Machine prognostics based on health state probability estimation

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation and can also improve productivity and enhance system’s safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. Although a variety of prognostic methodologies have been reported recently, their application in industry is still relatively new and mostly focused on the prediction of specific component degradations. Furthermore, they required significant and sufficient number of fault indicators to accurately prognose the component faults. Hence, sufficient usage of health indicators in prognostics for the effective interpretation of machine degradation process is still required. Major challenges for accurate longterm prediction of remaining useful life (RUL) still remain to be addressed. Therefore, continuous development and improvement of a machine health management system and accurate long-term prediction of machine remnant life is required in real industry application. This thesis presents an integrated diagnostics and prognostics framework based on health state probability estimation for accurate and long-term prediction of machine remnant life. In the proposed model, prior empirical (historical) knowledge is embedded in the integrated diagnostics and prognostics system for classification of impending faults in machine system and accurate probability estimation of discrete degradation stages (health states). The methodology assumes that machine degradation consists of a series of degraded states (health states) which effectively represent the dynamic and stochastic process of machine failure. The estimation of discrete health state probability for the prediction of machine remnant life is performed using the ability of classification algorithms. To employ the appropriate classifier for health state probability estimation in the proposed model, comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault data of three different faults in a high pressure liquefied natural gas (HP-LNG) pump. As a result of this comparison study, SVMs were employed in heath state probability estimation for the prediction of machine failure in this research. The proposed prognostic methodology has been successfully tested and validated using a number of case studies from simulation tests to real industry applications. The results from two actual failure case studies using simulations and experiments indicate that accurate estimation of health states is achievable and the proposed method provides accurate long-term prediction of machine remnant life. In addition, the results of experimental tests show that the proposed model has the capability of providing early warning of abnormal machine operating conditions by identifying the transitional states of machine fault conditions. Finally, the proposed prognostic model is validated through two industrial case studies. The optimal number of health states which can minimise the model training error without significant decrease of prediction accuracy was also examined through several health states of bearing failure. The results were very encouraging and show that the proposed prognostic model based on health state probability estimation has the potential to be used as a generic and scalable asset health estimation tool in industrial machinery.

Evaluation of financial incentives as a quality improvement strategy in the public hospital context : clinician attitudes, design variables, and economic costs

In 2008, a three-year pilot ‘pay for performance’ (P4P) program, known as ‘Clinical Practice Improvement Payment’ (CPIP) was introduced into Queensland Health (QHealth). QHealth is a large public health sector provider of acute, community, and public health services in Queensland, Australia. The organisation has recently embarked on a significant reform agenda including a review of existing funding arrangements (Duckett et al., 2008). Partly in response to this reform agenda, a casemix funding model has been implemented to reconnect health care funding with outcomes. CPIP was conceptualised as a performance-based scheme that rewarded quality with financial incentives. This is the first time such a scheme has been implemented into the public health sector in Australia with a focus on rewarding quality, and it is unique in that it has a large state-wide focus and includes 15 Districts. CPIP initially targeted five acute and community clinical areas including Mental Health, Discharge Medication, Emergency Department, Chronic Obstructive Pulmonary Disease, and Stroke. The CPIP scheme was designed around key concepts including the identification of clinical indicators that met the set criteria of: high disease burden, a well defined single diagnostic group or intervention, significant variations in clinical outcomes and/or practices, a good evidence, and clinician control and support (Ward, Daniels, Walker & Duckett, 2007). This evaluative research targeted Phase One of implementation of the CPIP scheme from January 2008 to March 2009. A formative evaluation utilising a mixed methodology and complementarity analysis was undertaken. The research involved three research questions and aimed to determine the knowledge, understanding, and attitudes of clinicians; identify improvements to the design, administration, and monitoring of CPIP; and determine the financial and economic costs of the scheme. Three key studies were undertaken to ascertain responses to the key research questions. Firstly, a survey of clinicians was undertaken to examine levels of knowledge and understanding and their attitudes to the scheme. Secondly, the study sought to apply Statistical Process Control (SPC) to the process indicators to assess if this enhanced the scheme and a third study examined a simple economic cost analysis. The CPIP Survey of clinicians elicited 192 clinician respondents. Over 70% of these respondents were supportive of the continuation of the CPIP scheme. This finding was also supported by the results of a quantitative altitude survey that identified positive attitudes in 6 of the 7 domains-including impact, awareness and understanding and clinical relevance, all being scored positive across the combined respondent group. SPC as a trending tool may play an important role in the early identification of indicator weakness for the CPIP scheme. This evaluative research study supports a previously identified need in the literature for a phased introduction of Pay for Performance (P4P) type programs. It further highlights the value of undertaking a formal risk assessment of clinician, management, and systemic levels of literacy and competency with measurement and monitoring of quality prior to a phased implementation. This phasing can then be guided by a P4P Design Variable Matrix which provides a selection of program design options such as indicator target and payment mechanisms. It became evident that a clear process is required to standardise how clinical indicators evolve over time and direct movement towards more rigorous ‘pay for performance’ targets and the development of an optimal funding model. Use of this matrix will enable the scheme to mature and build the literacy and competency of clinicians and the organisation as implementation progresses. Furthermore, the research identified that CPIP created a spotlight on clinical indicators and incentive payments of over five million from a potential ten million was secured across the five clinical areas in the first 15 months of the scheme. This indicates that quality was rewarded in the new QHealth funding model, and despite issues being identified with the payment mechanism, funding was distributed. The economic model used identified a relative low cost of reporting (under $8,000) as opposed to funds secured of over $300,000 for mental health as an example. Movement to a full cost effectiveness study of CPIP is supported. Overall the introduction of the CPIP scheme into QHealth has been a positive and effective strategy for engaging clinicians in quality and has been the catalyst for the identification and monitoring of valuable clinical process indicators. This research has highlighted that clinicians are supportive of the scheme in general; however, there are some significant risks that include the functioning of the CPIP payment mechanism. Given clinician support for the use of a pay–for-performance methodology in QHealth, the CPIP scheme has the potential to be a powerful addition to a multi-faceted suite of quality improvement initiatives within QHealth.

Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems

This paper presents an approach to predict the operating conditions of machine based on classification and regression trees (CART) and adaptive neuro-fuzzy inference system (ANFIS) in association with direct prediction strategy for multi-step ahead prediction of time series techniques. In this study, the number of available observations and the number of predicted steps are initially determined by using false nearest neighbor method and auto mutual information technique, respectively. These values are subsequently utilized as inputs for prediction models to forecast the future values of the machines’ operating conditions. The performance of the proposed approach is then evaluated by using real trending data of low methane compressor. A comparative study of the predicted results obtained from CART and ANFIS models is also carried out to appraise the prediction capability of these models. The results show that the ANFIS prediction model can track the change in machine conditions and has the potential for using as a tool to machine fault prognosis.

Cardiac health diagnosis using higher order spectra and support vector machine

The Electrocardiogram (ECG) is an important bio-signal 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. The HRV signal can be used as a base signal to observe the heart's functioning. These signals are non-linear and non-stationary in nature. So, higher order spectral (HOS) analysis, which is more suitable for non-linear systems and is robust to noise, was used. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, we have extracted seven features from the heart rate signals using HOS and fed them to a support vector machine (SVM) for classification. Our performance evaluation protocol uses 330 subjects consisting of five different kinds of cardiac disease conditions. We demonstrate a sensitivity of 90% for the classifier with a specificity of 87.93%. Our system is ready to run on larger data sets.

Soil carbon sequestration rates and associated economic costs for farming systems of south-eastern Australia

