Optimising the Preventive Maintenance Interval Using a Semi-Markov Process, Z-Transform, and Finite Planning Horizon

This chapter uses a semi-Markov process and the z transform to find the optimal preventive maintenance interval when dealing with maintenance decision making for a finite time planning horizon. The result is a method that can be easily implemented to assets for which a Weibull reliability analysis exists. The suggested preventive interval formulation is simple and practical. The requirements to apply this simple formula are related to the existence of asset´s reliability data as well as cost/rewards that the assets have when remaining or transitioning to a given state. The application of this method can be very straightforward, and the tool can become a good decision support tool allowing “what if” analysis for different time horizon and maintenance policies.

STOCHASTIC MODELING AND SENSITIVITY ANALYSIS OF AN INTEGRATED HARDWARE-SOFTWARE SYSTEM SUBJECT TO DIFFERENT WEATHER CONDITIONS

The current study covenants with stochastic investigation of an integrated hardware-software system considering hardware failure, software up-gradation upon failure, precautionary maintenance (PM) after a pre-determined process time, maximum repair time of hardware and different weather conditions. All time dependent indiscriminate variables are arbitrarily dispersed. Some important reliability measures like MTSF, availability and revenue of the system are obtained by using well established techniques semi-Markov process. Regenerative point technique has also been taken into consideration during model development. Sensitivity analysis of these measures is also performed. Finally, empirical analysis is done to demonstrate the results for a specific case. To climax the significance of the study, graphs of MTSF, availability, profit and sensitivity are also depicted.

Prognosis of Cancer - A Semi Markov Process

Cancer begins in cells, the building blocks that make up tissues. Tissues make up the organs of the body. The buildup of extra cells often forms a mass of tissue called a growth, polyp or tumor. Tumors can be benign (non cancerous) or malignant (cancerous). Benign tumors are not as harmful as malignant tumors. The transformation of normal cells into cancer cells is called Carcinogenesis.Cancer is one of the major health problems persisting world-wide. Urbanization, industrialization, changes in lifestyles, population growth and ageing all have contributed for epidemiological transition in the country. The absolute number of new cancer cases is increasing rapidly due to growth in size of the population The stages of cancer are considered as different states of a Markov Process. Discrete-time Markov chains have been successfully used to investigate treatment programs and health care protocols for chronic diseases like HIV, AIDS, Hypertension etc. In this study, the process of carcinogenesis was classified into 6 states. The history of every patient is recorded in the form of a data segment starting from initial state.The transitional states and absorbing states are well defined. Since all the patients under study do not reach the last state at a given point of time, the process was studied as a Semi Markov Process. Maximum likelihood estimation of the transitional probabilities, the survival function, the hazard function and the waiting time distribution of patients in different states were studied. This kind of statistical methodology used to study the prognosis of cancer can be applied to real-time data of cancer patients.

M2M Energy Saving Strategy in 5G Millimeter Wave System

M2M (Machine to Machine) technology has a broad application prospect in 5G network, but one of the bottlenecks is the energy consumption of intelligent devices powered by battery. In this paper, we study the energy saving strategy in 5G millimeter wave system. Firstly, a Discontinuous Reception scheme based on Beam Measurement (BM-DRX) is proposed to avoid the unnecessary beamforming. Secondly, by modifying the frame structure and optimizing the beamforming, the time of beamforming is further shortened and the power consumption is also saved. Finally, based on the ETSI (European Telecommunications Standards Institute) data model, the beam misalignment events are regarded as Poisson distribution, and the semi-Markov process is used to analyze the BM-DRX. Simulation results show that the proposed scheme can not only meets the delay performance, but also saves the energy consumption of the system.

A SEMI MARKOV PROCESS APPROACH IN EVALUATION OF SERVICE TIME OF BEACON MESSAGE TRANSMISSION IN THE DSRC MEDIUM ON VEHICULAR ENVIRONMENT

On a highway each vehicle periodically tries to communicate with the RSU by transmitting beacon messages and other general messages like position, speed, destination etc. Beacon messages need to be given high priority and high-speed service since they are important for the broadcasting vehicle and also for the other vehicles within the stipulated radius. Instead of employing a Markov model, we employ a Semi Markov process and evaluate the service time transmission of the tagged state with a particular emphasis on the QOS of the beacon messages

Stochastic Model on Three-Similar Units Three Phased Mission System with FCFS Repairs Pattern

The paper allocates a stochastic model on threesimilar units three-phased mission system. The developed system consists of units working in parallel, series and parallel configurations respectively. Initially, the three similar units are operational. Each component has only three states: good, degraded and failed. In this case, the single repair facility that repairs the units in first come first serve (FCFS)pattern has been thought of. Using Semi-Markov Process and regenerative point techniques, various measures of the system performance at each phase are obtained. The system has been analyzed graphically taking a particular case. Various conclusions are made regarding the reliability and cost consideration of the system at each phase as well as for the whole system(as combined Phase I, Phase II, Phase III).

Semi-Markov Based Dependability Modeling of Bitcoin Nodes Under Eclipse Attacks and State-Dependent Mitigation

The block chain technology has immense potential in many different applications, including but not limited to cryptocurrencies, financial services, smart contracts, supply chains, healthcare services, and energy trading. Due to the critical nature of these applications, it is pivotal to model and evaluate dependability of the block chain-based systems, contributing to their reliable and robust operation. This paper models and analyzes the dependability of Bitcoin nodes subject to Eclipse attacks and state-dependent mitigation activities. Built upon the block chain technology, the Bitcoin is a peer-to-peer cryptocurrency system enabling an individual user to trade freely without the involvement of banks or any other types of intermediate agents. However, a node in the Bitcoin is vulnerable to the Eclipse attack, which aims to monopolize the information flow of the victim node. A semi-Markov process (SMP) based approach is proposed to model the Eclipse attack behavior and possible mitigation activities that may prevent the attack from being successful during the attack process. The SMP model is then evaluated to determine the steady-state dependability of the Bitcoin node. Numerical examples are provided to demonstrate the influence of the time to restart the Bitcoin software and time to detect and delete the malicious message on the Bitcoin node dependability.