Prognostics, the real issues involved with predicting life remaining

Engel, Stephen J.; Gilmartin, B.J.; Bongort, K.; Hess, Andrew

doi:10.1109/aero.2000.877920

Cited by 170 publications

(134 citation statements)

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“…As given in Figure 2 and 3, the vocalization of healthy chickens presented higher intensity and uniformity in the shape of vocalization than the unhealthy ones. This was also reported in the literature (Engel et al, 2000;Mckay et al, 2005).…”

Section: Obtained Signalssupporting

confidence: 74%

An Intelligent Procedure for the Detection and Classification of Chickens Infected by Clostridium Perfringens Based on their Vocalization

Sadeghi

Banakar

Khazaee

et al. 2015

Rev. Bras. Cienc. Avic.

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In this study, an intelligent method was implemented for the detection and classification of chickens by infected Clostridium perfringens type A based on their vocalization. To this aim, the birds were first divided into two groups that were placed in separate cages with 15 chickens each. Chickens were inoculated with Clostridium perfringens type A on day 14. In order to ensure the absence of secondary diseases and their probable effect on bird vocalization, vaccines for common diseases were administered. During 30 days of the experiment, chicken vocalization was recorded every morning at 8 a.m. using a microphone and a data collection card under equal and controlled conditions. Sound signals were investigated in time domains, and 23 features were selected. Using Fisher Discriminate Analysis (FDA), five of the most important and effective features were chosen. Neural Network Pattern Recognition (NNPR) structure with one hidden layer was applied to detect signals and classifying healthy and unhealthy chickens. Firstly, this neural network was trained with 34 samples, after which eight samples were tested for accuracy. Classification accuracy was 66.6 and 100% for days 16 and 22; i.e., two and eight days after the disease, respectively. The results of this study demonstrated the usefulness and effectiveness of intelligent methods for diagnosing diseases in chickens.

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Section: Obtained Signalssupporting

confidence: 74%

An Intelligent Procedure for the Detection and Classification of Chickens Infected by Clostridium Perfringens Based on their Vocalization

Sadeghi

Banakar

Khazaee

et al. 2015

Rev. Bras. Cienc. Avic.

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“…But predicting the remaining useful life of something (prognosis) is not the same as predicting the optimum replacement age, or even a priori predictions of the end-of-life [13]. Instead of being static and history-based, prognostics are dynamic and continuously updated with new information [14] to predict the death of an item "to manage business risks that result from equipment failing unexpectedly" [15].…”

Section: 2mentioning

confidence: 99%

“…Firstly, Engel et al [13] said "Condition-based assessments, the underpinning of the Condition-Based Maintenance philosophy, have usually emphasized the diagnosis of problems rather than the prediction of remaining life. Prognoses are considerably more difficult to formulate since their accuracy is subject to stochastic processes that have not yet happened".…”

Section: 2mentioning

confidence: 99%

Risk Modelling of Heavy Mobile Equipment to Determine Optimum Replacement Ages

Kirstein

Visser

2017

SAJIE

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Maintenance and physical asset managers often have to decide when a major asset needs to be replaced. The main objective of this study was to develop a methodology to determine the optimum replacement age of heavy mobile equipment that is close to the end of its life. The study was conducted on an old electric rope shovel used at a surface coal mining operation. The failure impact and failure probability estimates of components were obtained from subject matter experts through Delphi analyses. A stochastic-andparametric-estimation modelling solution was developed to perform quantitative risk analyses using their inputs. The solution calculated the expected loss of the rope shovel as a function of machine-age within a 90 per cent confidence interval. The study demonstrated that the optimum replacement age of heavy mobile equipment can be obtained by modelling the expected losses due to the failure of critical end-of-life components, taking into account the uncertainty in data obtained from subject matter experts. OPSOMMINGFisiese bate-en instandhoudingsbestuurders moet dikwels besluit wanneer om 'n groot bate te vervang. Die hoofdoel van hierdie studie was om 'n metodologie te ontwikkel om die optimale vervangtyd van swaar mobiele toerusting naby die einde van sy leeftyd te bepaal. Die studie was uitgevoer op 'n ou elektriese tougraaf wat by 'n oopgroef steenkool myn aangewend word. Die falings impak en falings waarskynlikhede van komponente is verkry vanaf onderwerpkenners deur middel van Delphi analises. 'n Stogastiese-en-parametriese-beraming modellering oplossing was ontwikkel om kwantitatiewe risiko analises met hul insette te doen. Die oplossing het die verwagte verliese van die tougraaf as 'n funksie van ouderdom bereken binne 'n 90 persent vertrouensinterval. Die studie het getoon dat die optimum vervangingsouderdom van swaar mobiele toerusting bepaal kan word deur die verwagte verliese as gevolg van falings van kritieke komponente naby die einde van hul leeftyd te modelleer, met inagname van die onsekerheid in data verkry vanaf onderwerpkenners.

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“…A general method was purposed by Chelidze and Cusumano [108] for tracking the evolution of hidden damage process in the situation that a slowly evolving damage process is coupled to a fast, directly observable dynamical system. Some different approaches used model-based techniques for prognosis were proposed in [109][110][111][112][113][114]. However, model-based techniques are merely applied for some specific components and each requires a different mathematical model.…”

Section: Model-based Approachesmentioning

confidence: 99%

“…Model-based approaches [104][105][106][107][108][109][110][111][112][113][114] • Can be highly accurate • Require less data then data-driven approaches…”

Section: Approachesmentioning

confidence: 99%

Machine Fault Diagnosis and Prognosis: The State of The Art

Tran¹,

Yang²

2009

International Journal of Fluid Machinery and Systems

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Machine fault diagnostic and prognostic techniques have been the considerable subjects of condition-based maintenance system in the recent time due to the potential advantages that could be gained from reducing downtime, decreasing maintenance costs, and increasing machine availability. For the past few years, research on machine fault diagnosis and prognosis has been developing rapidly. These publications covered in the wide range of statistical approaches to model-based approaches. With the aim of synthesizing and providing the information of these researches for researcher's community, this paper attempts to summarize and classify the recent published techniques in diagnosis and prognosis of rotating machinery. Furthermore, it also discusses the opportunities as well as the challenges for conducting advance research in the field of machine prognosis.

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Prognostics, the real issues involved with predicting life remaining

Cited by 170 publications

References 1 publication

An Intelligent Procedure for the Detection and Classification of Chickens Infected by Clostridium Perfringens Based on their Vocalization

An Intelligent Procedure for the Detection and Classification of Chickens Infected by Clostridium Perfringens Based on their Vocalization

Risk Modelling of Heavy Mobile Equipment to Determine Optimum Replacement Ages

Machine Fault Diagnosis and Prognosis: The State of The Art

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