scholarly journals A Maintenance Cost Study of Transformers Based on Markov Model Utilizing Frequency of Transition Approach

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2006 ◽  
Author(s):  
Muhammad Yahaya ◽  
Norhafiz Azis ◽  
Amran Mohd Selva ◽  
Mohd Ab Kadir ◽  
Jasronita Jasni ◽  
...  
Keyword(s):  
Markov Model ◽  
Transition Probabilities ◽  
Prediction Interval ◽  
Transition States ◽  
Maintenance Cost ◽  
Maintenance Policy ◽  
Cost Study ◽  
Policy Model ◽  
Transition Approach ◽  
Year 2013

In this paper, a maintenance cost study of transformers based on the Markov Model (MM) utilizing the Health Index (HI) is presented. In total, 120 distribution transformers of oil type (33/11 kV and 30 MVA) are examined. The HI is computed based on condition assessment data. Based on the HI, the transformers are arranged according to its corresponding states, and the transition probabilities are determined based on frequency of a transition approach utilizing the transformer transition states for the year 2013/2014 and 2012/2013. The future states of transformers are determined based on the MM chain algorithm. Finally, the maintenance costs are estimated based on future-state distribution probabilities according to the proposed maintenance policy model. The study shows that the deterioration states of the transformer population for the year 2015 can be predicted by MM based on the transformer transition states for the year 2013/2014 and 2012/2013. Analysis on the relationship between the predicted and actual computed numbers of transformers reveals that all transformer states are still within the 95% prediction interval. There is a 90% probability that the transformer population will reach State 1 after 76 years and 69 years based on the transformer transition states for the year 2013/2014 and 2012/2013. Based on the probability-state distributions, it is found that the total maintenance cost increases gradually from Ringgit Malaysia (RM) 5.94 million to RM 39.09 million based on transformer transition states for the year 2013/2014 and RM 37.56 million for the year 2012/2013 within the 20 years prediction interval, respectively.

scholarly journals Optimal Cost-Effective Maintenance Policy for a Helicopter Gearbox Early Fault Detection under Varying Load

2017 ◽  
Vol 2017 ◽  
pp. 1-16
Author(s):  
Xin Li ◽  
Jing Cai ◽  
Hongfu Zuo ◽  
Huaiyuan Li
Keyword(s):  
Fault Detection ◽  
Markov Model ◽  
Remaining Useful Life ◽  
Replacement Policy ◽  
Detection Methods ◽  
Maintenance Cost ◽  
Maintenance Policy ◽  
Bayesian Control ◽  
Early Fault Detection ◽  
Optimal Maintenance Policy

Most of the existing fault detection methods rarely consider the cost-optimal maintenance policy. A novel multivariate Bayesian control approach is proposed, which enables the implementation of early fault detection for a helicopter gearbox with cost minimization maintenance policy under varying load. A continuous time hidden semi-Markov model (HSMM) is employed to describe the stochastic relationship between the unobservable states and observable observations of the gear system. Explicit expressions for the remaining useful life prediction are derived using HSMM. Considering the maintenance cost in fault detection, the multivariate Bayesian control scheme based on HSMM is developed; the objective is to minimize the long-run expected average cost per unit time. An effective computational algorithm in the semi-Markov decision process (SMDP) framework is designed to obtain the optimal control limit. A comparison with the multivariate Bayesian control chart based on hidden Markov model (HMM) and the traditional age-based replacement policy is given, which illustrates the effectiveness of the proposed approach.

Availability analysis and maintenance optimization for multiple failure mode systems considering imperfect repair

2021 ◽  
pp. 1748006X2110127
Author(s):  
Qingan Qiu ◽  
Baoliang Liu ◽  
Cong Lin ◽  
Jingjing Wang
Keyword(s):  
Failure Modes ◽  
Maintenance Cost ◽  
Imperfect Maintenance ◽  
Maintenance Policy ◽  
Cost Rate ◽  
Availability Analysis ◽  
Long Run ◽  
Optimal Maintenance ◽  
Maintenance Policies ◽  
Periodic Inspections

