Semi-Markov modeling applications in system availability analysis

System availability is considered to be an important measure of system performance. This chapter deals with modeling and analysis of complex mechanical systems that deteriorate with age using the analytical semi-Markov method to assess system availability. It is obvious to state that the failure–repair pattern of mechanical systems generally conforms to Gamma, Log-normal, or Weibull distributions, and in such cases, the conventional Markov analysis becomes untenable. The semi-Markov method is effective in modeling these distributions and provides a pragmatic way of analysis. Maintenance function plays a key role in prolonging the system life by improving its health by eliminating failures. The benefits of maintenance in terms of gain in system availability are predicted using the suggested methodology. Also, the optimal time interval for the preventive maintenance and the condition-based maintenance are determined using the semi-Markov process (SMP) approach combined with some optimization tools. Four examples of engineering systems with different maintenance strategies such as Run-To-Failure Maintenance (RTFM), Preventive Maintenance (PM), Condition-Based Maintenance (CBM), and Opportunistic Maintenance (OM) are presented to demonstrate availability analysis.