A Hybrid Approach for Predicting the Remaining Useful Life of Bearings Based on the RReliefF Algorithm and Extreme Learning Machine

Accurately predicting the remaining useful life(RUL)of bearings in mining rotating equipment is vital for mining enterprises.This research aims to distinguish the features associated with the RUL of bearings and propose a prediction model based on these selected features.This study proposes a hybrid predictive model to assess the RUL of rolling element bearings.The proposed model begins with the pre-processing of bearing vibration signals to reconstruct sixty time-domain features.The hybrid model selects relevant features from the sixty time-domain features of the vibration signal by adopting the RReliefF feature selection algorithm.Subsequently,the extreme learning machine(ELM)approach is applied to develop a predictive model of RUL based on the optimal features.The model is trained by optimizing its parameters via the grid search approach.The training datasets are adjusted to make them most suitable for the regression model using the cross-validation method.The proposed hybrid model is analyzed and validated using the vibration data taken from the public XJTU-SY rolling element-bearing database.The comparison is constructed with other traditional models.The experimental test results demonstrated that the proposed approach can predict the RUL of bearings with a reliable degree of accuracy.

A Risk-Averse Remaining Useful Life Estimation for Predictive Maintenance

Remaining useful life(RUL)prediction is an advanced technique for system maintenance scheduling.Most of existing RUL prediction methods are only interested in the precision of RUL estimation;the adverse impact of overestimated RUL on maintenance scheduling is not of concern.In this work,an RUL estimation method with risk-averse adaptation is developed which can reduce the over-estimation rate while maintaining a reasonable under-estimation level.The proposed method includes a module of degradation feature selection to obtain crucial features which reflect system degradation trends.Then,the latent structure between the degradation features and the RUL labels is modeled by a support vector regression(SVR)model and a long short-term memory(LSTM)network,respectively.To enhance the prediction robustness and increase its marginal utility,the SVR model and the LSTM model are integrated to generate a hybrid model via three connection parameters.By designing a cost function with penalty mechanism,the three parameters are determined using a modified grey wolf optimization algorithm.In addition,a cost metric is proposed to measure the benefit of such a risk-averse predictive maintenance method.Verification is done using an aero-engine data set from NASA.The results show the feasibility and effectiveness of the proposed RUL estimation method and the predictive maintenance strategy.

Modeling visitor use on high elevation mountain trails: An example from Longs Peak in Rocky Mountain National Park, USA

Increasing use on mountain summits has both social and ecological implications.High numbers of visitors climbing mountain summits can be a safety issue,particularly in areas where terrain or elevation leads to queueing that may cause time delays.Estimating visitor use levels at site specific locations en route to summits is needed to understand the potential benefits and impacts of visitor use in these locations.However,it can be difficult to obtain reliable and robust data to estimate use and develop statistical relationships because of the remote and harsh climates on mountain summits,as well as the financial and personnel requirements involved to collect the data in remote locations.In 2015,data were collected on the higher stretches of the Keyhole Route on Longs Peak in Rocky Mountain National Park,USA(RMNP)to better understand use levels near the summit and to explore potential statistical relationships to trailhead use data that are relatively easy to collect.Strong statistical relationships from robust regression analyses were found between trailhead use counts and daily and hourly use totals on the"Homestretch"which is a final section of the Keyhole Route.Additionally,a strong statistical relationship was found between total daily use and maximum hourly use on the Homestretch.The results suggest that trailhead counts are an accurate and reliable means from which to estimate use levels on upper portions of the Keyhole route.Moreover,this research demonstrates the usefulness of an approach using proxy variables to estimate visitor use along remote peaks where data collection can be difficult.These types of data can elucidate various options and decisions for park management teams who are charged with deciding if and how to manage high use areas.

Sequential Parameter Estimation Using Modal Dispersion Curves in Shallow Water

Existing sequential parameter estimation methods use the acoustic pressure of a line array as observations. The modal dispersion curves are employed to estimate the sound speed profile（SSP） and geoacoustic parameters based on the ensemble Kalman filter. The warping transform is implemented to the signals received by a single hydrophone to obtain the dispersion curves. The experimental data are collected at a range-independent shallow water site in the South China Sea. The results indicate that the SSPs are well estimated and the geoacoustic parameters are also well determined. Comparisons of the observed and estimated modal dispersion curves show good agreement.

