Functional and advanced functional and causal relationship diagrams for remote condition monitoring

Analysing the condition of an asset is a big challenge as there can be many aspects which can contribute to the overall functional reliability of the asset that have to be considered. In this paper we propose a two-step functional and causal relationship diagram (FCRD) to address this problem. In the first step, the FCRD is designed to facilitate the analysis of the condition of an asset by evaluating the interdependence (functional and causal) relationships between different components of the asset with the help of a relationship diagram. This is followed by the advanced FCRD (AFCRD) which refines the information from the FCRD into a comprehensive and manageable format. This new two-step methodology for asset condition monitoring is tested and validated for the case of a water treatment plant. © IMechE 2012.

Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure 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 for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

Thin film deposition and characterization of pure and iron-doped electron-beam evaporated tungsten oxide for gas sensors

Pure Tungsten Oxide (WO3) and Iron-doped (10 at%) Tungsten Oxide (WO3:Fe) nanostructured thin films were prepared using a dual crucible Electron Beam Evaporation techniques. The films were deposited at room temperature in high vacuum condition on glass substrate and post-heat treated at 300 oC for 1 hour. From the study of X-ray diffraction and Raman the characteristics of the as-deposited WO3 and WO3:Fe films indicated non-crystalline nature. The surface roughness of all the films showed in the order of 2.5 nm as observed using Atomic Force Microscopy (AFM). X-Ray Photoelectron Spectroscopy (XPS) analysis revealed tungsten oxide films with stoichiometry close to WO3. The addition of Fe to WO3 produced a smaller particle size and lower porosity as observed using Transmission Electron Microscopy (TEM). A slight difference in optical band gap energies of 3.22 eV and 3.12 eV were found between the as-deposited WO3 and WO3:Fe films, respectively. However, the difference in the band gap energies of the annealed films were significantly higher having values of 3.12 eV and 2.61 eV for the WO3 and WO3:Fe films, respectively. The heat treated samples were investigated for gas sensing applications using noise spectroscopy and doping of Fe to WO3 reduced the sensitivity to certain gasses. Detailed study of the WO3 and WO3:Fe films gas sensing properties is the subject of another paper.

Selective effects of acute exercise and breakfast interventions on mood and motivation to eat

The effects of exercise and breakfast manipulations on mood and motivation to eat were assessed in 11 healthy females who were regular exercisers and habitual breakfast eaters. The study involved a two by two repeated-measures design, with exercise (or no exercise) and a high-energy breakfast (or low-energy breakfast) as the repeated measures. The exercise or no-exercise session (0800 h) was followed by consumption of the low- or high-energy breakfast (0900 h). An ad libitum lunch test meal was provided 4 hours after the beginning of the exercise session (1200 h). Mood and motivation to eat were continuously tracked from 0800 until 1700 h by an electronic appetite ratings system (EARS). In general, morning subjective mood states (e.g., contentment) were significantly lower in the low-energy breakfast condition, but exercise reversed this effect. Exercise also significantly decreased feelings of lethargy, independent of the breakfast condition. Desire-to-eat and fullness ratings were significantly increased in the low-energy breakfast and high-energy breakfast conditions, respectively. Impairments of mood disappeared in the afternoon after consumption of an ad libitum lunch. In these healthy young adults, the condition inducing the largest energy deficit (exercise and low-energy breakfast) was not associated with the lowest mental states.

An investigation of rail condition monitoring by Fibre Bragg Grating sensors

Condition monitoring on rails and train wheels is vitally important to the railway asset management and the rail-wheel interactions provide the crucial information of the health state of both rails and wheels. Continuous and remote monitoring is always a preference for operators. With a new generation of strain sensing devices in Fibre Bragg Grating (FBG) sensors, this study explores the possibility of continuous monitoring of the health state of the rails; and investigates the required signal processing techniques and their limitations.

