Asset life prediction and maintenance decision-making using a non-linear non-Gaussian state space model

Estimating and predicting degradation processes of engineering assets is crucial for reducing the cost and insuring the productivity of enterprises. Assisted by modern condition monitoring (CM) technologies, most asset degradation processes can be revealed by various degradation indicators extracted from CM data. Maintenance strategies developed using these degradation indicators (i.e. condition-based maintenance) are more cost-effective, because unnecessary maintenance activities are avoided when an asset is still in a decent health state. A practical difficulty in condition-based maintenance (CBM) is that degradation indicators extracted from CM data can only partially reveal asset health states in most situations. Underestimating this uncertainty in relationships between degradation indicators and health states can cause excessive false alarms or failures without pre-alarms. The state space model provides an efficient approach to describe a degradation process using these indicators that can only partially reveal health states. However, existing state space models that describe asset degradation processes largely depend on assumptions such as, discrete time, discrete state, linearity, and Gaussianity. The discrete time assumption requires that failures and inspections only happen at fixed intervals. The discrete state assumption entails discretising continuous degradation indicators, which requires expert knowledge and often introduces additional errors. The linear and Gaussian assumptions are not consistent with nonlinear and irreversible degradation processes in most engineering assets. This research proposes a Gamma-based state space model that does not have discrete time, discrete state, linear and Gaussian assumptions to model partially observable degradation processes. Monte Carlo-based algorithms are developed to estimate model parameters and asset remaining useful lives. In addition, this research also develops a continuous state partially observable semi-Markov decision process (POSMDP) to model a degradation process that follows the Gamma-based state space model and is under various maintenance strategies. Optimal maintenance strategies are obtained by solving the POSMDP. Simulation studies through the MATLAB are performed; case studies using the data from an accelerated life test of a gearbox and a liquefied natural gas industry are also conducted. The results show that the proposed Monte Carlo-based EM algorithm can estimate model parameters accurately. The results also show that the proposed Gamma-based state space model have better fitness result than linear and Gaussian state space models when used to process monotonically increasing degradation data in the accelerated life test of a gear box. Furthermore, both simulation studies and case studies show that the prediction algorithm based on the Gamma-based state space model can identify the mean value and confidence interval of asset remaining useful lives accurately. In addition, the simulation study shows that the proposed maintenance strategy optimisation method based on the POSMDP is more flexible than that assumes a predetermined strategy structure and uses the renewal theory. Moreover, the simulation study also shows that the proposed maintenance optimisation method can obtain more cost-effective strategies than a recently published maintenance strategy optimisation method by optimising the next maintenance activity and the waiting time till the next maintenance activity simultaneously.

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.

Compensation of unmodeled aircraft dynamics in airborne inspection of linear infrastructure assets

Fixed-wing aircraft equipped with downward pointing cameras and/or LiDAR can be used for inspecting approximately piecewise linear assets such as oil-gas pipelines, roads and power-lines. Automatic control of such aircraft is important from a productivity and safety point of view (long periods of precision manual flight at low-altitude is not considered reasonable from a safety perspective). This paper investigates the effect of any unwanted coupling between guidance and autopilot loops (typically caused by unmodeled delays in the aircraft’s response), and the specific impact of any unwanted dynamics on the performance of aircraft undertaking inspection of piecewise linear corridor assets (such as powerlines). Simulation studies and experimental flight tests are used to demonstrate the benefits of a simple compensator in mitigating the unwanted lateral oscillatory behaviour (or coupling) that is caused by unmodeled time constants in the aircraft dynamics.

An exploration of the reasons why adolescents engage in traditional and cyber bullying

This study explored the reasons underlying adolescents’ perceptions of why their peers engage in bullying in the real and the cyber world. While there has been much research on why bullies engage in such behaviour, ranging from personality characteristics to social or familial reasons, the perceptions of young people on the motives of cyberbullies has not been researched. A new instrument, based on interviews and a literature review was piloted to measure young people’s perceptions of why their peers engage in both traditional and cyberbullying behaviour, according to their role in bullying. Four hundred students were surveyed in three co-educational independent secondary schools. A comparison between perceptions of bullies’ motives in traditional and cyberbullying was made. Implications for interventions with bullies are discussed.

Machine prognostics based on health state estimation using SVM

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 safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and an 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. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of machines based on health state probability estimation and involving historical knowledge embedded in the closed loop diagnostics and prognostics systems. The technique uses a Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation, which can affect the accuracy of prediction. To validate the feasibility of the proposed model, real life historical data from bearings of High Pressure Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life. The results obtained were very encouraging and showed that the proposed prognostic system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.

