Development of a cost-effective and flexible vibration DAQ system for long-term continuous structural health monitoring

In the structural health monitoring (SHM) field, long-term continuous vibration-based monitoring is becoming increasingly popular as this could keep track of the health status of structures during their service lives. However, implementing such a system is not always feasible due to on-going conflicts between budget constraints and the need of sophisticated systems to monitor real-world structures under their demanding in-service conditions. To address this problem, this paper presents a comprehensive development of a cost-effective and flexible vibration DAQ system for long-term continuous SHM of a newly constructed institutional complex with a special focus on the main building. First, selections of sensor type and sensor positions are scrutinized to overcome adversities such as low-frequency and low-level vibration measurements. In order to economically tackle the sparse measurement problem, a cost-optimized Ethernet-based peripheral DAQ model is first adopted to form the system skeleton. A combination of a high-resolution timing coordination method based on the TCP/IP command communication medium and a periodic system resynchronization strategy is then proposed to synchronize data from multiple distributed DAQ units. The results of both experimental evaluations and experimental-numerical verifications show that the proposed DAQ system in general and the data synchronization solution in particular work well and they can provide a promising cost-effective and flexible alternative for use in real-world SHM projects. Finally, the paper demonstrates simple but effective ways to make use of the developed monitoring system for long-term continuous structural health evaluation as well as to use the instrumented building herein as a multi-purpose benchmark structure for studying not only practical SHM problems but also synchronization related issues

Vibration characteristics and damage detection in a suspension bridge

Suspension bridges are flexible and vibration sensitive structures that exhibit complex and multi-modal vibration. Due to this, the usual vibration based methods could face a challenge when used for damage detection in these structures. This paper develops and applies a mode shape component specific damage index (DI) to detect and locate damage in a suspension bridge with pre-tensioned cables. This is important as suspension bridges are large structures and damage in them during their long service lives could easily go un-noticed. The capability of the proposed vibration based DI is demonstrated through its application to detect and locate single and multiple damages with varied locations and severity in the cables of the suspension bridge. The outcome of this research will enhance the safety and performance of these bridges which play an important role in the transport network

Effects of wireless sensor network uncertainties on output-only modal-based damage identification

The use of Wireless Sensor Networks (WSNs) for Structural Health Monitoring (SHM) has become a promising approach due to many advantages such as low cost, fast and flexible deployment. However, inherent technical issues such as data synchronization error and data loss have prevented these distinct systems from being extensively used. Recently, several SHM-oriented WSNs have been proposed and believed to be able to overcome a large number of technical uncertainties. Nevertheless, there is limited research examining effects of uncertainties of generic WSN platform and verifying the capability of SHM-oriented WSNs, particularly on demanding SHM applications like modal analysis and damage identification of real civil structures. This article first reviews the major technical uncertainties of both generic and SHM-oriented WSN platforms and efforts of SHM research community to cope with them. Then, effects of the most inherent WSN uncertainty on the first level of a common Output-only Modal-based Damage Identification (OMDI) approach are intensively investigated. Experimental accelerations collected by a wired sensory system on a benchmark civil structure are initially used as clean data before being contaminated with different levels of data pollutants to simulate practical uncertainties in both WSN platforms. Statistical analyses are comprehensively employed in order to uncover the distribution pattern of the uncertainty influence on the OMDI approach. The result of this research shows that uncertainties of generic WSNs can cause serious impact for level 1 OMDI methods utilizing mode shapes. It also proves that SHM-WSN can substantially lessen the impact and obtain truly structural information without having used costly computation solutions

SHM through flexible vibration sensing technologies and robust safety evaluation paradigm

This research has successfully developed a novel synthetic structural health monitoring system model that is cost-effective and flexible in sensing and data acquisition; and robust in the structural safety evaluation aspect for the purpose of long-term and frequent monitoring of large-scale civil infrastructure during their service lives. Not only did it establish a real-world structural monitoring test-bed right at the heart of QUT Gardens Point Campus but it can also facilitate reliable and prompt protection for any built infrastructure system as well as the user community involved

Effects of wireless sensor network uncertainties on output-only modal analysis employing merged data of multiple tests