Analysis of cardiac and epileptic signals using higher order spectra

The theory of nonlinear dyamic systems provides some new methods to handle complex systems. Chaos theory offers new concepts, algorithms and methods for processing, enhancing and analyzing the measured signals. In recent years, researchers are applying the concepts from this theory to bio-signal analysis. In this work, the complex dynamics of the bio-signals such as electrocardiogram (ECG) and electroencephalogram (EEG) are analyzed using the tools of nonlinear systems theory. In the modern industrialized countries every year several hundred thousands of people die due to sudden cardiac death. The Electrocardiogram (ECG) is an important biosignal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computerbased intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and four classes of arrhythmia. This thesis presents some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. Several features were extracted from the HOS and subjected an Analysis of Variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, seven features were extracted from the heart rate signals using HOS and fed to a support vector machine (SVM) for classification. The performance evaluation protocol in this thesis uses 330 subjects consisting of five different kinds of cardiac disease conditions. The classifier achieved a sensitivity of 90% and a specificity of 89%. This system is ready to run on larger data sets. In EEG analysis, the search for hidden information for identification of seizures has a long history. Epilepsy is a pathological condition characterized by spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic early detection of the seizure onsets would help the patients and observers to take appropriate precautions. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, these features are used to train both a Gaussian mixture model (GMM) classifier and a Support Vector Machine (SVM) classifier. Results show that the classifiers were able to achieve 93.11% and 92.67% classification accuracy, respectively, with selected HOS based features. About 2 hours of EEG recordings from 10 patients were used in this study. This thesis introduces unique bispectrum and bicoherence plots for various cardiac conditions and for normal, background and epileptic EEG signals. These plots reveal distinct patterns. The patterns are useful for visual interpretation by those without a deep understanding of spectral analysis such as medical practitioners. It includes original contributions in extracting features from HRV and EEG signals using HOS and entropy, in analyzing the statistical properties of such features on real data and in automated classification using these features with GMM and SVM classifiers.

Performance load testing and structural adequacy evaluation of road bridge decks

Many ageing road bridges, particularly timber bridges, require urgent improvement due to the demand imposed by the recent version of the Australian bridge loading code, AS 5100. As traffic volume plays a key role in the decision of budget allocations for bridge refurbishment/ replacement, many bridges in low volume traffic network remain in poor condition with axle load and/ or speed restrictions, thus disadvantaging many rural communities. This thesis examines an economical and environmentally sensible option of incorporating disused flat rail wagons (FRW) in the construction of bridges in low volume, high axle load road network. The constructability, economy and structural adequacy of the FRW road bridge is reported in the thesis with particular focus of a demonstration bridge commissioned in regional Queensland. The demonstration bridge comprises of a reinforced concrete slab (RCS) pavement resting on two FRWs with custom designed connection brackets at regular intervals along the span of the bridge. The FRW-RC bridge deck assembly is supported on elastomeric rubber pads resting on the abutment. As this type of bridge replacement technology is new and its structural design is not covered in the design standards, the in-service structural performance of the FRW bridge subjected to the high axle loadings prescribed in AS 5100 is examined through performance load testing. Both the static and the moving load tests are carried out using a fully laden commonly available three-axle tandem truck. The bridge deck is extensively strain gauged and displacement at several key locations is measured using linear variable displacement transducers (LVDTs). A high speed camera is used in the performance test and the digital image data are analysed using proprietary software to capture the locations of the wheel positions on the bridge span accurately. The wheel location is thus synchronised with the displacement and strain time series to infer the structural response of the FRW bridge. Field test data are used to calibrate a grillage model, developed for further analysis of the FRW bridge to various sets of high axle loads stipulated in the bridge design standard. Bridge behaviour predicted by the grillage model has exemplified that the live load stresses of the FRW bridge is significantly lower than the yield strength of steel and the deflections are well below the serviceability limit state set out in AS 5100. Based on the results reported in this thesis, it is concluded that the disused FRWs are competent to resist high axle loading prescribed in AS 5100 and are a viable alternative structural solution of bridge deck in the context of the low volume road networks.

Use of hierarchical cluster analysis to assess the representativeness of a baseline groundwater quality monitoring network: comparison of New Zealand’s national and regional groundwater monitoring programs

Baseline monitoring of groundwater quality aims to characterize the ambient condition of the resource and identify spatial or temporal trends. Sites comprising any baseline monitoring network must be selected to provide a representative perspective of groundwater quality across the aquifer(s) of interest. Hierarchical cluster analysis (HCA) has been used as a means of assessing the representativeness of a groundwater quality monitoring network, using example datasets from New Zealand. HCA allows New Zealand's national and regional monitoring networks to be compared in terms of the number of water-quality categories identified in each network, the hydrochemistry at the centroids of these water-quality categories, the proportions of monitoring sites assigned to each water-quality category, and the range of concentrations for each analyte within each water-quality category. Through the HCA approach, the National Groundwater Monitoring Programme (117 sites) is shown to provide a highly representative perspective of groundwater quality across New Zealand, relative to the amalgamated regional monitoring networks operated by 15 different regional authorities (680 sites have sufficient data for inclusion in HCA). This methodology can be applied to evaluate the representativeness of any subset of monitoring sites taken from a larger network.

