56 research outputs found
Prognostics of Ball Bearings in Cooling Fans
Ball bearings have been used to support rotating shafts in machines such as wind turbines, aircraft engines, and desktop computer fans. There has been extensive research in the areas of condition monitoring, diagnostics, and prognostics of ball bearings. As the identification of ball bearing defects by inspection interrupts the operation of rotating machines and can be costly, the assessment of the health of ball bearings relies on the use of condition monitoring techniques. Fault detection and life prediction methods have been developed to improve condition-based maintenance and product qualification. However, intermittent and catastrophic system failures due to bearing problems still occur resulting in loss of life and increase of maintenance and warranty costs. Inaccurate life prediction of ball bearings is of concern to industry. This research focuses on prognostics of ball bearings based on vibration and acoustic emission analysis to provide early warning of failure and predict life in advance. The failure mechanisms of ball bearings in cooling fans are identified and failure precursors associated with the defects are determined. A prognostic method based on Bayesian Monte Carlo method and sequential probability ratio test is developed to predict time-to-failure of ball bearings in advance. A benchmark study is presented to demonstrate the application of the developed prognostic method to desktop computer fans. The prognostic method developed in this research can be extended as a general method to predict life of a component or system
Machine learning-based predictive model for prevention of metabolic syndrome
Metabolic syndrome (MetS) is a chronic disease caused by obesity, high blood pressure, high blood sugar, and dyslipidemia and may lead to cardiovascular disease or type 2 diabetes. Therefore, the detection and prevention of MetS at an early stage are imperative. Individuals can detect MetS early and manage it effectively if they can easily monitor their health status in their daily lives. In this study, a predictive model for MetS was developed utilizing solely noninvasive information, thereby facilitating its practical application in real-world scenarios. The model\u27s construction deliberately excluded three features requiring blood testing, specifically those for triglycerides, blood sugar, and HDL cholesterol. We used a large-scale Korean health examination dataset (n = 70, 370; the prevalence of MetS = 13.6%) to develop the predictive model. To obtain informative features, we developed three novel synthetic features from four basic information: waist circumference, systolic and diastolic blood pressure, and gender. We tested several classification algorithms and confirmed that the decision tree model is the most appropriate for the practical prediction of MetS. The proposed model achieved good performance, with an AUC of 0.889, a recall of 0.855, and a specificity of 0.773. It uses only four base features, which results in simplicity and easy interpretability of the model. In addition, we performed calibrations on the prediction probability and calibrated the model. Therefore, the proposed model can provide MetS diagnosis and risk prediction results. We also proposed a MetS risk map such that individuals could easily determine whether they had metabolic syndrome
Optimal Gaussian measurements for phase estimation in single-mode Gaussian metrology
The central issue in quantum parameter estimation is to find out the optimal
measurement setup that leads to the ultimate lower bound of an estimation
error. We address here a question of whether a Gaussian measurement scheme can
achieve the ultimate bound for phase estimation in single-mode Gaussian
metrology that exploits single-mode Gaussian probe states in a Gaussian
environment. We identify three types of optimal Gaussian measurement setups
yielding the maximal Fisher information depending on displacement, squeezing,
and thermalization of the probe state. We show that the homodyne measurement
attains the ultimate bound for both displaced thermal probe states and squeezed
vacuum probe states, whereas for the other single-mode Gaussian probe states,
the optimized Gaussian measurement cannot be the optimal setup, although they
are sometimes nearly optimal. We then demonstrate that the measurement on the
basis of the product quadrature operators XP+PX, i.e., a non-Gaussian
measurement, is required to be fully optimal.Comment: 13 pages, 6 figure
Efficacy of virtual purification-based error mitigation on quantum metrology
Noise is the main source that hinders us from fully exploiting quantum
advantages in various quantum informational tasks. However, characterizing and
calibrating the effect of noise is not always feasible in practice. Especially
for quantum parameter estimation, an estimator constructed without precise
knowledge of noise entails an inevitable bias. Recently, virtual
purification-based error mitigation (VPEM) has been proposed to apply for
quantum metrology to reduce such a bias occurring from unknown noise. While it
was demonstrated to work for particular cases, whether VPEM always reduces a
bias for general estimation schemes is unclear yet. For more general
applications of VPEM to quantum metrology, we study factors determining whether
VPEM can reduce the bias. We find that the closeness between the dominant
eigenvector of a noisy state and the ideal quantum probe (without noise) with
respect to an observable determines the reducible amount of bias by VPEM. Next,
we show that one should carefully choose the reference point of the target
parameter, which gives the smallest bias because the bias depends on the
reference point. Otherwise, even if the dominant eigenvector and the ideal
quantum probe are close, the bias of the mitigated case could be larger than
the non-mitigated one. %We emphasize that the optimal reference point is a
unique feature that comes from the characteristic of quantum metrology.
Finally, we analyze the error mitigation for a phase estimation scheme under
various noises. Based on our analysis, we predict whether VPEM can effectively
reduce a bias and numerically verify our results.Comment: 13 pages, 10 figure
Optical estimation of unitary Gaussian processes without phase reference using Fock states
Since a general Gaussian process is phase-sensitive, a stable phase reference
is required to take advantage of this feature. When the reference is missing,
either due to the volatile nature of the measured sample or the measurement's
technical limitations, the resulting process appears as random in phase. Under
this condition, we consider two single-mode Gaussian processes, displacement
and squeezing. We show that these two can be efficiently estimated using photon
number states and photon number resolving detectors. For separate estimation of
displacement and squeezing, the practical estimation errors for hundreds of
probes' ensembles can saturate the Cram\'{e}r-Rao bound even for arbitrary
small values of the estimated parameters and under realistic losses. The
estimation of displacement with Fock states always outperforms estimation using
Gaussian states with equivalent energy and optimal measurement. For estimation
of squeezing, Fock states outperform Gaussian methods, but only when their
energy is large enough. Finally, we show that Fock states can also be used to
estimate the displacement and the squeezing simultaneously.Comment: 16 pages, 8 figure
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