The use of Wireless Sensor Networks (WSNs) for vibration-based Structural Health Monitoring (SHM) has become a promising approach due to many advantages such as low cost, fast and flexible deployment. However, inherent technical issues such as data asynchronicity and data loss have prevented these distinct systems from being extensively used. Recently, several SHM-oriented WSNs have been proposed and believed to be able to overcome a large number of technical uncertainties. Nevertheless, there is limited research verifying the applicability of those WSNs with respect to demanding SHM applications like modal analysis and damage identification. Based on a brief review, this paper first reveals that Data Synchronization Error (DSE) is the most inherent factor amongst uncertainties of SHM-oriented WSNs. Effects of this factor are then investigated on outcomes and performance of the most robust Output-only Modal Analysis (OMA) techniques when merging data from multiple sensor setups. The two OMA families selected for this investigation are Frequency Domain Decomposition (FDD) and data-driven Stochastic Subspace Identification (SSI-data) due to the fact that they both have been widely applied in the past decade. Accelerations collected by a wired sensory system on a large-scale laboratory bridge model are initially used as benchmark data after being added with a certain level of noise to account for the higher presence of this factor in SHM-oriented WSNs. From this source, a large number of simulations have been made to generate multiple DSE-corrupted datasets to facilitate statistical analyses. The results of this study show the robustness of FDD and the precautions needed for SSI-data family when dealing with DSE at a relaxed level. Finally, the combination of preferred OMA techniques and the use of the channel projection for the time-domain OMA technique to cope with DSE are recommended

Controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis squared distance

The use of Mahalanobis squared distance–based novelty detection in statistical damage identification has become increasingly popular in recent years. The merit of the Mahalanobis squared distance–based method is that it is simple and requires low computational effort to enable the use of a higher dimensional damage-sensitive feature, which is generally more sensitive to structural changes. Mahalanobis squared distance–based damage identification is also believed to be one of the most suitable methods for modern sensing systems such as wireless sensors. Although possessing such advantages, this method is rather strict with the input requirement as it assumes the training data to be multivariate normal, which is not always available particularly at an early monitoring stage. As a consequence, it may result in an ill-conditioned training model with erroneous novelty detection and damage identification outcomes. To date, there appears to be no study on how to systematically cope with such practical issues especially in the context of a statistical damage identification problem. To address this need, this article proposes a controlled data generation scheme, which is based upon the Monte Carlo simulation methodology with the addition of several controlling and evaluation tools to assess the condition of output data. By evaluating the convergence of the data condition indices, the proposed scheme is able to determine the optimal setups for the data generation process and subsequently avoid unnecessarily excessive data. The efficacy of this scheme is demonstrated via applications to a benchmark structure data in the field

Effects of wireless sensor network uncertainties on output-only modal analysis employing merged data of multiple tests

The use of Wireless Sensor Networks (WSNs) for Structural Health Monitoring (SHM) has become a promising\ud approach due to many advantages such as low cost, fast and flexible deployment. However, inherent technical\ud issues such as data synchronization error and data loss have prevented these distinct systems from being\ud extensively used. Recently, several SHM-oriented WSNs have been proposed and believed to be able to\ud overcome a large number of technical uncertainties. Nevertheless, there is limited research verifying the\ud applicability of those WSNs with respect to demanding SHM applications like modal analysis and damage\ud identification. This paper first presents a brief review of the most inherent uncertainties of the SHM-oriented\ud WSN platforms and then investigates their effects on outcomes and performance of the most robust Output-only\ud Modal Analysis (OMA) techniques when employing merged data from multiple tests. The two OMA families\ud selected for this investigation are Frequency Domain Decomposition (FDD) and Data-driven Stochastic\ud Subspace Identification (SSI-data) due to the fact that they both have been widely applied in the past decade.\ud Experimental accelerations collected by a wired sensory system on a large-scale laboratory bridge model are\ud initially used as clean data before being contaminated by different data pollutants in sequential manner to\ud simulate practical SHM-oriented WSN uncertainties. The results of this study show the robustness of FDD and\ud the precautions needed for SSI-data family when dealing with SHM-WSN uncertainties. Finally, the use of the\ud measurement channel projection for the time-domain OMA techniques and the preferred combination of the\ud OMA techniques to cope with the SHM-WSN uncertainties is recommended

