A Predictive Tool for Remaining Useful Life Estimation of Rotating Machinery Components

2005 ◽  
Author(s):  
Haitao Liao ◽  
Hai Qiu ◽  
Jay Lee ◽  
Daming Lin ◽  
Dragan Banjevic ◽  
...  
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Remaining Useful Life ◽  
Model Parameters ◽  
Predictive Tool ◽  
Hazards Model ◽  
On Line ◽  
Likelihood Approach ◽  
Useful Life ◽  
The Mean

This paper introduces a model for multiple degradation features of an individual component. The maximum likelihood approach is employed to estimate the model parameters. Afterwards, a proportional hazards model is presented, which considers hard failures and multiple degradation features simultaneously. The integrated model enables us to predict the mean remaining useful life of a component based on on-line degradation information. An example for bearing prognostic is provided to demonstrate the proposed models in practical use.

scholarly journals An Inference-based Prognostic Framework for Health Management of Automotive Systems

2020 ◽  
Vol 7 (2) ◽  
Author(s):  
Chaitanya Sankavaram ◽  
Anuradha Kodali ◽  
Krishna Pattipati ◽  
Satnam Singh ◽  
Yilu Zhang ◽  
...  
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Failure Time ◽  
Remaining Useful Life ◽  
Survival Functions ◽  
Time Data ◽  
Automotive Systems ◽  
Multiple Fault Diagnosis ◽  
Hazards Model ◽  
Useful Life

This paper presents a unified data-driven prognostic framework that combines failure time data, static parameter data and dynamic time-series data. The framework employs proportional hazards model and a soft dynamic multiple fault diagnosis algorithm for inferring the degraded state trajectories of components and to estimate their remaining useful life times. The framework takes into account the cross-subsystem fault propagation, a case prevalent in any networked and embedded system. The key idea is to use Cox proportional hazards model to estimate the survival functions of error codes and symptoms (probabilistic test outcomes/prognostic indicators) from failure time data and static parameter data, and use them to infer the survival functions of components via soft dynamic multiple fault diagnosis algorithm. The average remaining useful life and its higher-order central moments (e.g., variance, skewness, kurtosis) can be estimated from these component survival functions. The framework is demonstrated on datasets derived from two automotive systems, namely hybrid electric vehicle regenerative braking system, and an electronic throttle control subsystem simulator. Although the proposed framework is validated on automotive systems, it has the potential to be applicable to a wide variety of systems, ranging from aerospace systems to buildings to power grids.

Remaining useful life prediction of rolling bearings based on the three-parameter Weibull distribution proportional hazards model

2020 ◽  
Vol 62 (12) ◽  
pp. 710-718
Author(s):  
Ye Wang ◽  
Zhixiong Chen ◽  
Yang Zhang ◽  
Xin Li ◽  
Zhixiong Li
Keyword(s):  
Weibull Distribution ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Remaining Useful Life ◽  
Rolling Bearings ◽  
Time Frequency ◽  
Intelligent Maintenance ◽  
Hazards Model ◽  
Useful Life ◽  
Essential Knowledge

In order to accurately predict the remaining useful life (RUL) of rolling bearings, a novel method based on the threeparameter Weibull distribution proportional hazards model (WPHM) is proposed in this paper. In this new method, degradation features of the bearing vibration signals were calculated in the time, frequency and time-frequency domains and treated as the input covariates of the predictive WPHM. Essential knowledge of the bearing degradation dynamics was learnt from the input features to build an effective three-parameter WPHM for bearing RUL prediction. Experimental data acquired from the run-to-failure bearing tests of the intelligent maintenance system (IMS) was used to evaluate the proposed method. The analysis results demonstrate that the proposed model is able to produce accurate RUL prediction for the tested bearings and outperforms the popular two-parameter WPHM.

Remaining useful life prediction of lubricating oil with dynamic principal component analysis and proportional hazards model

2019 ◽  
Vol 234 (6) ◽  
pp. 964-971
Author(s):  
Ying Du ◽  
Tonghai Wu ◽  
Shengxi Zhou ◽  
Viliam Makis
Keyword(s):  
Condition Monitoring ◽  
Life Prediction ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Principal Component ◽  
Remaining Useful Life ◽  
Lubricating Oil ◽  
Hazards Model ◽  
Dynamic Principal Component Analysis ◽  
Useful Life

