Fuzzy-Logic Reliability Predictions in Microtechnologies

A mathematical model of the reliability prediction of generating units is presented,together with the reliability prediction method.Based on statistical analysis of operation reliability past data for generating units,statistical values of the repair factor and the mathematical model's parameters of the repair factor are determined.According to plan repair outage days and the mathematical model for the repair factor of a generating unit,equivalent availability factor(EAF) of the generating unit could be predicted in future three years.The reliability prediction examples for sub-critical 300 MW,supercritical 600 MW and sub-critical 600 MW fossil units are given together with reliability prediction results of 550 MW hydro units and 984 MW,990 MW nuclear units.The relative error's range for equivalent availability factor prediction values of the generating units is between –1.48% to 2.69% which indicates that reliability prediction precision is higher.By using of the reliability prediction method,prediction values for the reliability indexes of generating units can be quantitatively calculated which provides a basis for reliability objective management and optimization repair of generating units.

This paper introduces the theoretical basis of reliability prediction method based on Bellcore standard, including device failure rate and unit failure rate model. Active Bellcore standard-Telcordia SR-332 version manual is used for a Single-phase fischer-controlled smart meter reliability prediction in the commercial environment. The process and method of reliability prediction is given to the smart meter, as well as the expected failure rate results. At last, discuss the advantage and disadvantage of the reliability prediction method based on Bellcore.

Traditional reliability assessment methods are generally on the basis of failure lifetime data. The failure lifetime data of high reliability and long lifetime product are obtained difficultly by life test and accelerated life test. Degradation data can provide useful information about the reliability assessment for these products. Aiming at these problems, a method of reliability prediction is presented based on failure data's reliability assessment, accelerated performance degradation and information fusion theory. Finally, this paper gives a case of accelerated performance degradation testing of 24V/2A power supply board to steady voltage to apply the method, whose results demonstrate that this method is correct and valid for reliability assessment of high reliability and long life electronic equipments.

Reliability predictions have been for a long time a difficult task, due to the conflict between high reliability requirements and the lack of component manufacturer data. As the data available during the development phase is the product bill of materials, reliability prediction methods have developed component reliability models based on in-service field return data and/or physics of failure. The repartition of these two approaches has changed over time, where predictions were mainly based on empirical data at the beginning (MIL-HDBK-217), but more recently attempts have been made to incorporate some form of physics of failure (MIL217+, FIDES). Note, that even in these attempts the acceleration factor coefficients are still based on empirical data.

Reliability prediction of an offshore wind turbine gearbox

AimsTo devise a method to quantify and optimize tightness when inserting cortical screws, based on bone characterization and screw geometry.MethodsCortical human cadaveric diaphyseal tibiae screw holes (n = 20) underwent destructive testing to firstly establish the relationship between cortical thickness and experimental stripping torque (Tstr), and secondly to calibrate an equation to predict Tstr. Using the equation’s predictions, 3.5 mm screws were inserted (n = 66) to targeted torques representing 40% to 100% of Tstr, with recording of compression generated during tightening. Once the target torque had been achieved, immediate pullout testing was performed.ResultsCortical thickness predicted Tstr (R2 = 0.862; p < 0.001) as did an equation based on tensile yield stress, bone-screw friction coefficient, and screw geometries (R2 = 0.894; p < 0.001). Compression increased with screw tightness up to 80% of the maximum (R2 = 0.495; p < 0.001). Beyond 80%, further tightening generated no increase in compression. Pullout force did not change with variations in submaximal tightness beyond 40% of Tstr (R2 = 0.014; p = 0.175).ConclusionScrew tightening between 70% and 80% of the predicted maximum generated optimum compression and pullout forces. Further tightening did not considerably increase compression, made no difference to pullout, and increased the risk of the screw holes being stripped. While further work is needed for development of intraoperative methods for accurate and reliable prediction of the maximum tightness for a screw, this work justifies insertion torque being considerably below the maximum.Cite this article: Bone Joint Res 2020;9(8):493–500.

