When a digital twin’s model coefficient values are adjusted in real-time, noise, digitization, spurious signals and other data features can lead to unwarranted changes in model coefficient values. This is termed tampering, a statistical process control (SPC) label coined by W. Edward Demings to mean adjustment in response to noise—to a phantom indication—not to a true need for adjustment. Tampering increases variability, costs energy, increases wear and adds confusion.
This is Part 3 of a three-part series on understanding the digital twin. Part 1 defined a digital twin as a model of the process that's frequently adapted to match data from the process. This keeps it useful for its intended purpose. Part 2 discussed how to continuously adapt the model with online data, while this Part 3 discusses tempering adaptation in response to noise.
In the context of process model adjustment, data features such as process noise, signal discretization, spurious values, etc., perturb true process values. This can cause an algorithm to make a model coefficient value adjustment, even if no adjustment was justified. An SPC cumulative sum approach can be used to temper such adjustments. That is, to only permit coefficient adjustment when there's statistically significant evidence that adjustment is warranted. This approach follows that originally presented in reference [1], summarized in reference [2] and demonstrated in references [3, 4]. The rule is “Hold the prior coefficient value until there's adequate evidence to report that the value has truly changed.”
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