-critical components and systems. This thesis proposes an in-situ wireless SHM system based on acoustic emission (AE) techniques. The proposed wireless system of AE sensor networks is not without its own challenges amongst which are requirements of high sampling rates, limitations in the communication bandwidth, memory space, and power resources. This work is part of the HEMOWFP7 Project, ‘The Health Monitoring of Offshore Wind Farms’. The present study investigates solutions relevant to the abovementioned challenges. Two related topics have been considered: to implement a novel in-situ wireless SHM technique for wind turbine blades (WTBs); and to develop an appropriate signal processing algorithm to detect, localise, and classify different AE events. The major contributions of this study can be summarised as follows: 1) investigating the possibility of employing low sampling rates lower than the Nyquist rate in the data acquisition operation and content-based feature (envelope and time-frequency data analysis) for data analysis; 2) proposing techniques to overcome drawbacks associated with lowering sampling rates, such as information loss and low spatial resolution; 3) showing that the time-frequency domain is an effective domain for analysing the aliased signals, and an envelope-based wavelet transform cross-correlation algorithm, developed in the course of this study, can enhance the estimation accuracy of wireless acoustic source localisation; 4) investigating the implementation of a novel in-situ wireless SHM technique with field deployment on the WTB structure, and developing a constraint model and approaches for localisation of AE sources and environmental monitoring respectively. Finally, the system has been experimentally evaluated with the consideration of the localisation and classification of different AE events as well as changes of environmental conditions. The study concludes that the in-situ wireless SHM platform developed in the course of this research represents a promising technique for reliable SHM for OWTBs in which solutions for major challenges, e.g., employing low sampling rates lower than the Nyquist rate in the acquisition operation and resource constraints of WSNs in terms of communication bandwidth and memory space are presented.

/pdf/in-situ-health-monitoring-for-wind-turbine-blade-using-33057qozy2.pdf

In-situ health monitoring for wind turbine blade using acoustic wireless sensor networks at low sampling rates

One of the principal challenges facing the structural health monitoring (SHM) community is taking large, heterogeneous sets of data collected from sensors, and extracting information that allows the estimation of the remaining service life of a structure. Another important challenge is to collect relevant data from a structure in a manner that is cost effective, and respects the size, weight, cost, energy consumption, and bandwidth limitations placed on the system. In this work we explore the suitability of compressed sensing to address both challenges. In this work a digital version of a compressed sensor is implemented on-board a microcontroller similar to those used in embedded SHM sensor nodes. The sensor node is tested in a surrogate SHM application requiring acceleration measurements. Currently the prototype compressed sensor is capable of collecting compressed coefficients from measurements and sending them to an off-board processor for reconstruction using L1 norm minimization. A compressed version of the matched filter known as the smashed filter, has also been implemented on-board the sensor node, and its suitability for detecting structural damage will be discussed.

Donald R Hush

Papers

The application of compressed sensing to detecting damage in structures