This paper studies the availability and optimal maintenance policies for systems subject to competing failure modes under continuous and periodic inspections. The repair time distribution and maintenance cost are both dependent on the failure modes. We investigate the instantaneous availability and the steady state availability of the system maintained through several imperfect repairs before a replacement is allowed. Analytical expressions for system availability under continuous and periodic inspections are derived respectively. The availability models are then utilized to obtain the optimal inspection and imperfect maintenance policy that minimizes the average long-run cost rate. A numerical example for Remote Power Feeding System is presented to demonstrate the application of the developed approach.

scholarly journals States Transitions Inference of Postpartum Depression Based on Multi-State Markov Model

2021 ◽  
Vol 18 (14) ◽  
pp. 7449
Author(s):  
Juan Xiong ◽  
Qiyu Fang ◽  
Jialing Chen ◽  
Yingxin Li ◽  
Huiyi Li ◽  
...  
Keyword(s):  
Postpartum Depression ◽  
Markov Model ◽  
Normal State ◽  
Sojourn Time ◽  
Transition Probabilities ◽  
Transition Probability ◽  
Public Health Problem ◽  
Mental States ◽  
Hazard Ratio ◽  
Multiple Time

Background: Postpartum depression (PPD) has been recognized as a severe public health problem worldwide due to its high incidence and the detrimental consequences not only for the mother but for the infant and the family. However, the pattern of natural transition trajectories of PPD has rarely been explored. Methods: In this research, a quantitative longitudinal study was conducted to explore the PPD progression process, providing information on the transition probability, hazard ratio, and the mean sojourn time in the three postnatal mental states, namely normal state, mild PPD, and severe PPD. The multi-state Markov model was built based on 912 depression status assessments in 304 Chinese primiparous women over multiple time points of six weeks postpartum, three months postpartum, and six months postpartum. Results: Among the 608 PPD status transitions from one visit to the next visit, 6.2% (38/608) showed deterioration of mental status from the level at the previous visit; while 40.0% (243/608) showed improvement at the next visit. A subject in normal state who does transition then has a probability of 49.8% of worsening to mild PPD, and 50.2% to severe PPD. A subject with mild PPD who does transition has a 20.0% chance of worsening to severe PPD. A subject with severe PPD is more likely to improve to mild PPD than developing to the normal state. On average, the sojourn time in the normal state, mild PPD, and severe PPD was 64.12, 6.29, and 9.37 weeks, respectively. Women in normal state had 6.0%, 8.5%, 8.7%, and 8.8% chances of progress to severe PPD within three months, nine months, one year, and three years, respectively. Increased all kinds of supports were associated with decreased risk of deterioration from normal state to severe PPD (hazard ratio, HR: 0.42–0.65); and increased informational supports, evaluation of support, and maternal age were associated with alleviation from severe PPD to normal state (HR: 1.46–2.27). Conclusions: The PPD state transition probabilities caused more attention and awareness about the regular PPD screening for postnatal women and the timely intervention for women with mild or severe PPD. The preventive actions on PPD should be conducted at the early stages, and three yearly; at least one yearly screening is strongly recommended. Emotional support, material support, informational support, and evaluation of support had significant positive associations with the prevention of PPD progression transitions. The derived transition probabilities and sojourn time can serve as an importance reference for health professionals to make proactive plans and target interventions for PPD.

scholarly journals Forecasting the Long-Run Behavior of the Stock Price of Some Selected Companies in the Malaysian Construction Sector: A Markov Chain Approach

2020 ◽  
Vol 5 (2) ◽  
pp. 296-308
Author(s):  
Wajeeh Mustafa Sarsour ◽  
Shamsul Rijal Muhammad Sabri
Keyword(s):  
Markov Model ◽  
Stock Prices ◽  
Stock Price ◽  
Transition Probabilities ◽  
Return Time ◽  
Price Increase ◽  
State Model ◽  
Construction Sector ◽  
Long Run ◽  
Three State Model

The fluctuations in stock prices produce a high risk that makes investors uncertain about their investment decisions. The present paper provides a methodology to forecast the long-term behavior of five randomly selected equities operating in the Malaysian construction sector. The method used in this study involves Markov chains as a stochastic analysis, assuming that the price changes have the proparty of Markov dependency with their transition probabilities. We identified a three-state Markov model (i.e., increase, stable, fall) and a two-state Markov model (i.e., increase and fall). The findings suggested that the chains had limiting distributions. The mean return time was computed for respective equities as well as to determine the average duration to return to a stock price increase. The analysis might aid investors in improving their investment knowledge, and they will be able to make better decisions when an equity portfolio possesses higher transition probabilities, higher limiting distribution, and lowest mean return time in response to a price increase. Finally, our investigations suggest that investors are more likely to invest in the GKent based on the three-state model, while VIZIONE seems to be a better investment choice based on a two-state model.

