Nelinearne multivariatne statistične metode za sprotno odkrivanje napak v industrijskih procesih : doktorska disertacija

slovenian

doktorska dela

2009

Univerza v Mariboru, Fakulteta za elektrotehniko računalništvo in informatiko

Dissertation deals with fault detection in technical systems using multivariate statistical methods adapted to real-time (non-linear) industrial processes. Faults in technical systems are unavoidable and therefore associated with economic loss due to production down-times. To increase quality and efficiency it is necessary to monitor real-time production processes and implement fault detection systems to detect abnormalities. Operation of such system is based on comparison between measured process data and mathematical model, where the modelćs accuracy is crucial to avoid a largenumber of false alarms. Contribution of the dissertation is towards development and optimization of systems for fault detection, including processmodels based on process data, and extension of multivariate statisticalmethod of principal components to fit nonlinear processes. The process model is presented by nonlinear method of principal components, realized by auto-associative structure of artificial neural network. Implementation of advanced algorithms depends on industrial environment specifics and equipment capabilities, therefore performance optimization is needed. Development and optimization of the auto-associative artificial neuralnetwork, its structure and operation is presented, where the artificial neural model is designed by Taguchi design of experiments methodology. FDI on-line monitoring algorithms are processing nonlinear principal components behaviour, extracted from neural network layer, and produce information about process abnormalities, potential faults and serve as precaution measure to avoid process down-time. Furthermore the model quality, a-priory knowledge about the process and implemented sub-models of process faults, are used to achieve fault isolation and process diagnosis. Proposed solutions are tested and confirmed in simulation and on real laboratory test plant.

07.08.2023