Bearing fault prognosis based on health state probability estimation

In condition-based maintenance (CBM), effective diagnostic and prognostic tools are essential for maintenance engineers to identify imminent fault and predict the remaining useful life before the components finally fail. This enables remedial actions to be taken in advance and reschedule of production if necessary. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of bearings based on health state probability estimation and historical knowledge embedded in the closed loop diagnostics and prognostics system. The technique uses the Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation process to provide long term prediction. To validate the feasibility of the proposed model, real life fault historical data from bearings of High Pressure-Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life (RUL). The results obtained were very encouraging and showed that the proposed prognosis system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.

Intelligent reliability analysis with incomplete covariates

The reliability analysis is crucial to reducing unexpected down time, severe failures and ever tightened maintenance budget of engineering assets. Hazard based reliability methods are of particular interest as hazard reflects the current health status of engineering assets and their imminent failure risks. Most existing hazard models were constructed using the statistical methods. However, these methods were established largely based on two assumptions: one is the assumption of baseline failure distributions being accurate to the population concerned and the other is the assumption of effects of covariates on hazards. These two assumptions may be difficult to achieve and therefore compromise the effectiveness of hazard models in the application. To address this issue, a non-linear hazard modelling approach is developed in this research using neural networks (NNs), resulting in neural network hazard models (NNHMs), to deal with limitations due to the two assumptions for statistical models. With the success of failure prevention effort, less failure history becomes available for reliability analysis. Involving condition data or covariates is a natural solution to this challenge. A critical issue for involving covariates in reliability analysis is that complete and consistent covariate data are often unavailable in reality due to inconsistent measuring frequencies of multiple covariates, sensor failure, and sparse intrusive measurements. This problem has not been studied adequately in current reliability applications. This research thus investigates such incomplete covariates problem in reliability analysis. Typical approaches to handling incomplete covariates have been studied to investigate their performance and effects on the reliability analysis results. Since these existing approaches could underestimate the variance in regressions and introduce extra uncertainties to reliability analysis, the developed NNHMs are extended to include handling incomplete covariates as an integral part. The extended versions of NNHMs have been validated using simulated bearing data and real data from a liquefied natural gas pump. The results demonstrate the new approach outperforms the typical incomplete covariates handling approaches. Another problem in reliability analysis is that future covariates of engineering assets are generally unavailable. In existing practices for multi-step reliability analysis, historical covariates were used to estimate the future covariates. Covariates of engineering assets, however, are often subject to substantial fluctuation due to the influence of both engineering degradation and changes in environmental settings. The commonly used covariate extrapolation methods thus would not be suitable because of the error accumulation and uncertainty propagation. To overcome this difficulty, instead of directly extrapolating covariate values, projection of covariate states is conducted in this research. The estimated covariate states and unknown covariate values in future running steps of assets constitute an incomplete covariate set which is then analysed by the extended NNHMs. A new assessment function is also proposed to evaluate risks of underestimated and overestimated reliability analysis results. A case study using field data from a paper and pulp mill has been conducted and it demonstrates that this new multi-step reliability analysis procedure is able to generate more accurate analysis results.

The power of hands-on exercises in SCADA cyber security education

For decades Supervisory Control and Data Acquisition (SCADA) and Industrial Control Systems (ICS) have used computers to monitor and control physical processes in many critical industries, including electricity generation, gas pipelines, water distribution, waste treatment, communications and transportation. Increasingly these systems are interconnected with corporate networks via the Internet, making them vulnerable and exposed to the same risks as those experiencing cyber-attacks on a conventional network. Very often SCADA networks services are viewed as a specialty subject, more relevant to engineers than standard IT personnel. Educators from two Australian universities have recognised these cultural issues and highlighted the gap between specialists with SCADA systems engineering skills and the specialists in network security with IT background. This paper describes a learning approach designed to help students to bridge this gap, gain theoretical knowledge of SCADA systems' vulnerabilities to cyber-attacks via experiential learning and acquire practical skills through actively participating in hands-on exercises.

Vulnerability held not to be pre-requisite for establishing a duty of care in claims of pure economic Loss

Case note Apache Energy Ltd v Alcoa of Australia Ltd (No 2) [2013] In 2011, headlines were made when Alcoa sued Apache Energy and its partners for $158 million, a loss it claimed was a consequence of Apache Energy failing to adequately inspect and maintain the gas pipelines that supplied the gas used by Alcoa in its business. As the loss was not a consequence of any property damage or injury to Alcoa, the loss is characterised as pure economic loss...