Sustainable Road Management in Texas : network-level flexible pavement structural condition analysis using data mining techniques

The research team recognized the value of network-level Falling Weight Deflectometer (FWD) testing to evaluate the structural condition trends of flexible pavements. However, practical limitations due to the cost of testing, traffic control and safety concerns and the ability to test a large network may discourage some agencies from conducting the network-level FWD testing. For this reason, the surrogate measure of the Structural Condition Index (SCI) is suggested for use. The main purpose of the research presented in this paper is to investigate data mining strategies and to develop a prediction method of the structural condition trends for network-level applications which does not require FWD testing. The research team first evaluated the existing and historical pavement condition, distress, ride, traffic and other data attributes in the Texas Department of Transportation (TxDOT) Pavement Maintenance Information System (PMIS), applied data mining strategies to the data, discovered useful patterns and knowledge for SCI value prediction, and finally provided a reasonable measure of pavement structural condition which is correlated to the SCI. To evaluate the performance of the developed prediction approach, a case study was conducted using the SCI data calculated from the FWD data collected on flexible pavements over a 5-year period (2005 – 09) from 354 PMIS sections representing 37 pavement sections on the Texas highway system. The preliminary study results showed that the proposed approach can be used as a supportive pavement structural index in the event when FWD deflection data is not available and help pavement managers identify the timing and appropriate treatment level of preventive maintenance activities.

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.

Telephone support to rural and remote patients with heart failure : the Chronic Heart failure Assessment by Telephone (CHAT) study

Background Heart failure (HF) remains a condition with high morbidity and mortality. We tested a telephone support strategy to reduce major events in rural and remote Australians with HF, who have limited healthcare access. Telephone support comprised an interactive telecommunication software tool (TeleWatch) with follow-up by trained cardiac nurses. Methods Patients with a general practice (GP) diagnosis of HF were randomised to usual care (UC) or UC and telephone support intervention (UC+I) using a cluster design involving 143 GPs throughout Australia. Patients were followed for 12 months. The primary end-point was the Packer clinical composite score. Secondary end-points included hospitalisation for any cause, death or hospitalisation, as well as HF hospitalisation. Results Four hundred and five patients were randomised into CHAT. Patients were well matched at baseline for key demographic variables. The primary end-point of the Packer Score was not different between the two groups (P=0.98), although more patients improved with UC+I. There were fewer patients hospitalised for any cause (74 versus 114, adjusted HR 0.67 [95% CI 0.50-0.89], p=0.006) and who died or were hospitalised (89 versus 124, adjusted HR 0.70 [95% CI 0.53 – 0.92], p=0.011), in the UC+I vs UC group. HF hospitalisations were reduced with UC+I (23 versus 35, adjusted HR 0.81 [95% CI 0.44 – 1.38]), although this was not significant (p=0.43). There were 16 deaths in the UC group and 17 in the UC+I group (p=0.43). Conclusions Although no difference was observed in the primary end-point of CHAT (Packer composite score), UC+I significantly reduced the number of HF patients hospitalised amongst a rural and remote cohort. These data suggest that telephone support may be an efficacious approach to improve clinical outcomes in rural and remote HF patients.

In vitro assessment of surface congruency and integration of chondrocytes at adjacent edges of micro-fabricated clefts on cartilage

Osteochondral grafts are common treatment options for joint focal defects due to their excellent functionality. However, the difficulty is matching the topography of host and graft(s) surfaces flush to one another. Incongruence could lead to disintegration particularly when the gap reaches subchondoral region. The aim of this study is therefore to investigate cell response to gap geometry when forming cartilage-cartilage bridge at the interface. The question is what would be the characteristics of such a gap if the cells could bridge across to fuse the edges? To answer this, osteochondral plugs devoid of host cells were prepared through enzymatic decellularization and artificial clefts of different sizes were created on the cartilage surface using laser ablation. High density pellets of heterologous chondrocytes were seeded on the defects and cultured with chondrogenic differentiation media for 35 days. The results showed that the behavior of chondrocytes was a function of gap topography. Depending on the distance of the edges two types of responses were generated. Resident cells surrounding distant edges demonstrated superficial attachment to one side whereas clefts of 150 to 250 µm width experienced cell migration and anchorage across the interface. The infiltration of chondrocytes into the gaps provided extra space for their proliferation and laying matrix; as the result faster filling of the initial void space was observed. On the other hand, distant and fit edges created an incomplete healing response due to the limited ability of differentiated chondrocytes to migrate and incorporate within the interface. It seems that the initial condition of the defects and the curvature profile of the adjacent edges were the prime determinants of the quality of repair; however, further studies to reveal the underlying mechanisms of cells adapting to and modifying the new environment would be of particular interest.