A semi-markov model for clinical trials

1965 ◽  
Vol 2 (02) ◽  
pp. 269-285 ◽  
Author(s):  
George H. Weiss ◽  
Marvin Zelen
Keyword(s):  
Clinical Trials ◽  
Probability Distribution ◽  
Acute Leukemia ◽  
Markov Model ◽  
Markov Models ◽  
Transition Probabilities ◽  
State Transitions ◽  
Semi Markov Processes ◽  
Absorbing States ◽  
Epidemic Diseases

This paper applies the theory of semi-Markov processes to the construction of a stochastic model for interpreting data obtained from clinical trials. The model characterizes the patient as being in one of a finite number of states at any given time with an arbitrary probability distribution to describe the length of stay in a state. Transitions between states are assumed to be chosen according to a stationary finite Markov chain.Other attempts have been made to develop stochastic models of clinical trials. However, these have all been essentially Markovian with constant transition probabilities which implies that the distribution of time spent during a visit to a state is exponential (or geometric for discrete Markov chains). Markov models need also to assume that the transitions in the state of a patient depend only on absolute time whereas the semi-Markov model assumes that transitions depend on time relative to a patient. Thus the models are applicable to degenerative diseases (cancer, acute leukemia), while Markov models with time dependent transition probabilities are applicable to colds and epidemic diseases. In this paper the Laplace transforms are obtained for (i) probability of being in a state at timet, (ii) probability distribution to reach absorption state and (iii) the probability distribution of the first passage times to go from initial states to transient or absorbing states, transient to transient, and transient to absorbing. The model is applied to a clinical study of acute leukemia in which patients have been treated with methotrexate and 6-mercaptopurine. The agreement between the data and the model is very good.

scholarly journals ON PREVENTIVE MAINTENANCE POLICY FOR WAVE DISSIPATING BLOCKS COVERING CAISSON BREAKWATER

2018 ◽  
pp. 72
Author(s):  
Takao Ota ◽  
Hiroyuki Kawamura ◽  
Yoshiharu Matsumi ◽  
Junji Koyanagi ◽  
Takashi Satow
Keyword(s):  
Mathematical Model ◽  
Preventive Maintenance ◽  
Maintenance Cost ◽  
Maintenance Policy ◽  
Reliability Engineering ◽  
Coastal Structures ◽  
Service Period ◽  
Caisson Breakwater ◽  
External Forces ◽  
Preventive Maintenance Policy

The infrastructures are required to keep a certain level of performance during the duration of service. Because the performance of the infrastructures including harbor and coastal structures deteriorates due to aging and damage that is caused by the action of external forces, it is necessary to perform appropriate maintenance. Satow et al. (2009) proposed a mathematical model for the preventive maintenance of wave dissipating blocks based on the method of the reliability engineering. They also derived the expected maintenance cost over the in service period and the optimal preventive maintenance policy. In this study, the optimal threshold for preventive maintenance to minimize the expected maintenance cost is determined for the wave dissipating blocks covering caisson breakwater by using the above model.

Optimal Preventive Maintenance Policy Under a Budget Constraint

2008 ◽  
Author(s):  
Inderjeet Singh ◽  
Elmira Popova ◽  
Ernie Kee
Keyword(s):  
Preventive Maintenance ◽  
Real Data ◽  
Integer Program ◽  
South Texas ◽  
Maintenance Cost ◽  
Maintenance Policy ◽  
Data Set ◽  
Finite Time Horizon ◽  
Preventive Maintenance Policy ◽  
Operating Company

We design an optimal preventive maintenance policy for a system of N items that minimizes the total expected maintenance cost. We assume that the budget for preventive maintenance is limited and constrained. The problem has a finite time horizon and we consider constant inter-preventive maintenance times for every item. The resulting nonlinear optimization problem is reformulated as a binary integer program and computation results are presented on a real data set from South Texas Project Nuclear Operating Company in Bay City, Texas, USA.

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