Integrated approach for diagnostics and prognostics of HP LNG pump based on health state probability estimation

Effective machine fault prognostic technologies can lead to elimination of unscheduled downtime and increase machine useful life and consequently lead to reduction of maintenance costs as well as prevention of human casualties in real engineering asset management. This paper presents a technique for accurate assessment of the remnant life of machines based on health state probability estimation technique and historical failure knowledge embedded in the closed loop diagnostic and prognostic system. To estimate a discrete machine degradation state which can represent the complex nature of machine degradation effectively, the proposed prognostic model employed a classification algorithm which can use a number of damage sensitive features compared to conventional time series analysis techniques for accurate long-term prediction. To validate the feasibility of the proposed model, the five different level data of typical four faults from High Pressure Liquefied Natural Gas (HP-LNG) pumps were used for the comparison of intelligent diagnostic test using five different classification algorithms. In addition, two sets of impeller-rub data were analysed and employed to predict the remnant life of pump based on estimation of health state probability using the Support Vector Machine (SVM) classifier. The results obtained were very encouraging and showed that the proposed prognostics system has the potential to be used as an estimation tool for machine remnant life prediction in real life industrial applications.

Methane Recovery and use at Grantham Piggery

This report describes the outcomes from the Australian Methane to Markets in Agriculture (AM2MA) research project PRJ-005672 ‘Methane recovery and use at a piggery – Grantham’. This project involved upgrading the biogas extraction system originally installed in conjunction with a partial floating cover, retro-fitted to the primary anaerobic pond at the QNPH Grantham piggery under an earlier AM2MA project (Project No. PRJ-003003), as described by Skerman et al (2011). Following the system upgrade, this project also included installing a biogas reticulation pipeline to supply biogas from the extraction system, to a water heating system used to heat water circulated through underfloor heating pads in the piggery farrowing sheds. This biogas fired water heating system has the potential to significantly reduce on-farm energy costs by replacing a significant proportion of the Liquid Petroleum Gas (LPG) previously used for farrowing shed heating. Further monitoring of the biogas system performance has also been carried out. This report describes the work undertaken and outlines the monitoring results, implications, conclusions and recommendations arising from this work.

Fundamentos da exploração de petróleo e gás natural e análise geoeconômica da matriz energética brasileira atual e projeções futuras

A análise da matriz energética mundial, assim como a brasileira, nos mostra que o gás natural representará em breve um importante insumo energético favorecendo a balança de pagamentos nacional, visto que o gás poderá ser usado tanto para consumo interno, quanto para exportação. O aumento das reservas nacionais de hidrocarbonetos se deve ao desenvolvimento de tecnologias, que favoreceram o conhecimento das bacias brasileiras quanto ao seu potencial produtor de combustíveis fósseis, permitindo a descoberta de novas jazidas. O amadurecimento do mercado nacional quanto ao consumo de gás natural passa pela construção de uma infraestrutura robusta, eficiente e que possibilite a captação, o armazenamento e distribuição do mesmo. O Brasil tem todos os requisitos necessários para adentrar ao seleto grupo de países exportadores de hidrocarbonetos, a descoberta do Pré-sal tende a incrementar a importância do gás natural para o país. Ao final do trabalho são descritos cenários futuros (quanto o consumo de energéticos), onde se vê que o consumo de energia para os próximos anos crescerá a taxas superiores as das duas últimas décadas. Análise destes cenários permite antecipar o interesse de futuros investimentos no desenvolvimento do conhecimento geológico para áreas promissoras.

Alternativas técnico produtivas para o aproveitamento do gás natural da camada pré-sal

As reservas brasileiras de petróleo e gás natural apresentarão um significativo crescimento a partir do desenvolvimento das reservas da camada do pré-sal. Segundo estimativas elaboradas pela EPE e pela EIA, nos próximos vinte anos, haverá um quadro de grande oferta de gás natural no país, com a oferta excedendo a demanda. Como o segmento de transporte do gás natural tem grande importância na formação do custo desse energético, uma tecnologia de transporte menos onerosa irá proporcionar um cenário mais vantajoso para a entrada do gás no mercado. A presente dissertação analisa duas tecnologias disponíveis para escoamento do gás natural da camada pré-sal gasoduto submarino e gás natural liquefeito embarcado e as possibilidades de utilização no mercado interno ou para ser exportado. De acordo com dados da Petrobras, foram utilizadas três rotas para escoar o gás do pré-sal. A metodologia Valor Presente Líquido (VPL) foi utilizada para analisar qual dos investimentos em transporte é mais viável economicamente. Os resultados mostraram que de acordo com as perspectivas de produção do gás natural no horizonte de tempo analisado as duas tecnologias serão viáveis, com o transporte por gasodutos a alternativa mais viável economicamente.

