Prognosis of bearing failure based on health state estimation

This paper proposes a new prognosis model based on the technique for health state estimation of machines for accurate assessment of the remnant life. For the evaluation of health stages of machines, the Support Vector Machine (SVM) classifier was employed to obtain the probability of each health state. Two case studies involving bearing failures were used to validate the proposed model. Simulated bearing failure data and experimental data from an accelerated bearing test rig were used to train and test the model. The result obtained is very encouraging and shows that the proposed prognostic model produces promising results and has the potential to be used as an estimation tool for machine remnant life prediction.

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.

Integrated approach for HP-LNG pump diagnosis and prognostics based on health state probability estimation

This paper presents an innovative prognostics model based on health state probability estimation embedded in the closed loop diagnostic and prognostic system. To employ an appropriate classifier for health state probability estimation in the proposed prognostic model, the comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault levels of three faults in HP-LNG pump. Two sets of impeller-rubbing data were employed for the prediction of pump remnant life based on estimation of discrete health state probability using an outstanding capability of SVM and a feature selection technique. The results obtained were very encouraging and showed that the proposed prognosis system has the potential to be used as an estimation tool for machine remnant life prediction in real life industrial applications.

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.

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.

Modelling health state preference data using fixed and random effects models

The use of preference-based measures of health in the measurement of Health Related Quality of Life has become widely used in health economics. Hence, the development of preference-based measures of health has been a major concern for researchers throughout the world. This study aims to model health state preference data using a new preference-based measure of health (the SF- 6D) and to suggest alternative models for predicting health state utilities using fixed and random effects models. It also seeks to investigate the problems found in the SF-6D and to suggest eventual changes to it.

Predicting time trade-off health state valuations of adolescents in four Pacific countries using the assessment of quality-of-life (AQoL-6D) instrument

Objectives: Pacific Obesity Prevention in Communities (OPIC) is a community-based intervention project targeting adolescent obesity in Australia, New Zealand, Fiji, and Tonga. The Assessment of Quality of Life Mark 2 (AQoL-6D) instrument was completed by 15,481 adolescents to obtain a description of the quality of life associated with adolescent overweight and obesity, and a corresponding utility score for use in a cost–utility analysis of the interventions. This article describes the recalibration of this utility instrument for adolescents in each country.

Methods: The recalibration was based on country-specific time trade-off (TTO) data for 30 multiattribute health states constructed from the AQoL-6D descriptive system. Senior secondary students, in a classroom setting, responded to 10 health state scenarios each. These TTO interviews were conducted for 24 groups, comprising 279 students in the four countries resulting in 2790 completed TTO scores. The TTO scores were econometrically transformed by regressing the TTO scores upon predicted scores from the AQoL-6D to produce country-specific algorithms. The latter incorporated country-specific “corrections” to the Australian adult utility weights in the original AQoL.

Results: This article reports two methodological elements not previously reported. The first is the econometric modification of an extant multi-attribute utility instrument to accommodate cultural and other group-specific differences in preferences. The second is the use of the TTO technique with adolescents in a classroom group setting. Significant differences in utility scores were found between the four countries.

Conclusion: Statistical results indicate that the AQoL-6D can be validly used in the economic evaluation of both the OPIC interventions and other adolescent programs.

Measuring health state preferences for hemophilia: Development of a disease-specific utility instrument

Research has shown that disease-specific health related quality of life (HRQoL) instruments are more responsive than generic instruments to particular disease conditions. However, only a few studies have used disease-specific instruments to measure HRQoL in hemophilia. The goal of this project was to develop a disease-specific utility instrument that measures patient preferences for various hemophilia health states. The visual analog scale (VAS), a ranking method, and the standard gamble (SG), a choice-based method incorporating risk, were used to measure patient preferences. Study participants (n = 128) were recruited from the UT/Gulf States Hemophilia and Thrombophilia Center and stratified by age: 0–18 years and 19+. ^ Test retest reliability was demonstrated for both VAS and SG instruments: overall within-subject correlation coefficients were 0.91 and 0.79, respectively. Results showed statistically significant differences in responses between pediatric and adult participants when using the SG (p = .045). However, no significant differences were shown between these groups when using the VAS (p = .636). When responses to VAS and SG instruments were compared, statistically significant differences in both pediatric (p < .0001) and adult (p < .0001) groups were observed. Data from this study also demonstrated that persons with hemophilia with varying severity of disease, as well as those who were HIV infected, were able to evaluate a range of health states for hemophilia. This has important implications for the study of quality of life in hemophilia and the development of disease-specific HRQoL instruments. ^ The utility measures obtained from this study can be applied in economic evaluations that analyze the cost/utility of alternative hemophilia treatments. Results derived from the SG indicate that age can influence patients' preferences regarding their state of health. This may have implications for considering treatment options based on the mean age of the population under consideration. Although both instruments independently demonstrated reliability and validity, results indicate that the two measures may not be interchangeable. ^