Report on Data Collection of Trawl Fisheries By catch in Kien Giang , Vietnam 2014

Increasing human populations and exploitation pressures, growing threats from pollution and major ecosystem change are particular concerns to marine fisheries in Vietnam in general and in KienGiang in particular. Also – as more widely in the global context – the nontargeted capture of fish and non‐fish species (e.g. turtles, corals and other seabed fauna), commonly called bycatch and discards, and are also of concern. This part of the catch tends to be poorly monitored and unmanaged but could have an important impact on fishery resources, habitats and ecosystems. In some fisheries and regions, there is an increasing trend towards the retention of the bycatch consisting of juveniles and small‐sized fish for use as food for human consumption or for utilization as aquaculture feed. This is therefore a complex issue, requiring resource and biodiversity aspects to be tackled alongside human needs and involving a mix of policy, technical and community support measures. The project “Strategies for trawl fisheries bycatch management” (REBYC‐II CTI) was conceived based on the successes of the 2002‐2008 FAO/UNEP/GEF global project “Reduction of Environmental Impact from Tropical Shrimp Trawling through the Introduction of Bycatch Reduction Technologies and Change of Management”. The REBYC‐II CTI project focuses on multispecies bottom trawling, where by catch issues are amongst the most serious, with potentially significant effects on ecosystems and livelihoods. The Project is addressing these challenges by promoting sustainable fishing practices and improved management of trawl fishing. The REBYC‐II CTI project was developed under the leadership of FAO (the Project implementing agency) in close collaboration with its partners: Southeast Asian Fisheries Development Center (SEAFDEC) and the governments of the participating countries; Indonesia, Papua New Guinea, Philippines, Thailand and Viet Nam. KienGiang province was selected as a pilot site to implement the project in Vietnam. Marine capture fisheries of Vietnam have developed strongly and have significantly contributed to the socio‐economic development, food security and maritime sovereignty of Vietnam. Over the past few years, in KienGiang Province, marine capture fisheries have considerably increased and are contributing significantly to the economic development of the Province. However, these increases have revealed many problems such as unsustainable development, insufficient fisheries management, uncontrolled number of fishing boats and fragmented small‐scale fishing operations. Illegal fishing has been occurring and is threating marine resources sustainability, especially in the coastal areas. To overcome these shortcomings there is a need to manage capture fisheries better andmaximize the efficiency of using these resources whilst conserving the marine ecosystem. For this the development the implementation of fishing capacity management mechanisms will be very important. It is also recognized that the fishing capacity management tools such as various fishing gear restrictions andclosedfishingseasons/areas will likely be the most relevant so that marine fisheries can continue make significant contributions to socio‐economic development, food safety and security, coastal community livelihoods, and generation of foreign exchange through the export of fish and other fisheries products. The REBYC‐II CTI Project in Vietnam works closely with DECAFIREP and Provincial staff in KienGiangProvince, which is amongst the provinces with the highest number of trawlers in Vietnam. According to a recent report of local authority of KienGiang province, there are total of 12,435 fishing vessels registered in the province. Of those, the number of trawlers is 3,265 accounting for about 26% of the total number. However, the total catch from the trawl fisheries in KienGiang (and Vietnam overall)is not known with any degree of certainty. There is a lack of operational‐level data on catches for all regions and thereforethe trends in catch rates are difficult to monitor. Although a national legal framework has been established to implement a logbook program requiring the cooperation of fishing communities, implementation has been weak due to the lack of compliance and enforcement. The difficulty of catch monitoring is also exacerbated by the complex multi‐species and multi‐gear nature of the trawl fishery in KienGiang province. This report was prepared underthe REBYC‐II CTI activity ‘Data collection of trawl fisheries by catchinKienGiang waters of Vietnam’. The expected outputs are: (1) the collection of data relating tototal landings by selected trawl fishery fleets; (2) total bycatch by selected trawl fishery fleets (trash* fish, juveniles and sharks, rays and coral fragments); and (3)monthly species and size composition and volumes of catch and bycatch by selected trawl fishery fleets (by season, area, vessel type, gear type

Тот, кто жизни солдатской не знает

Тот, кто жизни солдатской не знает, / Сапоги никогда не таскал, / Тот пусть этот дневник не читает, / Я здесь все для солдата писал