Causal Analytics in IIoT – AI That Knows What Causes What , and When
These real-world applications involve methods that take into account the complexity of systems ( thereby including analytics of system machine and log data ). The inter-dependencies and dimensionality of many IIoT system devices mean that identifying their behavior ( causal and otherwise ) can be extremely difficult depending on the magnitude and nature of the couplings . One variable may be found to be a driver of another , but not alone . The multiple influences that have an impact on a particular variable must be teased out , such as the timings , state-dependencies and multi-dimensionality of other influences that impact an ‘ effect ’ of interest , such as a decrease in pressure or rise in temperature . These are identified as part of the rCA process for IIoT . industrial applied mathematics ." 31 This is a fairly recent set of developments and especially with respect to incorporating AI and machine learning where these algorithms can be applied to IIoT data at scale .
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Although the rCA approach can be employed on an ad-hoc basis by an analyst , the real benefits come from automating the rCA AI analysis as part of an IoT process . The rCA function can be executed based on trigger events such as data changes or exceptions . The rCA algorithms are embedded in the functions library of the XMPro IoT Process platform .
This approach has led to an area of causal research from a dynamical systems perspective . A dynamical system is one in which a function describes the time dependence of a point in a geometrical space . 27 28 29 30 A dynamical systems course at Harvard states that the methods have a focus on the behavior of systems described by ordinary differential equations . Application areas “… are diverse and multidisciplinary , ranging over areas of applied science and engineering , including biology , chemistry , physics , finance , and
27 https :// en . wikipedia . org / wiki / Dynamical _ system
28 https :// en . wikipedia . org / wiki / Dynamical _ systems _ theory
29 https :// mathinsight . org / dynamical _ system _ idea
30 http :// math . huji . ac . il /~ mhochman / research-expo . html
31 https :// scholar . harvard . edu / siams / am-147-nonlinear-dynamical-systems
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