72,188 research outputs found
Implementation of a multi-scale predictive system of the degradation of the urban front in Brno, Czech Republic
The unavoidable deterioration of the built urban front in the cities has been increasingly generating a huge environmental impact. From this perspective, it is necessary to develop systematized methods that facilitate strategic maintenance of the facades and which study the variables that can potentially play a significant role in the damage occurrence. Therefore it is convenient to implement analytical methodologies to the decision making process on conservation and sustainability of the built urban front with a macro-scale approach. The BRAIN platform (Building Research Analysis and Information Network) is a Multi-scale Predictive System of the Degradation of the Urban Front. By means of periodic inspections, BRAIN allows analyses of damage progression and prediction of the future affectation, based on survival/reliability statistical models. The aim of this paper is to introduce a preliminary study on the implementation of the Urban Laboratory in the city of Brno, Czech Republic. Results of this primary approach have been displayed and discussed.Peer ReviewedPostprint (published version
Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models
In the field of renewable energy, reliability analysis techniques combining the operating time of the system with the observation of operational and environmental conditions, are gaining importance over time.
In this paper, reliability models are adapted to incorporate monitoring data on operating assets, as well as information on their environmental conditions, in their calculations. To that end, a logical decision tool based on two artificial neural networks models is presented. This tool allows updating assets reliability analysis according to changes in operational and/or environmental conditions.
The proposed tool could easily be automated within a supervisory control and data acquisition system, where reference values and corresponding warnings and alarms could be now dynamically generated using the tool. Thanks to this capability, on-line diagnosis and/or potential asset degradation prediction can be certainly improved.
Reliability models in the tool presented are developed according to the available amount of failure data and are used for early detection of degradation in energy production due to power inverter and solar trackers functional failures.
Another capability of the tool presented in the paper is to assess the economic risk associated with the system under existing conditions and for a certain period of time. This information can then also be used to trigger preventive maintenance activities
On competing risk and degradation processes
Lehmann's ideas on concepts of dependence have had a profound effect on
mathematical theory of reliability. The aim of this paper is two-fold. The
first is to show how the notion of a ``hazard potential'' can provide an
explanation for the cause of dependence between life-times. The second is to
propose a general framework under which two currently discussed issues in
reliability and in survival analysis involving interdependent stochastic
processes, can be meaningfully addressed via the notion of a hazard potential.
The first issue pertains to the failure of an item in a dynamic setting under
multiple interdependent risks. The second pertains to assessing an item's life
length in the presence of observable surrogates or markers. Here again the
setting is dynamic and the role of the marker is akin to that of a leading
indicator in multiple time series.Comment: Published at http://dx.doi.org/10.1214/074921706000000473 in the IMS
Lecture Notes--Monograph Series
(http://www.imstat.org/publications/lecnotes.htm) by the Institute of
Mathematical Statistics (http://www.imstat.org
Threshold Regression for Survival Analysis: Modeling Event Times by a Stochastic Process Reaching a Boundary
Many researchers have investigated first hitting times as models for survival
data. First hitting times arise naturally in many types of stochastic
processes, ranging from Wiener processes to Markov chains. In a survival
context, the state of the underlying process represents the strength of an item
or the health of an individual. The item fails or the individual experiences a
clinical endpoint when the process reaches an adverse threshold state for the
first time. The time scale can be calendar time or some other operational
measure of degradation or disease progression. In many applications, the
process is latent (i.e., unobservable). Threshold regression refers to
first-hitting-time models with regression structures that accommodate covariate
data. The parameters of the process, threshold state and time scale may depend
on the covariates. This paper reviews aspects of this topic and discusses
fruitful avenues for future research.Comment: Published at http://dx.doi.org/10.1214/088342306000000330 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Machine Prognosis with Full Utilization of Truncated Lifetime Data
Intelligent machine fault prognostics estimates how soon and likely a failure will occur with little human expert judgement. It minimizes production downtime, spares inventory and maintenance labour costs. Prognostic models, especially probabilistic methods, require numerous historical failure instances. In practice however, industrial and military communities would rarely allow their engineering assets to run to failure. It is only known that the machine component survived up to the time of repair or replacement but there is no information as to when the component would have failed if left undisturbed. Data of this sort are called truncated data. This paper proposes a novel model, the Intelligent Product Limit Estimator (iPLE), which utilizes truncated data to perform adaptive long-range prediction of a machine component's remaining lifetime. It takes advantage of statistical models' ability to provide useful representation of survival probabilities, and of neural networks ability to recognise nonlinear relationships between a machine component's future survival condition and a given series of prognostic data features. Progressive bearing degradation data were simulated and used to train and validate the proposed model. The results support our hypothesis that the iPLE can perform better than similar prognostics models that neglect truncated data
On engineering reliability concepts and biological aging
Some stochastic approaches to biological aging modeling are studied. We assume that an organism acquires a random resource at birth. Death occurs when the accumulated dam-age (wear) exceeds this initial value, modeled by the discrete or continuous random vari-ables. Another source of death of an organism is also taken into account, when it occurs as a consequence of a shock or of a demand for energy, which is a generalization of the Strehler-Mildwan’s model (1960). Biological age based on the observed degradation is also defined. Finally, aging properties of repairable systems are discussed. We show that even in the case of imperfect repair, which is certainly the case for organisms, aging slows down with age and eventually can even fade out. This presents another possible explanation for the human mortality rate plateaus.mortality
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