Perspectivas da utilização de gás natural no Brasil

A experiência do mundo desenvolvido mostra que o crescimento econômico de um país sempre requer uma grande disponibilidade de capacidade de produção própria de energia, a preços de mercado competitivos e atraentes. A estabilidade de relações comerciais, definidas por uma regulação transparente e objetiva, adiversidade de fontes supridoras e a existência de políticas de governo que incentivemo desenvolvimento sustentável do mercado consumidor são requisitos imprescindíveis à captação de novos investidores para o setor energético. Não obstante o incremento recente do percentual de gás natural na matriz energética nacional e a perspectiva mundial de aumento do uso deste combustível, alguns desafios ainda se interpõem ao efetivo crescimento da participação do gás natural no mercado energético nacional. Itens críticos para a expansão do uso do gás natural no Brasil, tais como a realização de grandes investimentos em infraestrutura de produção, transporte e distribuição, a exploração das principais reservas de hidrocarbonetos, a redução das incertezas com relação à evolução da demanda por gás no mercado industrial e termelétrico, aliados aos grandes desafios tecnológicos para produção do pré-sal brasileiro geram grandes riscos ao retorno de investimentos no setor, causando postergações ao desenvolvimento de novas áreas de produção e à expansão da demanda de gás. O objetivo deste trabalho é apresentar uma visão ampla do mercado brasileiro de gás natural, baseada emcenários possíveis e desafios futuros à expansão da utilização do gás no país, desenvolvidos a partir da análise de levantamento de dados de produção e consumo e do atual estágio da evolução da indústria gasífera brasileira. Este trabalho apresenta também um conjunto de proposições como objetivo de mitigar as dificuldades citadas e alavancar o desenvolvimento do mercado de gás no Brasi

Era uma vez uma ilha de Pescadores Artesanais: impactos socioambientais dos grandes complexos industriais, conflitos e resistência (Ilha da Madeira/Itaguaí/RJ)

O presente estudo visa analisar os processos de transformações econômicos, políticos e socioambientais decorrentes da instalação dos grandes empreendimentos em territórios tradicionais da pesca, mais especificamente, as experiências da comunidade pesqueira da Ilha da Madeira/baía de Sepetiba/Itaguaí-RJ, desde a instalação da Cia Ingá Mercantil (1964) até os dias atuais, identificando, nos vários ciclos de industrialização: os fatores endógenos e exógenos que contribuem para a vulnerabilidade ou sustentabilidade da pesca artesanal e do meio ambiente. Sinalizando, nesta experiência, alguns aspectos que possam servir de referência para outras comunidades pesqueiras que vivenciam problemas similares. Introduzimos a problemática a partir da contextualização da pesca artesanal no Brasil, as políticas, a regulamentação da atividade, a organização dos pescadores. Ao evidenciar a pesca artesanal no estado do Rio de Janeiro, destacamos os conflitos socioambientais decorrentes da instalação de complexos industriais em territórios tradicionalmente ocupados por pescadores, com destaque para os conflitos relativos à instalação do Porto de Açu, em São João da Barra/RJ e os gasodutos para a refinaria de petróleo na baía de Guanabara. Aprofundamos a temática, a partir de um estudo de caso na Ilha da Madeira, baía de Sepetiba, Itaguaí/RJ. Esse território, tradicionalmente ocupado por pescadores, mergulhou em uma crise socioambiental a partir da década de 60 e, desde então, vem passando por diversas transformações: alteração radical da paisagem, degradação ambiental além do sufocamento da atividade pesqueira. Os fatos são evidenciados por meio de pesquisas bibliográficas, documentais, registros fotográficos, sobretudo, história de oral. Em entrevistas com informantes-chave resgatamos as memórias pessoais e, nesse percurso, fomos recuperando parte da história do território. Caracterizando a paisagem, a vida e trabalho dos pescadores, a cultura local: tradições, costumes, valores, aspectos materiais e simbólicos, em um período anterior a chegada das indústrias, quando a Ilha da Madeira era de fato, uma Ilha. Em suas narrativas os entrevistados foram pontuando as sucessões dos trágicos acontecimentos que ocorreram após a instalação da Ingá até os dias atuais. Esses fatos são demarcados em ciclos que compõem a crise socioambiental no território. Um estudo que retrata a injustiça ambiental, a vulnerabilidade de uma comunidade pesqueira, cuja experiência serve de alerta para outras comunidades tradicionais. Ressaltamos a importância das articulações entre os movimentos locais com instâncias extras locais, sinalizando para a necessidade de democratização dos processos decisórios e da gestão compartilhada dos recursos de uso comum. Também pontuamos a urgência de superação do paradigma que dissocia desenvolvimento, natureza e sociedade, fortalecendo uma lógica de produção que, ao se impor como hegemônica sufoca todas outras formas de organização do trabalho.