Pseudo value method for ultra high-dimensional semiparametric models with life-time data

2019 ◽  
Author(s):  
Tony Sit ◽  
Yue Xing ◽  
Yongze Xu ◽  
Minggao Gu
Keyword(s):  
Life Time ◽  
Semiparametric Models ◽  
High Dimensional ◽  
Time Data

Automatic Condition Monitoring and Early Fault Detection of Gearbox

2009 ◽  
Author(s):  
Dong Wang ◽  
Qiang Miao ◽  
Chengdong Wang ◽  
Jingqi Xiong
Keyword(s):  
Condition Monitoring ◽  
Wavelet Packet ◽  
Life Time ◽  
Health Condition ◽  
Evaluation Index ◽  
Dynamic Threshold ◽  
Time Data ◽  
Machinery Condition Monitoring ◽  
Maintenance Program ◽  
Gear Fault

Condition based maintenance (CBM) improves decision-making performances for a maintenance program through machinery condition monitoring. Therefore, it is a key step to trace machinery health condition for CBM. In this paper, a novel method is proposed to establish a health evaluation index named automatic evaluation index (AEI) and its corresponding dynamic threshold using Wavelet Packet Transform (WPT) and Hidden Markolv Model (HMM). In this process, WPT is used to decompose signal into detail signals and exhibits prominent gear fault features. In addition, HMM employed here is to recognize two concerned states of gear in the whole life validation, including normal gear state and early gear fault state. It is also important to build a dynamic threshold to differentiate the two states automatically. The proposed dynamic threshold not only renews by itself according to the history values of AEI but also easily and automatically detects occurrence of gear early fault. Finally, a set of whole life time data ending in gear failure is used to verify the proposed method effectively. Further, some related parameters included in this method are discussed and the obtained results show that condition monitoring performance of the proposed method is excellent in detection of gear failure.

scholarly journals The modified beta transmuted family of distributions with applications using the exponential distribution

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0258512
Author(s):  
Phillip Oluwatobi Awodutire ◽  
Oluwafemi Samson Balogun ◽  
Akintayo Kehinde Olapade ◽  
Ethelbert Chinaka Nduka
Keyword(s):  
Exponential Distribution ◽  
Real Life ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Life Time ◽  
Time Data ◽  
New Family ◽  
Special Cases ◽  
The Family ◽  
Family Of Distributions

In this work, a new family of distributions, which extends the Beta transmuted family, was obtained, called the Modified Beta Transmuted Family of distribution. This derived family has the Beta Family of Distribution and the Transmuted family of distribution as subfamilies. The Modified beta transmuted frechet, modified beta transmuted exponential, modified beta transmuted gompertz and modified beta transmuted lindley were obtained as special cases. The analytical expressions were studied for some statistical properties of the derived family of distribution which includes the moments, moments generating function and order statistics. The estimates of the parameters of the family were obtained using the maximum likelihood estimation method. Using the exponential distribution as a baseline for the family distribution, the resulting distribution (modified beta transmuted exponential distribution) was studied and its properties. The modified beta transmuted exponential distribution was applied to a real life time data to assess its flexibility in which the results shows a better fit when compared to some competitive models.

scholarly journals Tracking and Visualizing Signs of Degradation for Early Failure Prediction of Rolling Bearings

2021 ◽  
Vol 33 (3) ◽  
pp. 629-642
Author(s):  
Sana Talmoudi ◽  
Tetsuya Kanada ◽  
Yasuhisa Hirata ◽  
◽  
Keyword(s):  
Real Time ◽  
Training Data ◽  
High Dimensional ◽  
Time Data ◽  
Early Failure ◽  
Rolling Bearings ◽  
Full Spectrum ◽  
Time Frequency ◽  
Vibration Data ◽  
Visualization Technology

Predictive maintenance, which means detection of failure ahead of time, is one of the pillars of Industry 4.0. An effective method for this technique is to track early signs of degradation before failure occurs. This paper presents an innovative failure predictive scheme for machines. The proposed scheme combines the use of the full spectrum of vibration data from the machines and a data visualization technology. This scheme requires no training data and can be started quickly after installation. First, we proposed to use the full spectrum (as high-dimensional data vectors) with no cropping and no complex feature extraction and to visualize the data behavior by mapping the high-dimensional vectors into a two-dimensional (2D) map. This ensures simplicity of the process and less possibility of overlooking important information as well as provide a human-friendly and human-understandable output. Second, we developed a real-time data tracker that can predict failure at an appropriate time with sufficient allowance for maintenance by plotting real-time frequency spectrum data of the target machine on a 2D map created from normal data. Finally, we verified our proposal using vibration data of bearings from real-world test-to-failure measurements obtained from the IMS dataset.

scholarly journals Noise performance of microwave humidity sounders over their life time

2017 ◽  
Author(s):  
Imke Hans ◽  
Martin Burgdorf ◽  
Viju O. John ◽  
Jonathan Mittaz ◽  
Stefan A. Buehler
Keyword(s):  
Uncertainty Propagation ◽  
Life Time ◽  
Noise Spectrum ◽  
Data Series ◽  
Allan Deviation ◽  
Noise Performance ◽  
Climate Data ◽  
Time Data ◽  
Accessible Information ◽  
Long Time