CVIP-Net: A Convolutional Neural Network-Based Model for Forensic Radiology Image Classification

Automated and autonomous decisions of image classification systems have essential applicability in this modern age even.Image-based decisions are commonly taken through explicit or auto-feature engineering of images.In forensic radiology,auto decisions based on images significantly affect the automation of various tasks.This study aims to assist forensic radiology in its biological profile estimation when only bones are left.A benchmarked dataset Radiology Society of North America(RSNA)has been used for research and experiments.Additionally,a locally developed dataset has also been used for research and experiments to cross-validate the results.A Convolutional Neural Network(CNN)-based model named computer vision and image processing-net(CVIP-Net)has been proposed to learn and classify image features.Experiments have also been performed on state-of-the-art pertained models,which are alex_net,inceptionv_3,google_net,Residual Network(resnet)_50,and Visual Geometry Group(VGG)-19.Experiments proved that the proposed CNN model is more accurate than other models when panoramic dental x-ray images are used to identify age and gender.The specially designed CNN-based achieved results in terms of standard evaluation measures including accuracy(98.90%),specificity(97.99%),sensitivity(99.34%),and Area under the Curve(AUC)-value(0.99)on the locally developed dataset to detect age.The classification rates of the proposed model for gender estimation were 99.57%,97.67%,98.99%,and 0.98,achieved in terms of accuracy,specificity,sensitivity,and AUC-value,respectively,on the local dataset.The classification rates of the proposed model for age estimation were 96.80%,96.80%,97.03%,and 0.99 achieved in terms of accuracy,specificity,sensitivity,and AUC-value,respectively,on the RSNA dataset.

Satellite lithium-ion battery remaining useful life estimation with an iterative updated RVM fused with the KF algorithm

Lithium-ion batteries have become the third-generation space batteries and are widely utilized in a series of spacecraft. Remaining Useful Life （RUL） estimation is essential to a spacecraft as the battery is a critical part and determines the lifetime and reliability. The Relevance Vector Machine （RVM） is a data-driven algorithm used to estimate a battery＇s RUL due to its sparse feature and uncertainty management capability. Especially, some of the regressive cases indicate that the RVM can obtain a better short-term prediction performance rather than long-term prediction. As a nonlinear kernel learning algorithm, the coefficient matrix and relevance vectors are fixed once the RVM training is conducted. Moreover, the RVM can be simply influenced by the noise with the training data. Thus, this work proposes an iterative updated approach to improve the long-term prediction performance for a battery＇s RUL prediction. Firstly, when a new estimator is output by the RVM, the Kalman filter is applied to optimize this estimator with a physical degradation model. Then, this optimized estimator is added into the training set as an on-line sample, the RVM model is re-trained, and the coefficient matrix and relevance vectors can be dynamically adjusted to make next iterative prediction. Experimental results with a commercial battery test data set and a satellite battery data set both indicate that the proposed method can achieve a better performance for RUL estimation.

ON BAHADUR－TYPE ASYMPTOTIC EFFICIENCY OF POINT ESTIMATORS UNDER IRREGULAR TRUNCATED DISTRIBUTION FAM

In this paper, the optimal convergence rates of point estimators have been found under the irregular truncated distribution family, and corresponding Bahadurtype asymptotic efficiencies have been established. It has beed justified that commonly used estimators are all efficient in this sense.

Independently recurrent neural network for remaining useful life estimation

In the industrial fields, the mechanical equipment will inevitably wear out in the process of operation. With the accumulation of losses, the probability of equipment failure is increasing. Therefore, if the remaining useful life(RUL) of the equipment can be accurately predicted, the equipment can be maintained in time to avoid the downtime caused by equipment failure and greatly improve the production efficiency of enterprises. This paper aims to use independently recurrent neural network(IndRNN) to learn health degradation of turbofan engine and make accurate predictions of its RUL, which not only effectively solves the problem of gradient explosion and vanishing, but also increases the interpretability of neural networks. IndRNN can be used to process longer time series which matches the scene with high frequency sampling sensor in industrial practical applications. The results demonstrate that IndRNN for RUL estimation significantly outperforms traditional approaches, as well as convolutional neural network(CNN) and long short-term memory network(LSTM) for RUL estimation.

Using an Alternative Method to Estimate the Status of Iodine Nutrition in Pregnant Women

Iodine is an element that is essential for the synthesis of thyroid hormones.Adequate intake of dietary iodine has been recognized as a critical factor for maintaining health.It is a well-known fact that iodine deficiency can impede the production of thyroid hormones in both the mother and fetus,which increases the risk of brain damage in the fetal stage.

Three-dimensional color photon counting microscopy using Bayesian estimation with adaptive priori information

In this Letter, we propose a novel three-dimeusional （3D） color microscopy for microorganisms under photon- starved conditions using photon counting integral imaging and Bayesian estimation with adaptive priori infor- mation. In photon counting integral imaging, 3D images can be visualized using maximum likelihood estimation （MLE）. However, since MLE does not consider a priori information of objects, the visual quality of 3D images may not be accurate. In addition, the only grayscale image can be reconstructed. Therefore, to enhance the visual quality of 3D images, we propose photon counting microscopy using maximum a posteriori with adaptive priori information. In addition, we consider a wavelength of each basic color channel to reconstruct 3D color images. To verify our proposed method, we carry out optical experiments.

