Case studies on vibration based damage identification : multi-criteria approach

Vibration based damage identification methods examine the changes in primary modal parameters or quantities derived from modal parameters. As one method may have advantages over the other under some circumstances, a multi-criteria approach is proposed. Case studies are conducted separately on beam, plate and plate-on-beam structures. Using the numerically simulated modal data obtained through finite element analysis software, algorithms based on flexibility and strain energy changes before and after damage are obtained and used as the indices for the assessment of the state of structural health. Results show that the proposed multi-criteria method is effective in damage identification in these structures.

Damage assessment in multiple-girder composite bridge using vibration characteristics

This paper uses dynamic computer simulation techniques to apply a procedure using vibration-based methods for damage assessment in multiple-girder composite bridge. In addition to changes in natural frequencies, this multi-criteria procedure incorporates two methods, namely the modal flexibility and the modal strain energy method. Using the numerically simulated modal data obtained through finite element analysis software, algorithms based on modal flexibility and modal strain energy change before and after damage are obtained and used as the indices for the assessment of structural health state. The feasibility and capability of the approach is demonstrated through numerical studies of proposed structure with six damage scenarios. It is concluded that the modal strain energy method is competent for application on multiple-girder composite bridge, as evidenced through the example treated in this paper.

Maintaining ERP packaged software : a revelatory case study

For many organizations, maintaining and upgrading enterprise resource planning (ERP) systems (large packaged application software) is often far more costly than the initial implementation. Systematic planning and knowledge of the fundamental maintenance processes and maintenance-related management data are required in order to effectively and efficiently administer maintenance activities. This paper reports a revelatory case study of Government Services Provider (GSP), a high-performing ERP service provider to government agencies in Australia. GSP ERP maintenance-process and maintenance-data standards are compared with the IEEE/EIA 12207 software engineering standard for custom software, also drawing upon published research, to identify how practices in the ERP context diverge from the IEEE standard. While the results show that many best practices reflected in the IEEE standard have broad relevance to software generally, divergent practices in the ERP context necessitate a shift in management focus, additional responsibilities, and different maintenance decision criteria. Study findings may provide useful guidance to practitioners, as well as input to the IEEE and other related standards.

Incorporating interdependence in risk likelihood analysis to enhance diagnostics in condition monitoring

This research is aimed at addressing problems in the field of asset management relating to risk analysis and decision making based on data from a Supervisory Control and Data Acquisition (SCADA) system. It is apparent that determining risk likelihood in risk analysis is difficult, especially when historical information is unreliable. This relates to a problem in SCADA data analysis because of nested data. A further problem is in providing beneficial information from a SCADA system to a managerial level information system (e.g. Enterprise Resource Planning/ERP). A Hierarchical Model is developed to address the problems. The model is composed of three different Analyses: Hierarchical Analysis, Failure Mode and Effect Analysis, and Interdependence Analysis. The significant contributions from the model include: (a) a new risk analysis model, namely an Interdependence Risk Analysis Model which does not rely on the existence of historical information because it utilises Interdependence Relationships to determine the risk likelihood, (b) improvement of the SCADA data analysis problem by addressing the nested data problem through the Hierarchical Analysis, and (c) presentation of a framework to provide beneficial information from SCADA systems to ERP systems. The case study of a Water Treatment Plant is utilised for model validation.

Infrastructure Sustainability : Damage Assessment in Multiple-Girder Composite Bridges Using Vibration Characteristics

Assessing the structural health state of urban infrastructure is crucial in terms of infrastructure sustainability. This chapter uses dynamic computer simulation techniques to apply a procedure using vibration-based methods for damage assessment in multiple-girder composite bridges. In addition to changes in natural frequencies, this multi-criteria procedure incorporates two methods, namely, the modal flexibility and the modal strain energy method. Using the numerically simulated modal data obtained through finite element analysis software, algorithms based on modal flexibility and modal strain energy change, before and after damage, are obtained and used as the indices for the assessment of structural health state. The feasibility and capability of the approach is demonstrated through numerical studies of a proposed structure with six damage scenarios. It is concluded that the modal strain energy method is capable of application to multiple-girder composite bridges, as evidenced through the example treated in this chapter.

