Reliability Of An Integrated System With Big Data And Reliability Analytics

Reliability Of An Integrated System With Big Data And Reliability Analytics

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Reliability Analytics
Reliability is defined as the probability that an item will perform a required function without failure under stated conditions for a specific period of time.(O’Connor) Failure or errors come in many different forms. Failure in parts or products can come in the form of fractures, fatigue, creep, corrosion, or dimensional inaccuracies. Failure can also come in the form of machine breakdowns or inefficient workers. Unreliable products will result in large amounts of materials and time being wasted. If the part or product has already left for distribution it can also result in recalls, high warranty costs, and decreases in sales. Occasional machine and tool breakdowns that stop production are quite costly as well. All of these different types of errors result in millions of dollars of added costs for companies every year. The use of big data can be an effective tool in minimizing these costs and increasing reliability.
Developing an integrated system with big data and reliability analytics is crucial for reducing the costs associated with reliability. The ability to handle more data means larger and more frequent sample sizes. This data can be instantly stored and shared with factories throughout the world. The use of such large data sets makes it easier to detect variances and then accurately predict potential failures. Using certain graphical or statistical methods you can spot trends in this data to further optimize processes. For example, trends in the timing of machine breakdowns can be spotted and investigated. The causes of these trends can be found and then steps can be made to reduce such breakdowns.
In manufacturing, acceptance sampling is used to d...


... middle of paper ...


...amounts of data from every machine in the shop to be integrated and analyzed.
Charts with multiple variables are also utilized. Multivariable charts are useful for determining if variation is temporary, cyclic, or spatial. The parameter of interest is simply measured at different positions and at different points in production.(O’Connor) For example, measuring hardness on different areas of the product in different points in the production cycle. This data is then plotted against another variable in order to spot trends. For example the hardness could be plotted against batch number to determine the consistency of hardness from day to day. Correlations that were previously unnoticed may appear when using big data. These correlations lead to the discovery of new causes of failure.

(still want to touch up a couple things and get sources figured out)

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