An ICA Approach To Control Loop Diagnosis

It is acknowledged that normal conditions of processes areguaranteed by control systems, thereby whose reliability and safety areparticularly important. In response to strong correlation and non-Gaussiandistribution characteristics of control loop signals, Independentcomponent analysis (ICA) is suggested as a data-driven approach tocontrol loop diagnosis.Firstly, common faults associated with every part of a control loopare presented, along with their mathematical expressions as well asresponse curves with respect to open and closed-loops. Then, ICA isintroduced to monitor control loops, where inputs and outputs of thecontroller and transmitter, as well as the output of the actuator arespecified as the monitoring signals. Historical data in normal conditionsare utilized to construct ICA models which contribute to control limits ofI2andSPE. In this sense, a fault happened in a control loop can bedetected by means of changes of the statistics. Simulation resultsdemonstrate the benefits of the contribution.Two separate situations concerning unchanged and changed set-points are particularly considered. In the former situation, diagnosisaccuracy is influenced by the strength of noise variance, significant noisevariance possibly even leading to false diagnosis. While in the lattersituation, ICA approaches could achieve relatively correct diagnosisresults.TE process is employed as an exemplary case to testify thecontribution. Experimental results show that the proposed ICAapproaches can promptly and accurately detect and diagnosis controlloops. In addition, an application software system is developed using VBlanguage as well as OPC and database techniques.