Basics of System Reliability Analysis

=Overview= In life data analysis and accelerated life testing data analysis, as well as other testing activities, one of the primary objectives is to obtain a life distribution that describes the times-to-failure of a component, subassembly, assembly or system. This analysis is based on the time of successful operation or time-to-failure data of the item (component), either under use conditions or from accelerated life tests.

For any life data analysis, the analyst chooses a point at which no more detailed information about the object of analysis is known or needs to be considered. At that point, the analyst treats the object of analysis as a "black box." The selection of this level (e.g. component, subassembly, assembly or system) determines the detail of the subsequent analysis.

In System Reliability one constructs a "System" model from these component models. In other words in system reliability analysis we are concerned with the construction of a model (life distribution) that represents the times-to-failure of the entire system based on the life distributions of the components, subassemblies and/or assemblies ("black boxes") from which it is composed. As illustrated in Figure 2.1.



To accomplish this, the relationships between components are considered and decisions about the choice of components can be made to improve or optimize the overall system reliability, maintainability and/or availability. There are many specific reasons for looking at component data to estimate the overall system reliability. One of the most important is that in many situations it is easier and less expensive to test components/subsystems rather than entire systems. Many other benefits of the system reliability analysis approach also exist and will be presented throughout this reference.

=Basic Terminology=

Simulation Calculation
If one includes information on the repair and maintenance characteristics of the components and resources available in the system, other information can also be analyzed/obtained, such as system availability, throughput, spare parts usage, life costs, etc. This can be accomplished through discrete event simulation. In simulation, random failure times from each component's failure distribution are generated. These failure times are then combined in accordance with the way the components are reliability-wise arranged within the system. The overall results are analyzed in order to determine the behavior of the entire system. The advantages of the simulation approach are:
 * 1) It can be used for highly complex scenarios involving a multitude of probabilistic events, such as corrective maintenance, preventive maintenance, inspections, imperfect repairs, crew response times, spare part availability, etc. When events such as these are considered, analytical solutions become impossible when dealing with real systems of sufficient complexity.
 * 2) The discrete event simulation also has the capabilities for:


 * a) Examining resource usage, efficiency and costs.
 * b) Optimizing procedures and resource allocation.
 * c) Analyzing relationships between systems and components.
 * d) Maximizing throughput.
 * e) Minimizing work downtimes.

The disadvantages of the simulation approach are:


 * 1) It can be time-consuming.
 * 2) The results are dependent on the number of simulations.
 * 3) There is a lack of repeatability in the results due to the random nature of data generation.

Simulation is discussed in Chapters 8 and 9.