ESTIMATION OF VERTICAL DISTRIBUTION OF RELATIVE HUMIDITY USING SATELLITE DATA

In this paper, an attempt has been made to find out the vertical distribution of RH at levels of 850, 700 and 500 hPa by using satellite-derived radiation parameters (i.e., albedo, outgoing longwave fluxes, absorb- ed solar radiation and net radiation). For this purpose, multiple regression equations are derived from MONEX-79 upsonde and dropsonde data over the Arabian Sea for the period 11--20 June 1979. Satellite- estimated RH fields have been compared with ECMWF RH fields obtained from FGGE level ⅢB data. The RMS error and error variance for satellite-estimated RH fields have been found to be less than for those of ECMWF. Satellite-estimated isohygric patterns show good agreement with the cloudiness patterns of GOES satellite, whereas ECMWF isohygric patterns do not show much resemblance with the cloudiness patterns. The results of the study suggest that satellite-estimated RH fields could be more useful than ECMWF RH fields and they can be used with some confidence in NWP models.

Estimation and feedback control of air-fuel ratio for gasoline engines

In 4-stroke internal combustion engines, air-fuel ratio control is a challenging task due to the rapid changes of engine throttle,especially during transient operation. To improve the transient performance, managing the cycle-to-cycle transient behavior of the mass of the air, the fuel and the burnt gas is a key issue due to the imbalance of cyclic combustion process. This paper address the model-based estimation and control problem for cyclic air-fuel ratio of spark-ignition engines. A discrete-time model of air-fuel ratio is proposed, which represents the cycle-to-cycle transient behavior of in-cylinder state variables under the assumptions of cyclic measurability of the total in-cylinder charge mass, combustion efficiency and the residual gas fraction. With the model,a Kalman filter-based air-fuel ratio estimation algorithm is proposed that enable us to perform a feedback control of air-fuel ratio without using lambda sensor. Finally, experimental validation result is demonstrated to show the effectiveness of proposed estimation and control scheme that is conducted on a full-scaled gasoline engine test bench.

An analytical method for quantitative estimation of the cytotoxicity of dental alloys using MTT assay,ICP analysis and effective cytotoxicity calculation

The two most important criteria for dental materials are their biofunctional and biocompatible endurance within the anticipated life-span of the dental restoration in the mouth. Biocompatibility relates mainly to the allergenicity and the toxicity of the material. To test the non-specific toxicity of dental materials, in vitro cell culture assays have been developed. For in vitro screening, such tests are recommended to check the cytotoxicity of dental materials (ISO 10993 5). Various studies have already been performed to quantitatively determine the cytotoxicity level of dental alloys. However, as long as only dental alloys and the cell culture technique are applied, it is not possible to determine which of the alloying elements cause the cytotoxicity. Therefore, an analytical method is needed. Wataha et al determined in 1991 the TC50 values of 9 metal cations of various dental casting alloys, using cell culture methods. Kapert et al reported in 1994 a complex in vitro test concept, where the ICP analysis (inductively coupled plasma emission spectroscopy) was introduced to measure the trace elements extracted from various alloys. Experimentelle Zahnheilkunde, Universitts ZMK Klinik Freiburg, Germany (Lü XY and Kappert HF) The aim of the present study was to find a relation between the ICP results, the TC50 value of metal cations, and the cytotoxicity of dental alloys. The cytotoxicity levels of various dental alloys and the TC50 values of 10 metal cations were established using the MTT assay, an effective cell culture of method. Then, the concentrations of the corrosively soluted metal cations in the extracts of the alloys were measured using the ICP method. From all these experimental results it was found that the relation between the effective cytotoxicity Z eff of an alloy, the concentrations C i of i th trace element and the TC50 values T Ci of the i th metal cation can approximately be expressed by Z eff =∑iC i2·T Ci . Two significant applications of this expression are a) The cytotoxicity of an alloy can be estimated by ICP analysis of the extract if the TC50 values of the trace elements are know. b) The cytotoxicity of a new-developed-alloy can be estimated in advance, according to the alloying components.

Perioperative Evaluation of Cerebral Blood Flow Using ^123I-labeled N-isopropyl-p-iodoamphetamine Single-Photon Emission Computed Tomography without Blood Sampling in Patients Who Underwent Carotid Artery Stenting

The evaluation of regional cerebral vascular reserve （rCVR） with single-photon emission computed tomography （SPECT） is useful for predicting cerebral hyperperfusion following carotid artery stenting （CAS） and carotidendarterectomy （CEA）.