Soil organic carbon (C) sequestration rates based on the Intergovernmental Panel for Climate Change (IPCC) methodology were combined with local economic data to simulate the economic potential for C sequestration in response to conservation tillage in the six agro-ecological zones within the Southern Region of the Australian grains industry. The net C sequestration rate over 20 years for the Southern Region (which includes discounting for associated greenhouse gases) is estimated to be 3.6 or 6.3 Mg C/ha after converting to either minimum or no-tillage practices, respectively, with no-till practices estimated to return 75% more carbon on average than minimum tillage. The highest net gains in C per ha are realised when converting from conventional to no-tillage practices in the high-activity clay soils of the High Rainfall and Wimmera agro-ecological zones. On the basis of total area available for change, the Slopes agro-ecological zone offers the highest net returns, potentially sequestering an additional 7.1 Mt C under no-tillage scenario over 20 years. The economic analysis was summarised as C supply curves for each of the 6 zones expressing the total additional C accumulated over 20 years for a price per t C sequestered ranging from zero to AU$200. For a price of $50/Mg C, a total of 427 000 Mg C would be sequestered over 20 years across the Southern Region, <5% of the simulated C sequestration potential of 9.1 Mt for the region. The Wimmera and Mid-North offer the largest gains in C under minimum tillage over 20 years of all zones for all C prices. For the no-tillage scenario, for a price of $50/Mg C, 1.74 Mt C would be sequestered over 20 years across the Southern Region, <10% of the simulated C sequestration potential of 18.6 Mt for the region over 20 years. The Slopes agro-ecological zone offers the best return in C over 20 years under no-tillage for all C prices. The Mallee offers the least return for both minimum and no-tillage scenarios. At a price of $200/Mg C, the transition from conventional tillage to minimum or no-tillage practices will only realise 19% and 33%, respectively, of the total biogeochemical sequestration potential of crop and pasture systems of the Southern Region over a 20-year period.

The application of the statutory derivative action in Pt 2F.1A of the Corporations Act to companies in liquidation under Ch 5 : Well oiled machine or is Pt 2F.1A in need of a tune up?

The statutory derivative action was introduced in Australia in 2000. This right of action has been debated in the literature and introduced in a number of other jurisdictions as well. However, it is by no means clear that all issues have been resolved despite its operation in Australia for over 10 years. This article considers the application of Pt 2F.1A of the Corporations Act to companies in liquidation under Ch 5. It demonstrates that the application involves consideration of not only proper statutory interpretation but also policy matters around the role and the supervision by the court of a liquidator once a company has entered liquidation.

Machine recognition of hand-drawn circuit diagrams

An application of image processing techniques to recognition of hand-drawn circuit diagrams is presented. The scanned image of a diagram is pre-processed to remove noise and converted to bilevel. Morphological operations are applied to obtain a clean, connected representation using thinned lines. The diagram comprises of nodes, connections and components. Nodes and components are segmented using appropriate thresholds on a spatially varying object pixel density. Connection paths are traced using a pixel-stack. Nodes are classified using syntactic analysis. Components are classified using a combination of invariant moments, scalar pixel-distribution features, and vector relationships between straight lines in polygonal representations. A node recognition accuracy of 82% and a component recognition accuracy of 86% was achieved on a database comprising 107 nodes and 449 components. This recogniser can be used for layout “beautification” or to generate input code for circuit analysis and simulation packages

Application of machine learning in fault diagnostics of mechanical systems

A diagnostic method based on Bayesian Networks (probabilistic graphical models) is presented. Unlike conventional diagnostic approaches, in this method instead of focusing on system residuals at one or a few operating points, diagnosis is done by analyzing system behavior patterns over a window of operation. It is shown how this approach can loosen the dependency of diagnostic methods on precise system modeling while maintaining the desired characteristics of fault detection and diagnosis (FDD) tools (fault isolation, robustness, adaptability, and scalability) at a satisfactory level. As an example, the method is applied to fault diagnosis in HVAC systems, an area with considerable modeling and sensor network constraints.

Demonstration of closed-­loop airborne sense-­and-­avoid using machine vision

This paper describes a vision-based airborne collision avoidance system developed by the Australian Research Centre for Aerospace Automation (ARCAA) under its Dynamic Sense-and-Act (DSA) program. We outline the system architecture and the flight testing undertaken to validate the system performance under realistic collision course scenarios. The proposed system could be implemented in either manned or unmanned aircraft, and represents a step forward in the development of a “sense-and-avoid” capability equivalent to human “see-and-avoid”.

Understanding business process models : the costs and benefits of structuredness

Previous research has put forward a number of properties of business process models that have an impact on their understandability. Two such properties are compactness and(block-)structuredness. What has not been sufficiently appreciated at this point is that these desirable properties may be at odds with one another. This paper presents the results of a two-pronged study aimed at exploring the trade-off between compactness and structuredness of process models. The first prong of the study is a comparative analysis of the complexity of a set of unstructured process models from industrial practice and of their corresponding structured versions. The second prong is an experiment wherein a cohort of students was exposed to semantically equivalent unstructured and structured process models. The key finding is that structuredness is not an absolute desideratum vis-a-vis for process model understandability. Instead, subtle trade-offs between structuredness and other model properties are at play.