Reliability analysis is understanding the likelihood that a product will perform its required functions without failure under stated conditions over a period, which affect how commodities are produced and acquired. A common practice is to first perform product quality assessment to determine how well products perform within its specifications. This is followed by distributing product warranties in order for customers can have some level of redress to account for the possibility of product failures happening within a specified period. However, numerically assigning quality to products only estimates how well they meet certain specifications within the warranty period and neglects to consider their overall lifecycle functionality. Furthermore, this approach is not conducive for understanding the fidelity of complex automated systems for these processes operate continuously as well as contain several interdependent components. Also, most products are repairable, with also some replaceable components, and this also needs to be considered in determining the faithfulness of a product. Reliability analysis addresses these concerns because it accounts for the uncertainty of failures. Additionally, this analysis also takes into account when products as well as its components are repairable or replaceable.

Reliability analysis also plays an integral component of the system engineering lifecycle consisting of the following stages.

  1. Sample Acquisition – In this stage, a representative sample of the product of interest is collected to perform the necessary testing to assess how well the product functions as well as its limitations.
  2. Statistical Reliability Analysis – In this stage, statistical reliability analysis is performed to understand how well this representative sample functions. In this step, statistical data analysis techniques are explored to describe the reliability of the product where a range of outcomes, associated with the specifications of the product, is tested to gather global and micro perspectives of failure behavior.>/li>
  3. Statistical Inference Analysis – In this stage, statistical inference analysis is performed to draw conclusions about the product’s effectiveness from both system and component levels. Furthermore, necessary adjustments are made, such as acquiring more data (or different types of data) as well as more testing methodologies, to provide more robust performance assessments of the product.

This iterative process provides consistent and in-depth performance assessments, where periodic updates and adjustments can be made at each stage. Furthermore, additional assessments and use-cases are also explored which also include repairing and replacing different subsystems and components. For this scenario, the necessary adjustments need to be taken into account in terms of the range of operability of the component and system as well as the changes in the overall product effectiveness. Cost-benefit analyses can also be conducted in each of these stages to not only demonstrate the economic impact of a systems failures on an organization but also how certain adjustments can be made to optimize an organization’s time and costs. At CRC, our specialty is understanding the reliability of complex systems and processes to include high performance computing systems and iris recognition algorithms.