Two-Zone Proportional Hazard Model for Equipment Remaining Useful Life Prediction

2010 ◽  
Vol 132 (4) ◽  
Author(s):  
Ming-Yi You ◽  
Lin Li ◽  
Guang Meng ◽  
Jun Ni
Keyword(s):  
Life Prediction ◽  
Hazard Model ◽  
Remaining Useful Life ◽  
Proportional Hazard ◽  
Reliability Engineering ◽  
Proportional Hazard Model ◽  
Catastrophic Failures ◽  
Significant Research ◽  
Useful Life ◽  
Stable Zone

Since pioneering work in 1972, the proportional hazard model (PHM) has been widely studied for survival analysis in the area of medicine. Recently, applying the PHM in the area of reliability engineering attracts significant research attentions. In this paper, a two-zone PHM is investigated to predict equipment remaining useful life (RUL) based on the practice that the equipment lifecycle could be divided into two zones: a stable zone and a degradation zone. Results from the numerical experiment illustrate that RUL prediction by applying the proposed two-zone PHM is more accurate and reliable than prediction using the traditional PHM for the entire lifecycle. In practice, this improvement is crucial for real-time maintenance decision making to prevent equipment from catastrophic failures.

Predicting the remaining useful life of a cutting tool during turning titanium metal matrix composites

2016 ◽  
Vol 232 (4) ◽  
pp. 681-689 ◽  
Author(s):  
Yasser Shaban ◽  
Soumaya Yacout
Keyword(s):  
Metal Matrix Composites ◽  
Metal Matrix ◽  
Hazard Model ◽  
Remaining Useful Life ◽  
Matrix Composites ◽  
Proportional Hazard ◽  
Titanium Metal ◽  
Proportional Hazard Model ◽  
Machining Conditions ◽  
Useful Life

A cutting tool’s remaining useful life is what is left for a tool, at a particular working age, in order to reach a pre-specified level of acceptable performance. The prediction of remaining useful life is crucial in order to decrease the scrapped products or the unnecessary interruption of the machining process in order to replace the tool. Consequently, the accuracy of its estimation affects the cost of machining, particularly when the product’s material is very expensive. In this article, the remaining useful lifes of 25 identical tools are estimated during turning titanium metal matrix composites. These composites are extensively used in aerospace and aviation industries. Accurate estimation of the remaining useful life has positive impact on product quality in terms of producing the required specifications. In this article, experimental data are gathered, and the proportional hazard model are used in order to model the tool’s reliability and hazard functions with EXAKT software and then the remaining useful life curves are developed for different machining conditions, namely, the cutting speed and the feed rate. The use of the proportional hazard model is validated using a normalization process and Kolmogorov–Smirnov test. The proportionality assumption is verified using log minus log plot. The final result is the development of the curves that represent the tools’ reliability and the remaining useful life for different machining conditions of the titanium metal matrix composites.

scholarly journals Prognostics uncertainty reduction by right-time prediction of remaining useful life based on hidden Markov model and proportional hazard model

2021 ◽  
Vol 23 (1) ◽  
pp. 154-165
Author(s):  
Gao Zhiyong ◽  
Li Jiwu ◽  
Wang Rongxi
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Survival Function ◽  
Remaining Useful Life ◽  
Proportional Hazard ◽  
Proportional Hazard Model ◽  
Time Prediction ◽  
Useful Life ◽  
Degradation State

Uncertainty is a key problem in remaining useful life (RUL) prediction, and measures to reduce uncertainty are necessary to make RUL prediction truly practical. In this paper, a right-time prediction method is proposed to reduce the prognostics uncertainty of mechanical systems under unobservable degradation. Correspondingly, the whole RUL prediction process is divided into three parts, including offline modelling, online state estimating and online life predicting. In the offline modelling part, hidden Markov model (HMM) and proportional hazard model (PHM) are built to map the whole degradation path. During operation, the degradation state of the object is estimated in real time. Once the last degradation state reached, the degradation characteristics are extracted, and the survival function is obtained with the fitted PHM. The proposed method is demonstrated on an engine dataset and shows higher accuracy than traditional method. By fusing the extracted degradation characteristics, the obtained survival function can be basis for optimal maintenance with lower uncertainty.

Dynamic Modeling and Wear-Based Remaining Useful Life Prediction of High Power Clutch Systems

2005 ◽  
Vol 48 (2) ◽  
pp. 208-217 ◽  
Author(s):  
Matthew Watson ◽  
Carl Byington ◽  
Douglas Edwards ◽  
Sanket Amin
Keyword(s):  
Dynamic Modeling ◽  
High Power ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Useful Life

scholarly journals A Study of Factors Affecting the Length of Hospital Stay of COVID-19 Patients by Cox-Proportional Hazard Model in a South Indian Tertiary Care Hospital

2021 ◽  
Vol 12 ◽  
pp. 215013272110002
Author(s):  
Gayathri Thiruvengadam ◽  
Marappa Lakshmi ◽  
Ravanan Ramanujam
Keyword(s):  
Hospital Stay ◽  
Tertiary Care ◽  
Length Of Hospital Stay ◽  
Hazard Model ◽  
Cox Proportional Hazard Model ◽  
Care Hospital ◽  
Proportional Hazard ◽  
Proportional Hazard Model ◽  
Cox Proportional Hazard ◽  
South Indian

Background: The objective of the study was to identify the factors that alter the length of hospital stay of COVID-19 patients so we have an estimate of the duration of hospitalization of patients. To achieve this, we used a time to event analysis to arrive at factors that could alter the length of hospital stay, aiding in planning additional beds for any future rise in cases. Methods: Information about COVID-19 patients was collected between June and August 2020. The response variable was the time from admission to discharge of patients. Cox proportional hazard model was used to identify the factors that were associated with the length of hospital stay. Results: A total of 730 COVID-19 patients were included, of which 675 (92.5%) recovered and 55 (7.5%) were considered to be right-censored, that is, the patient died or was discharged against medical advice. The median length of hospital stay of COVID-19 patients who were hospitalized was found to be 7 days by the Kaplan Meier curve. The covariates that prolonged the length of hospital stay were found to be abnormalities in oxygen saturation (HR = 0.446, P < .001), neutrophil-lymphocyte ratio (HR = 0.742, P = .003), levels of D-dimer (HR = 0.60, P = .002), lactate dehydrogenase (HR = 0.717, P = .002), and ferritin (HR = 0.763, P = .037). Also, patients who had more than 2 chronic diseases had a significantly longer length of stay (HR = 0.586, P = .008) compared to those with no comorbidities. Conclusion: Factors that are associated with prolonged length of hospital stay of patients need to be considered in planning bed strength on a contingency basis.

scholarly journals A Fusion Prognostic Method for Remaining Useful Life Prediction Based on an Extended Belief Rule Base and Particle Filters

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Bincheng Wen ◽  
Mingqing Xiao ◽  
Guanghao Wang ◽  
Zhao Yang ◽  
Jianfeng Li ◽  
...  
Keyword(s):  
Life Prediction ◽  
Particle Filters ◽  
Remaining Useful Life ◽  
Rule Base ◽  
Belief Rule Base ◽  
Useful Life
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