scholarly journals A fault diagnosis and quality prediction method of ball valves based on state transition probability matrix in Markov chain

2018 ◽  
Vol 1074 ◽  
pp. 012158
Author(s):  
Jihong Pang ◽  
Hua Zhao ◽  
Yini Jin ◽  
Ruiting Wang ◽  
Yongteng Zhong ◽  
...  
Keyword(s):  
Markov Chain ◽  
Fault Diagnosis ◽  
State Transition ◽  
Transition Probability ◽  
Prediction Method ◽  
Transition Probability Matrix ◽  
Quality Prediction ◽  
State Transition Probability

Finite dams with inputs forming a Markov chain

1970 ◽  
Vol 7 (02) ◽  
pp. 291-303 ◽  
Author(s):  
M.S. Ali Khan
Keyword(s):  
Markov Chain ◽  
Probability Distribution ◽  
Generating Functions ◽  
State Transition ◽  
Transition Probability ◽  
Transition Probability Matrix ◽  
Time Dependent ◽  
Probability Generating Functions ◽  
State Transition Probability ◽  
Finite Dam

This paper considers a finite dam fed by inputs forming a Markov chain. Relations for the probability of first emptiness before overflow and with overflow are obtained and their probability generating functions are derived; expressions are obtained in the case of a three state transition probability matrix. An equation for the probability that the dam ever dries up before overflow is derived and it is shown that the ratio of these probabilities is independent of the size of the dam. A time dependent formula for the probability distribution of the dam content is also obtained.

Finite dams with inputs forming a Markov chain

1970 ◽  
Vol 7 (2) ◽  
pp. 291-303 ◽  
Author(s):  
M.S. Ali Khan
Keyword(s):  
Markov Chain ◽  
Probability Distribution ◽  
Generating Functions ◽  
State Transition ◽  
Transition Probability ◽  
Transition Probability Matrix ◽  
Time Dependent ◽  
Probability Generating Functions ◽  
State Transition Probability ◽  
Finite Dam

This paper considers a finite dam fed by inputs forming a Markov chain. Relations for the probability of first emptiness before overflow and with overflow are obtained and their probability generating functions are derived; expressions are obtained in the case of a three state transition probability matrix. An equation for the probability that the dam ever dries up before overflow is derived and it is shown that the ratio of these probabilities is independent of the size of the dam. A time dependent formula for the probability distribution of the dam content is also obtained.

scholarly journals Grey theory–based BP-NN co-training for dense sequence long-term tendency prediction

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yuling Hong ◽  
Yingjie Yang ◽  
Qishan Zhang
Keyword(s):  
State Transition ◽  
Transition Probability ◽  
Training Model ◽  
Transition Probability Matrix ◽  
Content Type ◽  
Fine Grained ◽  
State Transition Probability ◽  
Dense Sequence ◽  
Popularity Prediction

PurposeThe purpose of this paper is to solve the problems existing in topic popularity prediction in online social networks and advance a fine-grained and long-term prediction model for lack of sufficient data.Design/methodology/approachBased on GM(1,1) and neural networks, a co-training model for topic tendency prediction is proposed in this paper. The interpolation based on GM(1,1) is employed to generate fine-grained prediction values of topic popularity time series and two neural network models are considered to achieve convergence by transmitting training parameters via their loss functions.FindingsThe experiment results indicate that the integrated model can effectively predict dense sequence with higher performance than other algorithms, such as NN and RBF_LSSVM. Furthermore, the Markov chain state transition probability matrix model is used to improve the prediction results.Practical implicationsFine-grained and long-term topic popularity prediction, further improvement could be made by predicting any interpolation in the time interval of popularity data points.Originality/valueThe paper succeeds in constructing a co-training model with GM(1,1) and neural networks. Markov chain state transition probability matrix is deployed for further improvement of popularity tendency prediction.

Availability models of series mechanical systems considering failure and maintenance dependencies

2018 ◽  
Vol 42 (3) ◽  
pp. 222-232
Author(s):  
Peng Gao ◽  
Liyang Xie
Keyword(s):  
Optimization Design ◽  
Path Dependence ◽  
State Transition ◽  
Transition Probability ◽  
Mechanical Systems ◽  
Transition Probability Matrix ◽  
Strength Degradation ◽  
Statistical Characteristics ◽  
Established Method ◽  
State Transition Probability

Availability models of series mechanical systems based on system working mechanisms are developed by integrating the statistical characteristics of stress, strength, and maintenance parameters. Failure, maintenance, and strength degradation path dependence are taken into consideration in the proposed models without empirical parameters. Moreover, the problem of inconsistency between the failure rate and the availability in time is pointed out, and a method to solve this problem is proposed. Monte Carlo simulations are carried out to verify the proposed models. In addition, numerical examples are given to illustrate the established method. The results show that failure, maintenance, and strength degradation path dependence and time scale inconsistence in the state transition probability matrix have significant influences on the availability of mechanical systems. The proposed models provide an analytical basis for quantitative availability estimation, optimization design, and maintenance strategy decision-making of mechanical systems.

Predict the Volume of Passenger Transport of Railway Based on Grey Markov Chain Model

2014 ◽  
Vol 1030-1032 ◽  
pp. 2069-2072 ◽  
Author(s):  
Ying Li
Keyword(s):  
Markov Chain ◽  
Prediction Accuracy ◽  
State Transition ◽  
Markov Chain Model ◽  
Transition Probability ◽  
Chain Model ◽  
Grey Prediction ◽  
Passenger Transport ◽  
Passenger Flow ◽  
State Transition Probability

Combined with grey model and the characteristics of the Markov chain, based on the grey prediction model, calculating the state transition probability, grey Markov chain model is established. The results show that the grey Markov chain model has higher prediction accuracy than GM (1, 1) model, can offer references for passenger flow organization.

Prediction of Displacement Time Series Based on Support Vector Machines-Markov Chain

2014 ◽  
Vol 580-583 ◽  
pp. 436-439 ◽  
Author(s):  
Fei Xu ◽  
Wen Xiong Xu ◽  
Ke Wang
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Support Vector Machines ◽  
Transition Probability ◽  
Absolute Error ◽  
Transition Probability Matrix ◽  
Support Vector ◽  
State Transition Probability ◽  
Vector Machines ◽  
Measured Displacement

A new displacement time series predicting model was proposed by combining the Support Vector Machines and the Markov Chain, which was named as Support Vector Machines and Markov Chain (SVM-MC) model. Through studying the measured displacement, SVM optimized by particle swarm optimization (PSO) was used to forecast the trend of macro development in roll. Markov chain was applied to compute State Transition Probability Matrix. By classifying system state and calculating absolute error and relative error between measured value and SVM fitting value, the predicting results are improved. The model was used on predicting displacement time series of a high slope of a permanent lock. The engineering case studies indicated that the model was scientific and reliable, and there was engineering practical value for displacement time series forecasting.

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