Designing High Availability Systems: DFSS and Classical by Zachary Taylor

By Zachary Taylor

A functional, step by step consultant to designing world-class, excessive availability structures utilizing either classical and DFSS reliability techniques

Whether designing telecom, aerospace, automobile, scientific, monetary, or public security platforms, each engineer goals for the maximum reliability and availability within the platforms he, or she, designs. yet among the dream of world-class functionality and truth falls the shadow of complexities which could bedevil even the main rigorous layout approach. whereas there are an array of strong predictive engineering instruments, there was no single-source advisor to figuring out and utilizing them . . . till now.

Offering a case-based method of designing, predicting, and deploying world-class high-availability platforms from the floor up, this publication brings jointly the simplest classical and DFSS reliability innovations. even though it specializes in technical features, this advisor considers the company and marketplace constraints that require that platforms be designed correct the 1st time.

Written in simple English and following a step by step "cookbook" structure, Designing excessive Availability Systems:

  • Shows the best way to combine an array of design/analysis instruments, together with Six Sigma, Failure research, and Reliability Analysis
  • Features many real-life examples and case experiences describing predictive layout equipment, tradeoffs, threat priorities, "what-if" eventualities, and more
  • Delivers quite a few high-impact takeaways that you should observe on your present tasks immediately
  • Provides entry to MATLAB courses for simulating challenge units offered, besides PowerPoint slides to aid in outlining the problem-solving process

Designing excessive Availability Systems is an integral operating source for process engineers, software/hardware architects, and undertaking groups operating in all industries.

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Sample text

Thus, we win 6 out of 8 times (2/3), which is the same for the nonrandom scenario. ” Now what information can we extract to make our bet? ” and we bet on heads, we win only 50% of the time. ” and we bet on tails, we also win only 50% of the time. This is because what the assistant says is not adding any extra information. 6 DICE PARADOX REVISITED From the discussion on two-coin toss example, the two-dice paradox is simply a 6 × 6 version of the 2 × 2 coin toss. 15 Combinations of Only One ‘4’ Showing Up event reduces the number of possible events that need to be considered, thus increasing our chances of guessing correctly that a particular event (or events) has occurred.

What this means is that the chances of at least one ‘4’ showing up when we roll the dice is 11/36, a slightly more rare event than the “intuitive” but incorrect answer of 1/6. Since this event is rarer, our sample space for conditional probability is smaller, that is, our sample space is now 11 events. Of these 11 events, 2 pairs have a sum of 7, thus the probability is 2/11. What if instead of picking a random number before each dice throw to see if it shows up, let us instead just look at the pair of dice and tell the gambler one of the values shown.

10/6 probability of getting a ‘4’ ”, we proudly say. Wait a minute! How can we have a probability that exceeds 1? After all, a probability value can only range from 0 (absolutely impossible) to 1 (happens always every time). Does obtaining 10/6 or 167% must mean that we are absolutely certain this will happen after 10 rolls and just to make sure we have added 67% padding on top for good measure? Well, something is not right. We know a probability of any event or series of events can never exceed 1.

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