Worldwide, medium to short term tsunami prediction is becoming ever more essential for safeguarding man due to an un-abating population increase within low-lying coastal regions of all of the affected oceans. But hitherto there have been no verifiable methods of reliable tsunami prediction developed -except for a few isolated examples of placing arrays of costly short-lived sensors along the ocean bottom requiring monthly re-calibration efforts. This dilemma is a result of not yet having identified the proper approaches to tsunami prediction. The question on whether there do exist reliable prediction methods was answered long ago by fauna living within the coastal littoral zone that is affected by tsunamis. Especially during the last devastating "Boxing Day 050426 Tsunami" caused by the Sumatra-Andaman "Super-Earthquake" of M = 9.3 with epicenter near Simeulue Island, there were many verifiable episodes on how fish escaped the affected coastal region in time; the elephants, water-buffalos and other non-domesticated animals rushed for higher ground locations well in time before the tsunami crest and subsequent swells approached, and so on. These observations provide proof that some electromagnetic or, more likely, infrasonic local warning signatures are received by these creatures relatively long before the approaching tsunami strikes. We presume that the signatures could be infrasonic waves traveling at high speeds as under-water surface waves that could be detected by marine fauna as well as coastal animals and birds. Tsunamis have existed for millions of years and fauna of the affected coastal region has developed instinctive warning mechanisms that we need to explore. Another promising natural sensor may utilize electromagnetic precursor signatures that yet need to be fully discovered. Both, the role electromagnetic phenomena and the role infrasonic signatures will be scrutinized. The results of successful post-event model reconstructions of viable electromagnetic and infrasonic precursor signatures will be presented.

Accurate thermophysical property data are essential for process design, but experimental databases are often limited, so reliable predictive methods are needed. Conventional equations of state and activity-coefficient (GE) models are effective for correlation but have limited predictive power. This work demonstrates molecular modeling and simulation as an alternative approach for pure fluids and mixtures with strong predictive capability. Molecular models for 78 pure substances (e.g., N₂, O₂, ethane, CO, CO₂, halogens, and alternative refrigerants) are developed using two-center Lennard–Jones models with either a point quadrupole (2CLJQ) or point dipole (2CLJD). A new parametrization strategy based on global correlations of vapor–liquid equilibrium (VLE) for these model fluids enables efficient model development, with required VLE data generated by molecular simulation. The resulting models typically match experiments within about 0.5% for saturated liquid densities, ~4% for vapor pressures, and ~3% for enthalpies of vaporization, outperforming many literature models and remaining accurate in homogeneous states far from VLE. More detailed Lennard–Jones–based models are additionally developed for ethylene oxide and methanol. The compatible pure-fluid models are then applied to 45 binary mixtures, first using purely predictive Lorentz–Berthelot combining rules, which already yield more reliable VLE predictions than the Peng–Robinson equation of state. Quantitative agreement for real mixtures is achieved by introducing a single fitted binary interaction parameter per mixture, determined from one experimental equilibrium pressure. Finally, these binary interaction models are used to predict VLE for five ternary mixtures, reproducing vapor compositions and saturated densities more accurately than Peng–Robinson. Overall, the developed molecular model set provides a broadly applicable and improvable foundation for predictive thermophysical-property modeling.

Today, numerous reliability prediction methods exist for electronic systems. A detailed literature survey is conducted to investigate the various techniques/models to ensure electronic components/system's reliability. Based on such a method's merits and limitations, we recommend using the hybrid model to predict an electronic system's reliability prediction in the distinct development phase of the product life cycle to get higher accuracy.

Estimating 2-year flood flows using the generalized structure of the Group Method of Data Handling

Managing product reliability in business processes ‘under pressure’

One of the main reasons for neonatal deaths is preterm delivery, and infants who have survived preterm birth (PB) are at risk of significant health complications. However, an effective method for reliable and accurate prediction of preterm labor has yet to be proposed. This study proposes an artificial neural network (ANN)-based approach for early prediction of PB, and consequently can hint physicians to start the treatment earlier, reducing the chance of morbidity and mortality in the infant. This historical cohort study proposes a feed-forward ANN with 7 hidden neurons to predict PB. Thirteen risk factors of PB were collected from 300 pregnant women (150 with preterm delivery and 150 normal) as the ANN inputs from 2018 to 2019. From each group, 70%, 15%, and 15% of the subjects were randomly selected for training, validation, and testing of the model, respectively. The ANN achieved an accuracy of 79.03% for the classification of the subjects into two classes normal and PB. Moreover, a sensitivity of 73.45% and specificity of 84.62% were obtained. The advantage of this approach is that the risk factors used for prediction did not require any lab test and were collected in a questionnaire. The efficacy of the proposed approach for the early identification of pregnant women, who are at high risk of preterm delivery, leads to necessary care and clinical interventions, applied during the pregnancy.