Abstract. The microwave humidity sounders Special Sensor Microwave Water Vapour Profiler (SSMT-2), Advanced Microwave Sounding Unit-B (AMSU-B) and Microwave Humidity Sounder (MHS) to date have been providing data records for 25 years. So far, the data records lack uncertainty information essential for constructing consistent long time data series. In this study, we assess the quality of the recorded data with respect to the uncertainty caused by noise. We calculate the noise on the raw calibration counts from the deep space view (DSV) of the instrument and the Noise Equivalent Differential Temperature (NEΔT) as a measure for the radiometer sensitivity. For this purpose, we use the Allan Deviation that is not biased from an underlying varying mean of the data and that has been suggested only recently for application in atmospheric remote sensing. Moreover, we use the bias function related to the Allan Deviation to infer the underlying spectrum of the noise. As examples, we investigate the noise spectrum in flight for some instruments. For the assessment of the noise evolution in time, we provide a descriptive and graphical overview of the calculated NEΔT over the life span of each instrument and channel. This overview can serve as an easily accessible information for users interested in the noise performance of a specific instrument, channel and time. Within the time evolution of the noise, we identify periods of instrumental degradation, which manifest themselves in an increasing NEΔT, and periods of erratic behaviour, which show sudden increases of NEΔT interrupting the overall smooth evolution of the noise. From this assessment and subsequent exclusion of the aforementioned periods, we present a chart showing available data records with NEΔT < K. Due to overlapping life spans of the instruments, these reduced data records still cover without gaps the time since 1994 and may therefore serve as first step for constructing long time series. Our method for count noise estimation, that has been used in this study, will be used in the data processing to provide input values for the uncertainty propagation in the generation of a new set of Fundamental Climate Data Records (FCDR) that are currently produced in the project Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO).

scholarly journals The Effects of Notches on the Fatigue Strength of Steel Structures

2019 ◽  
Vol 279 ◽  
pp. 02001
Author(s):  
Pavol Juhas
Keyword(s):  
Fatigue Strength ◽  
Steel Structures ◽  
Life Time ◽  
Fatigue Tests ◽  
Constructional Steel ◽  
Fatigue Properties ◽  
Stress Range ◽  
Time Data ◽  
Butt Weld ◽  
Applied Loading

The paper informs about the research devoted to load–carrying capacity, fatigue strength and life–time of welded steel structures. The experimental programme comprises fatigue tests of constructional steel S380 (QStE 380 TM). In the first stage 35 specimens were tested: 9 without any weld connection, 14 with transverse milled butt weld and 12 with transverse rough butt weld. The applied loading in this stage was harmonic with constant stress range. All tests ended by fatigue failure. The second part of the research comprised the tests with block simulated loading with variable stress range. The third part applied continuous recording of stresses and strains in critical sections, that gave information about the local failure development in time. Data sets have allowed to define fatigue properties of investigated steel and degradation effects of used welds including the initiation time of remarkable changes in stress-strain stage expressed through the total kinetic energy. The degradation effects of welds on fatigue strength of structural steels were confirmed, especially it was the case of rough welds - without additional milling. Additionally, the differences in the fatigue curves inclinations were indicated that can depend on the level of fatigue strength. The applied approach gives an opportunity to analyse the effects of actual loading process and improve the methodology of judgement of fatigue strength and life-time of steel elements. Reasonable fatigue properties of this steel suggest it for using also in severe technology structures.

scholarly journals Group Tests for High-dimensional Failure Time Data with the Additive Hazards Models

2017 ◽  
Vol 13 (1) ◽  
Author(s):  
Dandan Jiang ◽  
Jianguo Sun
Keyword(s):  
Failure Time ◽  
Numerical Study ◽  
Test Procedure ◽  
High Dimensional ◽  
Ratio Test ◽  
Time Data ◽  
Genetic Studies ◽  
Additive Hazards ◽  
Hazards Model ◽  
Or Genes

AbstractStatistical analysis of high-dimensional data has been attracting more and more attention due to the abundance of such data in various fields such as genetic studies or genomics and the existence of many interesting topics. Among them, one is the identification of a gene or genes that have significant effects on the occurrence of or are significantly related to a certain disease. In this paper, we will discuss such a problem that can be formulated as a group test or testing a group of variables or coefficients when one faces right-censored failure time response variable. For the problem, we develop a corrected variance reduced partial profiling (CVRPP) linear regression model and a likelihood ratio test procedure when the failure time of interest follows the additive hazards model. The numerical study suggests that the proposed method works well in practical situations and gives better performance than the existing one. An illustrative example is provided.

scholarly journals Cure fraction models using mixture and non-mixture models

2012 ◽  
Vol 51 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Jorge A. Achcar ◽  
Emílio A. Coelho-Barros ◽  
Josmar Mazucheli
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Mixture Models ◽  
Censored Data ◽  
Life Time ◽  
Time Data ◽  
Data Set ◽  
Cure Fraction ◽  
The Bayesian Approach

ABSTRACT We introduce the Weibull distributions in presence of cure fraction, censored data and covariates. Two models are explored in this paper: mixture and non-mixture models. Inferences for the proposed models are obtained under the Bayesian approach, using standard MCMC (Markov Chain Monte Carlo) methods. An illustration of the proposed methodology is given considering a life- time data set.

Sign in / Sign up

Export Citation Format

Share Document