Repeatability of measuring corneal subbasal nerve fiber length in individuals with Type 2 Diabetes

Purpose: To analyze the repeatability of measuring nerve fiber length (NFL) from images of the human corneal subbasal nerve plexus using semiautomated software. Methods: Images were captured from the corneas of 50 subjects with type 2 diabetes mellitus who showed varying severity of neuropathy, using the Heidelberg Retina Tomograph 3 with Rostock Corneal Module. Semiautomated nerve analysis software was independently used by two observers to determine NFL from images of the subbasal nerve plexus. This procedure was undertaken on two occasions, 3 days apart. Results: The intraclass correlation coefficient values were 0.95 (95% confidence intervals: 0.92–0.97) for individual subjects and 0.95 (95% confidence intervals: 0.74–1.00) for observer. Bland-Altman plots of the NFL values indicated a reduced spread of data with lower NFL values. The overall spread of data was less for (a) the observer who was more experienced at analyzing nerve fiber images and (b) the second measurement occasion. Conclusions: Semiautomated measurement of NFL in the subbasal nerve fiber layer is highly repeatable. Repeatability can be enhanced by using more experienced observers. It may be possible to markedly improve repeatability when measuring this anatomic structure using fully automated image analysis software.

An analysis of the Brisbane residential property market and the global financial crisis

The period from 2007 to 2009 covered the residential property boom from early 2000, to the property recession following the Global Financial Crisis. Since late 2008, a number of residential property markets have suffered significant falls in house prices, buth this has not been consistent across all market sectors. This paper will analyze the housing market in Brisbane Australia to determine the impact, similarities and differences that the4 GFC had on range of residential sectors across a divesified property market. Data analysis will provide an overview of residential property prices, sales and listing volumes over the study period and will provide a comparison of median house price performance across the geographic and socio-economic areas of Brisbane.

Development of an online condition monitoring system for slow speed machinery

One of the main challenges of slow speed machinery condition monitoring is that the energy generated from an incipient defect is too weak to be detected by traditional vibration measurements due to its low impact energy. Acoustic emission (AE) measurement is an alternative for this as it has the ability to detect crack initiations or rubbing between moving surfaces. However, AE measurement requires high sampling frequency and consequently huge amount of data are obtained to be processed. It also requires expensive hardware to capture those data, storage and involves signal processing techniques to retrieve valuable information on the state of the machine. AE signal has been utilised for early detection of defects in bearings and gears. This paper presents an online condition monitoring (CM) system for slow speed machinery, which attempts to overcome those challenges. The system incorporates relevant signal processing techniques for slow speed CM which include noise removal techniques to enhance the signal-to-noise and peak-holding down sampling to reduce the burden of massive data handling. The analysis software works under Labview environment, which enables online remote control of data acquisition, real-time analysis, offline analysis and diagnostic trending. The system has been fully implemented on a site machine and contributing significantly to improve the maintenance efficiency and provide a safer and reliable operation.

A comparative analysis of business analysis (BA) and business process management (BPM) capabilities

Many initiatives to improve Business processes are emerging. The essential roles and contributions of Business Analyst (BA) and Business Process Management (BPM) professionals to such initiatives have been recognized in literature and practice. The roles and responsibilities of a BA or BPM practitioner typically require different skill-sets; however these differences are often vague. This vagueness creates much confusion in practice and academia. While both the BA and BPM communities have made attempts to describe their domains through capability defining empirical research and developments of Bodies of knowledge, there has not yet been any attempt to identify the commonality of skills required and points of uniqueness between the two professions. This study aims to address this gap and presents the findings of a detailed content mapping exercise (using NVivo as a qualitative data analysis tool) of the International Institution of Business Analysis (IIBA®) Guide to the Business Analysis Body of Knowledge (BABOK® Guide) against core BPM competency and capability frameworks.

Exploration and optimization of user experience in viewing videos on a mobile phone

Compared with viewing videos on PCs or TVs, mobile users have different experiences in viewing videos on a mobile phone due to different device features such as screen size and distinct usage contexts. To understand how mobile user’s viewing experience is impacted, we conducted a field user study with 42 participants in two typical usage contexts using a custom-designed iPhone application. With user’s acceptance of mobile video quality as the index, the study addresses four influence aspects of user experiences, including context, content type, encoding parameters and user profiles. Accompanying the quantitative method (acceptance assessment), we used a qualitative interview method to obtain a deeper understanding of a user’s assessment criteria and to support the quantitative results from a user’s perspective. Based on the results from data analysis, we advocate two user-driven strategies to adaptively provide an acceptable quality and to predict a good user experience, respectively. There are two main contributions from this paper. Firstly, the field user study allows a consideration of more influencing factors into the research on user experience of mobile video. And these influences are further demonstrated by user’s opinions. Secondly, the proposed strategies — user-driven acceptance threshold adaptation and user experience prediction — will be valuable in mobile video delivery for optimizing user experience.