Worldwide, medium- to short-term earthquake prediction is becoming ever more essential for safeguarding man due to an un-abating population increase, but hitherto there have been no verifiable methods of reliable earthquake prediction developed - except for a few isolated examples of earthquake prediction in China and in Greece. This dilemma is a result of previous and still current approaches to earthquake prediction which are squarely based on the measurement of crustal movements, observable only after a tectonic stress-change discharge (earthquake) has occurred. The prediction models were derived from past histories of measurements, mainly carried out during the past 30 - 40 years, although initiated soon after the San Francisco Earthquake of 1906. During the past decade it was proved and shown that it is not possible to derive reliable models for earthquake predictions from crustal movement measurements alone, and that an entirely new approach must be taken and rigorously pursued over years and decades to come. In support of this conclusion, there have been reported throughout the history of man anecdotal historical up to scientifically verifiable earthquake precursor or "seismo-genic" signatures of various kind - biological, geological, geo-chemical and especially a rather large plethora of diverse electromagnetic ones on ground, in air and space, denoted as "seismo- electromagnetic" signatures. The existence of all of these signatures can no longer be denied even by the fiercest seismological expert opponents; and it is absolutely high noon that those signatures be more rigorously assessed in order to develop a strategy for designing and carrying out controlled "seismo-genic" and "seismo-electromagnetic" studies on how to set up world-wide a network of measurement sites for conducting a holistic set of measurements for providing an improved understanding on why and how such precursor signatures are generated, and how and where those may best be observed subject to the rather poor signal-to-noise ratio (SNR), requiring much improved digital instrumentation as time goes on due to the ever increasing man-made electromagnetic noise generation. A number of pilot studies had been initiated, had been supported for a few years, and then aborted because of the high operating costs involved, the poor SNR making signal detection tedious if not impossible with the current state of the art in instrumentation, and because earthquakes don't appear upon demand. For example such major studies as the USGS/NSF NEHER Program of the early 1990's after the Loma Prieta M 7 earthquake of 1987; in Japan the ERSFP after the Kobe Earthquake of 1995; in Greece the ongoing electro-potentialmethods of Varatsov; in China, and in various regions as well as independent states of the former Soviet Union. There exists a rather large number of fiercely competing groups in Russia coming up with their own diversified yet highly incomplete modeling approaches seeking support from the West for unfortunately all too low-cost scientific mercenary services. No clear picture has evolved and should not be expected; and a much wider internationally coordinated investigation is required, which may well last for several decades before a unified approach and with it a solution to this vital problem may be found - if ever. In this overview a systematic analysis of main historical records, a summary of pertinent "seimo-genic" as well as observed "seismo- electromagnetic" effects and modern ground-based to air- and space-borne metrological signature investigations are presented. Specifically, remote sensing techniques not yet conceived but in urgent need - such as the remote sensing of the groundwater table - for advancing our understanding of this highly interdisciplinary complicated geophysical problem are being identified, and input is sought from participants for possible active future involvement.

A dose deposition matrix (DDM) prediction method using several voxel features and a machine learning (ML) approach is proposed for plan optimization in radiation therapy. Head and lung cases with the inhomogeneous medium are used as training and testing data. The prediction model is a cascade forward backprop neural network where the input is the features of the voxel, including 1) voxel to body surface distance along the beamlet axis, 2) voxel to beamlet axis distance, 3) voxel density, 4) heterogeneity corrected voxel to body surface distance, 5) heterogeneity corrected voxel to beamlet axis, and (6) the dose of voxel obtained from the pencil beam (PB) algorithm. The output is the predicted voxel dose corresponding to a beamlet. The predicted DDM was used for plan optimization (ML method) and compared with the dose of MC-based plan optimization (MC method) and the dose of pencil beam-based plan optimization (PB method). The mean absolute error (MAE) value was calculated for full volume relative to the dose of the MC method to evaluate the overall dose performance of the final plan. For patient with head tumor, the ML method achieves MAE value 0.49 × 10-4 and PB has MAE 1.86 × 10-4. For patient with lung tumor, the ML method has MAE 1.42 × 10-4 and PB has MAE 3.72 × 10-4. The maximum percentage difference in PTV dose coverage (D98) between ML and MC methods is no more than 1.2% for patient with head tumor, while the difference is larger than 10% using the PB method. For patient with lung tumor, the maximum percentage difference in PTV dose coverage (D98) between ML and MC methods is no more than 2.1%, while the difference is larger than 16% using the PB method. In this work, a reliable DDM prediction method is established for plan optimization by applying several voxel features and the ML approach. The results show that the ML method based on voxel features can obtain plans comparable to the MC method and is better than the PB method in achieving accurate dose to the patient, which is helpful for rapid plan optimization and accurate dose calculation. Establishment of a new machine learning method based on the relationship between the voxel and beamlet features for dose deposition matrix prediction in radiation therapy.

High sound insulation performance of a wall is one of the significant goals of the architecture design. For exploring a reliable sound insulation prediction method to a single-leaf wall, this paper introduces the coincidence effect in the calculation. This method can predict the wall’s sound reduction index easily, and depict the coincidence effect of a wall especially. Meanwhile, it is shown that the prediction method might be applied to the practical building acoustic design.