Plant data processing with AI technologies enables a holistic approach to investigations of process deviations or equipment failures. These comprehensive analyses quickly reveal Systemic Root Causes (SRC), as well as, typical (in some cases evident) Discreet Root Causes (DRC).
Systemic Root Causes are in general originated in early stages of a process, several functional blocks upstream the machine or system, where the problem (symptom) manifests.
Traditionally, influences of operating parameters, so distant from the problem, were difficult to identify, but now with the computational power of AI algorithms it is possible to analyse thousands of parameters over long periods of time, and in a matter of fractions of minutes these correlations are revealed.
Most frequently SRC´s have the highest potential for bringing dramatic improvements if they were corrected. This is because SRC´s tend to have a negative effect on multiple functional blocks in addition to the one experiencing the problem. When SRC´s are corrected the benefit has multiple effects and achieved savings are significant, from overall improvement in plant efficiency.
Discreet Root Causes are those identified nearby the machine or system affected.Generally they manifest when the degradation has already taken place and the failure or deviation is imminent. Very frequently RCA´s gravitate around these DRC´s and correcting these have limited benefit and don´t address the main problem.