Assessment of “change in flexibility method” in structural health monitoring systems

The modern structural diagnosis process is rely on vibration characteristics to assess safer serviceability level of the structure. This paper examines the potential of change in flexibility method to use in damage detection process and two main practical constraints associated with it. The first constraint addressed in this paper is reduction in number of data acquisition points due to limited number of sensors. Results conclude that accuracy of the change in flexibility method is influenced by the number of data acquisition points/sensor locations in real structures. Secondly, the effect of higher modes on damage detection process has been studied. This addresses the difficulty of extracting higher order modal data with available sensors. Four damage indices have been presented to identify their potential of damage detection with respect to different locations and severity of damage. A simply supported beam with two degrees of freedom at each node is considered only for a single damage cases throughout the paper.

Assessing the representativeness of a groundwater quality monitoring network

The monitoring sites comprising a state of the environment (SOE) network must be carefully selected to ensure that they will be representative of the broader resource. Hierarchical cluster analysis (HCA) is a data-driven technique that can potentially be employed to assess the representativeness of a SOE monitoring network. The objective of this paper is to explore the use of HCA as an approach for assessing the representativeness of the New Zealand National Groundwater Monitoring Programme (NGMP), which is comprised of 110 monitoring sites across the country.

Analysis of acoustic emission data for accurate damage assessment for structural health monitoring applications

Structural health monitoring (SHM) refers to the procedure used to assess the condition of structures so that their performance can be monitored and any damage can be detected early. Early detection of damage and appropriate retrofitting will aid in preventing failure of the structure and save money spent on maintenance or replacement and ensure the structure operates safely and efficiently during its whole intended life. Though visual inspection and other techniques such as vibration based ones are available for SHM of structures such as bridges, the use of acoustic emission (AE) technique is an attractive option and is increasing in use. AE waves are high frequency stress waves generated by rapid release of energy from localised sources within a material, such as crack initiation and growth. AE technique involves recording these waves by means of sensors attached on the surface and then analysing the signals to extract information about the nature of the source. High sensitivity to crack growth, ability to locate source, passive nature (no need to supply energy from outside, but energy from damage source itself is utilised) and possibility to perform real time monitoring (detecting crack as it occurs or grows) are some of the attractive features of AE technique. In spite of these advantages, challenges still exist in using AE technique for monitoring applications, especially in the area of analysis of recorded AE data, as large volumes of data are usually generated during monitoring. The need for effective data analysis can be linked with three main aims of monitoring: (a) accurately locating the source of damage; (b) identifying and discriminating signals from different sources of acoustic emission and (c) quantifying the level of damage of AE source for severity assessment. In AE technique, the location of the emission source is usually calculated using the times of arrival and velocities of the AE signals recorded by a number of sensors. But complications arise as AE waves can travel in a structure in a number of different modes that have different velocities and frequencies. Hence, to accurately locate a source it is necessary to identify the modes recorded by the sensors. This study has proposed and tested the use of time-frequency analysis tools such as short time Fourier transform to identify the modes and the use of the velocities of these modes to achieve very accurate results. Further, this study has explored the possibility of reducing the number of sensors needed for data capture by using the velocities of modes captured by a single sensor for source localization. A major problem in practical use of AE technique is the presence of sources of AE other than crack related, such as rubbing and impacts between different components of a structure. These spurious AE signals often mask the signals from the crack activity; hence discrimination of signals to identify the sources is very important. This work developed a model that uses different signal processing tools such as cross-correlation, magnitude squared coherence and energy distribution in different frequency bands as well as modal analysis (comparing amplitudes of identified modes) for accurately differentiating signals from different simulated AE sources. Quantification tools to assess the severity of the damage sources are highly desirable in practical applications. Though different damage quantification methods have been proposed in AE technique, not all have achieved universal approval or have been approved as suitable for all situations. The b-value analysis, which involves the study of distribution of amplitudes of AE signals, and its modified form (known as improved b-value analysis), was investigated for suitability for damage quantification purposes in ductile materials such as steel. This was found to give encouraging results for analysis of data from laboratory, thereby extending the possibility of its use for real life structures. By addressing these primary issues, it is believed that this thesis has helped improve the effectiveness of AE technique for structural health monitoring of civil infrastructures such as bridges.