A pilot study assessing the feasibility of a home-based progressive resistance exercise program and trend toward healing rates for patients with venous leg ulcers

Venous leg ulceration is a serious condition affecting 1 – 3% of the population. Decline in the function of the calf muscle pump is correlated with venous ulceration. Many previous studies have reported an improvement in the function of the calf muscle pump, endurance of the calf muscle and increased range of ankle motion after structured exercise programs. However, there is a paucity of published research that assesses if these improvements result in an improvement in the healing rates of venous ulcers. The primary purpose of this pilot study was to establish the feasibility of a homebased progressive resistance exercise program and examine if there was any clinical significance or trend toward healing. The secondary aims were to examine the benefit of a home-based progressive resistance exercise program on calf muscle pump function and physical parameters. The methodology used was a randomised controlled trial where eleven participants were randomised into an intervention (n = 6) or control group (n = 5). Participants who were randomised to receive a 12-week home-based progressive resistance exercise program were instructed through weekly face-to-face consultations during their wound clinic appointment by the author. Control group participants received standard wound care and compression therapy. Changes in ulcer parameters were measured fortnightly at the clinic (number healed at 12 weeks, percentage change in area and pressure ulcer score healing score). An air plethysmography test was performed at baseline and following the 12 weeks of training to determine changes in calf muscle pump function. Functional measures included maximum number of heel raises (endurance), maximal isometric plantar flexion (strength) and range of ankle motion (ROAM); these tests were conducted at baseline, week 6 and week 12. The sample for the study was drawn from the Princess Alexandra Hospital in Brisbane, Australia. Participants with venous leg ulceration who met the inclusion criteria were recruited. The participants were screened via duplex scanning and ankle brachial pressure index (ABPI) to ensure they did not have any arterial complications. Participants were excluded if there was evidence of cellulitis. Demographic data were obtained from each participant and details regarding medical history, quality of life and geriatric depression scores were collected at baseline. Both the intervention and control group were required to complete a weekly exercise diary to monitor activity levels between groups. To test for the effect of the intervention over time, a repeated measures analysis of variance was conducted on the major outcome variables. Group (intervention versus control) was the between subject factor and time (baseline, week 6, week 12) was the within subject or repeated measures factor. Due to the small sample size, further tests were conducted to check the assumptions of the statistical test to be used. The results showed that Mauchly.s Test, the Sphericity assumptions of repeated measures for ANOVA were met. Further tests of homogeneity of variance assumptions also confirmed that this assumption was met. Data analysis was conducted using the software package SPSS for Windows Release 17.0. The pilot study proved feasible with all of the intervention (n=6) participants continuing with the resistance program for the 12 week duration and no deleterious effects noted. Clinical significance was observed in the intervention group with a 32% greater change in ulcer size (p= 0.26) than the control group, and a 10% (p = 0.74) greater difference between the numbers healed compared to the control group. Statistical significance was observed for the ejection fraction (p = 0.05), residual volume fraction (p = 0.04) and ROAM (p = 0.01), which all improved significantly in the intervention group over time. These results are encouraging, nevertheless, further investigations seem warranted to examine the effect exercise has on the healing rates of venous leg ulcers, with a multistudy site, larger sample size and longer follow up period.

New variational Bayesian approaches for statistical data mining : with applications to profiling and differentiating habitual consumption behaviour of customers in the wireless telecommunication industry

This thesis investigates profiling and differentiating customers through the use of statistical data mining techniques. The business application of our work centres on examining individuals’ seldomly studied yet critical consumption behaviour over an extensive time period within the context of the wireless telecommunication industry; consumption behaviour (as oppose to purchasing behaviour) is behaviour that has been performed so frequently that it become habitual and involves minimal intentions or decision making. Key variables investigated are the activity initialised timestamp and cell tower location as well as the activity type and usage quantity (e.g., voice call with duration in seconds); and the research focuses are on customers’ spatial and temporal usage behaviour. The main methodological emphasis is on the development of clustering models based on Gaussian mixture models (GMMs) which are fitted with the use of the recently developed variational Bayesian (VB) method. VB is an efficient deterministic alternative to the popular but computationally demandingMarkov chainMonte Carlo (MCMC) methods. The standard VBGMMalgorithm is extended by allowing component splitting such that it is robust to initial parameter choices and can automatically and efficiently determine the number of components. The new algorithm we propose allows more effective modelling of individuals’ highly heterogeneous and spiky spatial usage behaviour, or more generally human mobility patterns; the term spiky describes data patterns with large areas of low probability mixed with small areas of high probability. Customers are then characterised and segmented based on the fitted GMM which corresponds to how each of them uses the products/services spatially in their daily lives; this is essentially their likely lifestyle and occupational traits. Other significant research contributions include fitting GMMs using VB to circular data i.e., the temporal usage behaviour, and developing clustering algorithms suitable for high dimensional data based on the use of VB-GMM.