"The mechanical designers and fitters were still adamant that it was my problem because they had checked everything else already and nothing else was different from the machines that worked perfectly."
As a test engineer, whenever I was called to the line to try to figure out what was wrong with a test set I had built, I would always ask if the operator had run the "golden" units we used for calibration, to see if the test set was working properly and the data was accurate. The answer was often no, it never occurred to them when parts started failing that their process could have shifted - it MUST be something wrong with the test set!
Great story. This is the kind of tale that illustrates how much chance enters into problem solving. I wonder what the odds are against all those conditions lining up perfectly just so the real problem could actually be perceived, let alone what Rod then figured out to solve it.
In an age of globalization and rapid changes through scientific progress, two of our societies' (and economies') main concerns are to satisfy the needs and wishes of the individual and to save precious resources. Cloud computing caters to both of these.
For industrial control applications, or even a simple assembly line, that machine can go almost 24/7 without a break. But what happens when the task is a little more complex? That’s where the “smart” machine would come in. The smart machine is one that has some simple (or complex in some cases) processing capability to be able to adapt to changing conditions. Such machines are suited for a host of applications, including automotive, aerospace, defense, medical, computers and electronics, telecommunications, consumer goods, and so on. This discussion will examine what’s possible with smart machines, and what tradeoffs need to be made to implement such a solution.