Domestic energy monitoring in Victorian households with PV solar installations : feasibility report for GV Community Energy

Australia requires decisive action on climate change and issues of sustainability. The Urban Informatics Research Lab has been funded by the Queensland State Government to conduct a three year study (2009 – 2011) exploring ways to support Queensland residents in making more sustainable consumer and lifestyle choices. We conduct user-centred design research that inform the development of real-time, mobile, locational, networked information interfaces, feedback mechanisms and persuasive and motivational approaches that in turn assist in-situ decision making and environmental awareness in everyday settings. The study aims to deliver usable and useful prototypes offering individual and collective visualisations of ecological impact and opportunities for engagement and collaboration in order to foster a participatory and sustainable culture of life in Australia. Raising people’s awareness with environmental data and educational information does not necessarily trigger sufficient motivation to change their habits towards a more environmentally friendly and sustainable lifestyle. Our research seeks to develop a better understanding how to go beyond just informing and into motivating and encouraging action and change. Drawing on participatory culture, ubiquitous computing, and real-time information, the study delivers research that leads to viable new design approaches and information interfaces which will strengthen Australia’s position to meet the targets of the Clean Energy Future strategy, and contribute to the sustainability of a low-carbon future in Australia. As part of this program of research, the Urban Informatics Research Lab has been invited to partner with GV Community Energy Pty Ltd on a project funded by the Victorian Government Sustainability Fund. This feasibility report specifically looks at the challenges and opportunities of energy monitoring in households in Victoria that include a PV solar installation. The report is structured into two parts: In Part 1, we first review a range of energy monitoring solutions, both stand-alone and internet-enabled. This section primarily focusses on the technical capacilities. However, in order to understand this information and make an informed decision, it is crucial to understand the basic principles and limitations of energy monitoring as well as the opportunities and challenges of a networked approach towards energy monitoring which are discussed in Section 2.

EPG monitoring of the probing behaviour of the common brown leafhopper Orosius orientalis on artificial diet and selected host plants

The common brown leafhopper Orosius orientalis (Hemiptera: Cicadellidae) is a polyphagous vector of a range of economically important pathogens, including phytoplasmas and viruses, which infect a diverse range of crops. Studies on the plant penetration behaviour by O. orientalis were conducted using the electrical penetration graph (EPG) technique to assist in the characterisation of pathogen acquisition and transmission. EPG waveforms representing different probing activities were acquired from adult O. orientalis probing in planta, using two host species, tobacco Nicotiana tabacum and bean Phaseolus vulgaris, and in vitro using a simple sucrose-based artificial diet. Five waveforms (O1–O5) were evident when O. orientalis fed on bean, whereas only four waveforms (O1–O4) and three waveforms (O1–O3) were observed when the leafhopper fed on tobacco and on the artificial diet, respectively. Both the mean duration of each waveform and waveform type differed markedly depending on the food substrate. Waveform O4 was not observed on the artificial diet and occurred relatively rarely on tobacco plants when compared with bean plants. Waveform O5 was only observed with leafhoppers probing on beans. The attributes of the waveforms and comparative analyses with previously published Hemipteran data are presented and discussed, but further characterisation studies will be needed to confirm our suggestions.

Estimating the effects of environmental exposures using a weighted mean of monitoring stations

The health effects of environmental hazards are often examined using time series of the association between a daily response variable (e.g., death) and a daily level of exposure (e.g., temperature). Exposures are usually the average from a network of stations. This gives each station equal importance, and negates the opportunity for some stations to be better measures of exposure. We used a Bayesian hierarchical model that weighted stations using random variables between zero and one. We compared the weighted estimates to the standard model using data on health outcomes (deaths and hospital admissions) and exposures (air pollution and temperature) in Brisbane, Australia. The improvements in model fit were relatively small, and the estimated health effects of pollution were similar using either the standard or weighted estimates. Spatial weighted exposures would be probably more worthwhile when there is either greater spatial detail in the health outcome, or a greater spatial variation in exposure.