Viewpoint : social and economic dimensions of involving savanna communities in carbon management systems

In this paper we discuss the social, economic and institutional aspects of the development of carbon management systems within Australia's tropical savannas. Land-use values in savanna landscapes are changing as a result of changing economic markets, greater recognition of native title, and growing social demands and expectations for tourism, recreation and conservation. In addition, there is increasing interest in developing markets and policy arrangements for greenhouse gas abatement, carbon sequestration and carbon trade in savannas. We argue that for carbon management to lead to national greenhouse outcomes, attention must be paid to social, economic and institutional issues in environmental planning and policy arrangements. From an economic perspective, the financial impact of carbon management on savanna enterprises will depend on appropriate and available policy mechanisms, unit price for carbon, landscape condition, existing management strategies and abatement measurements used. Local social and cultural features of communities and regions may enhance or constrain the implementation of carbon abatement strategies, depending on how they are perceived. In terms of institutional arrangements, policies and plans must support and enable carbon management. We identify three areas that require priority investigation and adjustment: regional planning arrangements, property rights, and rules for accounting at enterprise and regional scales. We conclude that the best potential for managing for carbon will be achieved while managing for range of other natural resource management outcomes, especially where managing for carbon delivers collateral benefits to enterprises.

High and low fear phenotypes show differences in phosphorylated (p44/42 ERK) MAPK-expressing neurons in the lateral amygdala

Post traumatic stress disorder (PTSD) is a serious medical condition effecting both military and civilian populations. While its etiology remains poorly understood it is characterized by high and prolonged levels of fear responding. One biological unknown is whether individuals expressing high or low conditioned fear memory encode the memory differently and if that difference underlies fear response. In this study we examined cellular mechanisms that underlie high and low conditioned fear behavior by using an advanced intercrossed mouse line (B6D2F1) selected for high and low Pavlovian fear response. A known requirement for consolidation of fear memory, phosphorylated mitogen activated protein kinase (p44/42 (ERK) MAPK (pMAPK)) in the lateral amygdala (LA) is a reliable marker of fear learning-related plasticity. In this study, we asked whether high and low conditioned fear behavior is associated with differential pMAPK expression in the LA and if so, is it due to an increase in neurons expressing pMAPK or increased pMAPK per neuron. To examine this, we quantified pMAPK-expressing neurons in the LA at baseline and following Pavlovian fear conditioning. Results indicate that high fear phenotype mice have more pMAPK-expressing neurons in the LA. This finding suggests that increased endogenous plasticity in the LA may be a component of higher conditioned fear responses and begins to explain at the cellular level how different fear responders encode fear memories. Understanding how high and low fear responders encode fear memory will help identify novel ways in which fear-related illness risk can be better predicted and treated.

Consensus statement on the investigation and management of non-radiographic axial spondyloarthritis (nr-axSpA)

Aim Non-radiographic axial spondyloarthritis (nr-axSpA) is axial inflammatory arthritis where plain radiographic damage is not evident. An unknown proportion of these patients will progress to ankylosing spondylitis (AS). The increasing recognition of nr-axSpA has been greatly assisted by the widespread use of magnetic resonance imaging. The aim of this article was to construct a set of consensus statements based on a literature review to guide investigation and promote best management of nr-axSpA. Methods A literature review using Medline was conducted covering the major investigation modalities and treatment options available. A group of rheumatologists and a radiologist with expertise in investigation and management of SpA reviewed the literature and formulated a set of consensus statements. The Grade system encompassing the level of evidence and strength of recommendation was used. The opinion of a patient with nr-axSpA and a nurse experienced in the care of SpA patients was also sought and included. Results The literature review found few studies specifically addressing nr-axSpA, or if these patients were included, their results were often not separately reported. Fourteen consensus statements covering investigation and management of nr-axSpA were formulated. The level of agreement was high and ranged from 8.1 to 9.8. Treatment recommendations vary little with established AS, but this is primarily due to the lack of available evidence on the specific treatment of nr-axSpA. Conclusion The consensus statements aim to improve the diagnosis and management of nr-axSpA. We aim to raise awareness of this condition by the public and doctors and promote appropriate investigation and management.