The Health State of the Cryosphere

The term cryosphere is used to refer to all portions of the Earth surface where water appears in solid form. This includes the snow cover; sea, lake and river ice; glaciers, ice caps and ice sheets; and permafrost. The aim of this contribution is to present the current state of the cryosphere. Emphasis will be given to sea ice and continental ice masses (glaciers, ice caps and ice sheets), and the contribution of the losses from the latter to sea level rise (SLR).

Exclusion criteria in national health state valuation studies : a systematic review

BACKGROUND: Health state valuation data are often excluded from studies that aim to provide a nationally representative set of values for preference-based health-related quality of life (HRQoL) instruments. The purpose was to provide a systematic examination of exclusion criteria used in the derivation of societal scoring algorithms for preference-based HRQoL instruments. METHODS: Data sources included MEDLINE, official instrument websites, and publication reference lists. Analyses that used data from national valuation studies and reported a scoring algorithm for a generic preference-based HRQoL instrument were included. Data extraction included exclusion criteria and associated justifications, exclusion rates, the characteristics of excluded respondents, and analyses that explored consequential implications of exclusion criteria on the respective national tariff. RESULTS: Seventy-six analyses (from 70 papers) met the inclusion criteria. In addition to being excluded for logical inconsistencies, respondents were often excluded if they valued fewer than 3 health states or if they gave the same value to all health states. Numerous other exclusion criteria were identified, with varying degrees of justification, often based on an assumption that respondents did not understand the task or as a consequence of the chosen statistical modeling techniques. Rates of exclusion ranged from 0% to 65%, with excluded respondents more likely to be older, less educated, and less healthy. Limitations included that the database search was confined to MEDLINE; study selection focused on national valuation studies that used standard gamble, time tradeoff, and/or visual analog scale techniques; and only English-language studies were included. CONCLUSION: Exclusion criteria used in national valuation studies vary considerably. Further consideration is necessary in this important and influential area of research, from the design stage to the reporting of results.

"When I saw walking I just kind of took it as wheeling": interpretations of mobility-related items in generic, preference-based health state instruments in the context of spinal cord injury

Background : In health economic analyses, health states are typically valued using instruments with few items per dimension. Due to the generic (and often reductionist) nature of such instruments, certain groups of respondents may experience challenges in describing their health state. This study is concerned with generic, preference-based health state instruments that provide information for decisions about the allocation of resources in health care. Unlike physical measurement instruments, preference-based health state instruments provide health state values that are dependent on how respondents interpret the items. This study investigates how individuals with spinal cord injury (SCI) interpret mobility-related items contained within six preference-based health state instruments.

Methods : Secondary analysis of focus group transcripts originally collected in Vancouver, Canada, explored individuals’ perceptions and interpretations of mobility-related items contained within the 15D, Assessment of Quality of Life 8-dimension (AQoL-8D), EQ-5D-5L, Health Utilities Index (HUI), Quality of Well-Being Scale Self-Administered (QWB-SA), and the 36-item Short Form health survey version 2 (SF-36v2). Ritchie and Spencer’s ‘Framework Approach’ was used to perform thematic analysis that focused on participants’ comments concerning the mobility-related items only.

Results : Fifteen individuals participated in three focus groups (five per focus group). Four themes emerged: wording of mobility (e.g., ‘getting around’ vs ‘walking’), reference to aids and appliances, lack of suitable response options, and reframing of items (e.g., replacing ‘walking’ with ‘wheeling’). These themes reflected item features that respondents perceived as relevant in enabling them to describe their mobility, and response strategies that respondents could use when faced with inaccessible items.

Conclusion : Investigating perceptions to mobility-related items within the context of SCI highlights substantial variation in item interpretation across six preference-based health state instruments. Studying respondents’ interpretations of items can help to understand discrepancies in the health state descriptions and values obtained from different instruments. This line of research warrants closer attention in the health economics and quality of life literature.

State mental health plan for a client-centered comprehensive mental health service system (FY89 - FY91).

Cover title: State mental health plan.

State of Illinois preliminary state health plan : draft.

Cover title.