Lubricating oil contains a lot of tribological information of the machine and plays an important role in machine health. Oil degrades with serving time and causes severe wear afterwards, which is a complex dynamic process, and difficult to be accurately described by a single property. Therefore, the main purpose of deterioration prediction is to estimate the remaining useful life that the oil can still fulfill its functions by analyzing oil condition monitoring data. With a large amount of oil condition monitoring data collected, a vector autoregressive model is applied to the original oil data to describe the dynamic deterioration process. Then dynamic principal component analysis, an effective dimensionality reduction method, is employed to obtain the principal components capturing the most information of the oil data. The proportional hazards model is then built to calculate the failure risk of the lubricating oil based on the condition monitoring information, where its baseline function represents the aging process assuming to follow the Weibull distribution and its positive link function represents the influence of covariates (the principal components) on the failure risk. Finally, the remaining useful life prediction of lubricating oil can be obtained by explicit formulas of the characteristics such as the conditional reliability function and the mean residual life function. This work provides an approach to assess the health of lubricating oil, and a guidance for oil maintenance strategy.

HRA Model for Hypercholesterolemia Based on a Longitudinal Health Database

1998 ◽  
Vol 37 (02) ◽  
pp. 130-133
Author(s):  
T. Kishimoto ◽  
Y. Iida ◽  
K. Yoshida ◽  
M. Miyakawa ◽  
H. Sugimori ◽  
...  
Keyword(s):  
Total Cholesterol ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Model Analysis ◽  
Serum Total Cholesterol ◽  
Hazards Model ◽  
The Mean ◽  
Health Database ◽  
Observation Period

AbstractTo evaluate the risk factors for hypercholesterolemia, we examined 4,371 subjects (3,207 males and 1,164 females) who received medical checkups more than twice at an AMHTS in Tokyo during the period from 1976 through 1991; and whose serum total cholesterol was under 250 mg/dl. The mean follow-up duration was 6.6 years. A self-registering questionnaire was administered at the time of the health checkup. The endpoint of this study was the onset of hypercholesterolemia when the level of serum total cholesterol was 250 mg/dl and over. We compared two prognosis groups (normal and hypercholesterol) in terms of age, examination findings and lifestyle. After assessing each variable, we employed Cox's proportional hazards model analysis to determine the factors related to the occurrence of hypercholesterolemia. According to proportional hazards model analysis, total cholesterol, triglyceride and smoking at the beginning, and hypertension during the observation period were selected in males; and total cholesterol at the beginning and age were selected in females to determine the factors related to the occurrence of hypercholesterolemia.

Subdistribution hazard models for competing risks in discrete time

Biostatistics ◽  
2018 ◽  
Vol 21 (3) ◽  
pp. 449-466 ◽  
Author(s):  
Moritz Berger ◽  
Matthias Schmid ◽  
Thomas Welchowski ◽  
Steffen Schmitz-Valckenberg ◽  
Jan Beyersmann
Keyword(s):  
Competing Risks ◽  
Discrete Time ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Model Parameters ◽  
Subdistribution Hazard ◽  
Ml Estimation ◽  
Modeling Approach ◽  
Hazards Model ◽  
Event Times

Summary A popular modeling approach for competing risks analysis in longitudinal studies is the proportional subdistribution hazards model by Fine and Gray (1999. A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association94, 496–509). This model is widely used for the analysis of continuous event times in clinical and epidemiological studies. However, it does not apply when event times are measured on a discrete time scale, which is a likely scenario when events occur between pairs of consecutive points in time (e.g., between two follow-up visits of an epidemiological study) and when the exact lengths of the continuous time spans are not known. To adapt the Fine and Gray approach to this situation, we propose a technique for modeling subdistribution hazards in discrete time. Our method, which results in consistent and asymptotically normal estimators of the model parameters, is based on a weighted ML estimation scheme for binary regression. We illustrate the modeling approach by an analysis of nosocomial pneumonia in patients treated in hospitals.

Predicting Compressor of Gas Turbine Power Plant On-Line Washing Interval Using Proportional Hazards Model

2012 ◽  
Vol 452-453 ◽  
pp. 195-199 ◽  
Author(s):  
Lei Zhu ◽  
Hong Fu Zuo
Keyword(s):  
Gas Turbine ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Prediction Method ◽  
Turbine Efficiency ◽  
Hazards Model ◽  
Aero Engine ◽  
On Line ◽  
Gas Turbine Power Plant ◽  
Over Time

Due to compressor fouling, gas turbine efficiency decreases over time, resulting in decreased power output of the plant. To counteract the effects of compressor fouling, compressor on-line and off-line washing procedures are used. The present research is aimed to propose a method of mathematical modeling of offline washing interval which will be estimated as the RUL of compressor based on Proportional hazards model. Application of the proposed prediction method to the case of Civil Aero-engine proved its effectiveness.

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