Monitoring charged particle and ion emissions from a laser printer

While the emission rate of ultrafine particles has been measured and quantified, there is very little information on the emission rates of ions and charged particles from laser printers. This paper describes a methodology that can be adopted for measuring the surface charge density on printed paper and the ion and charged particle emissions during operation of a high-emitting laser printer and shows how emission rates of ultrafine particles, ions and charged particles may be quantified using a controlled experiment within a closed chamber.

Monitoring charged particles in indoor air using a neutral cluster and air ion spectrometer

While there are sources of ions both outdoors and indoors, ventilation systems can introduce as well as remove ions from the air. As a result, indoor ion concentrations are not directly related to air exchange rates in buildings. In this study, we attempt to relate these quantities with the view of understanding how charged particles may be introduced into indoor spaces.

A practical signal processing approach for condition monitoring of low speed machinery using Peak-Hold-Down-Sample algorithm

A simple and effective down-sample algorithm, Peak-Hold-Down-Sample (PHDS) algorithm is developed in this paper to enable a rapid and efficient data transfer in remote condition monitoring applications. The algorithm is particularly useful for high frequency Condition Monitoring (CM) techniques, and for low speed machine applications since the combination of the high sampling frequency and low rotating speed will generally lead to large unwieldy data size. The effectiveness of the algorithm was evaluated and tested on four sets of data in the study. One set of the data was extracted from the condition monitoring signal of a practical industry application. Another set of data was acquired from a low speed machine test rig in the laboratory. The other two sets of data were computer simulated bearing defect signals having either a single or multiple bearing defects. The results disclose that the PHDS algorithm can substantially reduce the size of data while preserving the critical bearing defect information for all the data sets used in this work even when a large down-sample ratio was used (i.e., 500 times down-sampled). In contrast, the down-sample process using existing normal down-sample technique in signal processing eliminates the useful and critical information such as bearing defect frequencies in a signal when the same down-sample ratio was employed. Noise and artificial frequency components were also induced by the normal down-sample technique, thus limits its usefulness for machine condition monitoring applications.

Utilising Reliability and Condition Monitoring Data for Asset Health Prognosis

The ability to forecast machinery health is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models which attempt to forecast machinery health based on condition data such as vibration measurements. This paper demonstrates how the population characteristics and condition monitoring data (both complete and suspended) of historical items can be integrated for training an intelligent agent to predict asset health multiple steps ahead. The model consists of a feed-forward neural network whose training targets are asset survival probabilities estimated using a variation of the Kaplan–Meier estimator and a degradation-based failure probability density function estimator. The trained network is capable of estimating the future survival probabilities when a series of asset condition readings are inputted. The output survival probabilities collectively form an estimated survival curve. Pump data from a pulp and paper mill were used for model validation and comparison. The results indicate that the proposed model can predict more accurately as well as further ahead than similar models which neglect population characteristics and suspended data. This work presents a compelling concept for longer-range fault prognosis utilising available information more fully and accurately.

Underground power cable environment on-line monitoring and analysis

Knowledge of cable parameters has been well established but a better knowledge of the environment in which the cables are buried lags behind. Research in Queensland University of Technology has been aimed at obtaining and analysing actual daily field values of thermal resistivity and diffusivity of the soil around power cables. On-line monitoring systems have been developed and installed with a data logger system and buried spheres that use an improved technique to measure thermal resistivity and diffusivity over a short period. Results based on long term continuous field data are given. A probabilistic approach is developed to establish the correlation between the measured field thermal resistivity values and rainfall data from weather bureau records. This data from field studies can reduce the risk in cable rating decisions and provide a basis for reliable prediction of “hot spot” of an existing cable circuit