Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

Roadmap on optical security

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

PhD ThesisThe 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 propos- es 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 HEMOW- FP7 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 pro- cessing algorithm to detect, localise, and classify different AE events. The major contri- butions 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 opera- tion 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 localisa- tion; 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 lo- calisation 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 presente

Embedded systems and advanced signal processing for Acousto- Ultrasonic Inspections

Non Destructive Testing (NDT) and Structural Health Monitoring (SHM) are becoming essential in many application contexts, e.g. civil, industrial, aerospace etc., to reduce structures maintenance costs and improve safety. Conventional inspection methods typically exploit bulky and expensive instruments and rely on highly demanding signal processing techniques. The pressing need to overcome these limitations is the common thread that guided the work presented in this Thesis. In the first part, a scalable, low-cost and multi-sensors smart sensor network is introduced. The capability of this technology to carry out accurate modal analysis on structures undergoing flexural vibrations has been validated by means of two experimental campaigns. Then, the suitability of low-cost piezoelectric disks in modal analysis has been demonstrated. To enable the use of this kind of sensing technology in such non conventional applications, ad hoc data merging algorithms have been developed. In the second part, instead, imaging algorithms for Lamb waves inspection (namely DMAS and DS-DMAS) have been implemented and validated. Results show that DMAS outperforms the canonical Delay and Sum (DAS) approach in terms of image resolution and contrast. Similarly, DS-DMAS can achieve better results than both DMAS and DAS by suppressing artefacts and noise. To exploit the full potential of these procedures, accurate group velocity estimations are required. Thus, novel wavefield analysis tools that can address the estimation of the dispersion curves from SLDV acquisitions have been investigated. An image segmentation technique (called DRLSE) was exploited in the k-space to draw out the wavenumber profile. The DRLSE method was compared with compressive sensing methods to extract the group and phase velocity information. The validation, performed on three different carbon fibre plates, showed that the proposed solutions can accurately determine the wavenumber and velocities in polar coordinates at multiple excitation frequencies

Polycrystalline Silicon Capacitive MEMS Strain Sensor for Structural Health Monitoring of Wind Turbines

Wind energy is a fast-growing sustainable energy technology and driven by the need for more efficient energy harvesting, size of the wind turbines has increased over the years for both off-shore and land-based installations. Therefore, structural health monitoring and maintenance of such turbine structures have become critical and challenging. In order to keep the number of physical inspections to minimum without increasing the risk of structural failure, a precise and reliable remote monitoring system for damage identification is necessary. Condition-based maintenance which significantly improves safety compared to periodic visual inspections, necessitates a method to determine the condition of machines while in operation and involves the observation of the system by sampling dynamic response measurements from a group of sensors and the analysis of the data to determine the current state of system health. This goal is being pursued in this thesis through the development of reliable sensors, and reliable damage detection algorithms. Blade strain is the most important quantities to judge the health of wind turbine structure. Sensing high stress fields or early detection of cracks in blades bring safety and saving in rehabilitation costs. Therefore, high performance strain measurement system, consisting of sensors and interface electronics, is highly desirable and the best choice. It has been revealed that the conventional strain gauge techniques exhibit significant errors and uncertainties when applied to composite materials of wind turbine blades. Micro-electro-mechanical system (MEMS) based sensors are very attractive among other sensing techniques owing to high sensitivity, low noise, better scaling characteristics, low cost and higher potential for integration with low power CMOS circuits. MEMS sensors that are fabricated on a chip can be either bonded to the surface of wind turbine blade or embedded into the fiber reinforced composite. Therefore, MEMS technology is selected to fabricate the strain sensor in this work. Two new sensor structures that can be used for strain measurement are designed. While the proposed sensors focus on high sensitivity, they are based on simple operating principle of comb-drive differential variable capacitances and chevron displacement amplification. Device performances are validated both by analytical solutions and finite element method simulations. The transmission of strain fields in adhesively bonded strain sensors is also studied. In strain sensors that are attached to host structures using adhesive layers such as epoxy, complete strain transfer to the sensor is hindered due to the influence of the adhesive layer on the transfer. An analytical model, validated by finite element method simulation, to provide insight and accurate formulation for strain transfer mechanism for bonded sensors is developed. The model is capable of predicting the strain transmission ratio through a sensor gauge factor, and it clearly establishes the effects of the flexibility, length, and thickness of the adhesive layer and sensor substrate. Several fabrication steps were required to realize the MEMS capacitive strain sensor in our lab. Polycrystalline silicon is selected as the structural layer and silicon nitride as the sacrificial layer. Polysilicon is deposited using LPCVD and SiN is deposited by PECVD in our lab. A comprehensive material study of silicon nitride and polycrystalline silicon layers is therefore performed. The whole fabrication process involves deposition, etching, and photolithography of five material layers. Although this process is developed to realize the MEMS strain sensors, it is also able to fabricate other designs of surface micromachining structures as well. The fabricated MEMS capacitive strain sensors are tested on a test fixture setup. The measurement setup is created under the probe station by using a cantilever beam fixed on one side and free on other side where a micrometer applies accurate displacement. The displacement creates bending stress on the beam which transfers to the MEMS sensor through the adhesive bond. Measurement results are in a good match with the simulation results. Finally, a real-time non-destructive health monitoring technique based on multi-sensor data fusion is proposed. The objective is to evaluate the feasibility of the proposed method to identify and localize damages in wind turbine blades. The structural properties of turbine blade before and after damage are investigated and based on the obtained results, it is shown that information from smart sensors, measuring strains and vibrations, distributed over the turbine blades can be used to assist in more accurate damage detection and overall understanding of the health condition of blades. Data fusion technique is proposed to combine the diagnostic tools to improve the detection system with providing a more robust reading and fewer false alarms

COMPRESSION OF WEARABLE BODY SENSOR NETWORK DATA USING IMPROVED TWO-THRESHOLD-TWO-DIVISOR DATA CHUNKING ALGORITHM

Compression plays a significant role in Body Sensor Networks (BSN) data since the sensors in BSNs have limited battery power and memory. Also, data needs to be transmitted fast and in a lossless manner to provide near real-time feedback. The paper evaluates lossless data compression algorithms like Run Length Encoding (RLE), Lempel Zev Welch (LZW) and Huffman on data from wearable devices and compares them in terms of Compression Ratio, Compression Factor, Savings Percentage and Compression Time. It also evaluates a data deduplication technique used for Low Bandwidth File Systems (LBFS) named Two Thresholds Two Divisors (TTTD) algorithm to determine if it could be used for BSN data. By changing the parameters and running the algorithm multiple times on the data, it arrives at a set of values that give \u3e50 compression ratio on BSN data. This is the first value of the paper. Based on these performance evaluation results of TTTD and various classical compression algorithms, it proposes a technique to combine multiple algorithms in sequence. Upon comparison of the performance, it has been found that the new algorithm, TTTD-H, which does TTTD and Huffman in sequence, improves the Savings Percentage by 23 percent over TTTD, and 31 percent over Huffman when executed independently. Compression Factor improved by 142 percent over TTTD, 52 percent over LZW, 178 percent over Huffman for a file of 3.5 MB. These significant results are the second important value of the project