An Investigation of State-Space Model Fidelity for SSME Data

In previous studies, a variety of unsupervised anomaly detection techniques for anomaly detection were applied to SSME (Space Shuttle Main Engine) data. The observed results indicated that the identification of certain anomalies were specific to the algorithmic method under consideration. This is the reason why one of the follow-on goals of these previous investigations was to build an architecture to support the best capabilities of all algorithms. We appeal to that goal here by investigating a cascade, serial architecture for the best performing and most suitable candidates from previous studies. As a precursor to a formal ROC (Receiver Operating Characteristic) curve analysis for validation of resulting anomaly detection algorithms, our primary focus here is to investigate the model fidelity as measured by variants of the AIC (Akaike Information Criterion) for state-space based models. We show that placing constraints on a state-space model during or after the training of the model introduces a modest level of suboptimality. Furthermore, we compare the fidelity of all candidate models including those embodying the cascade, serial architecture. We make recommendations on the most suitable candidates for application to subsequent anomaly detection studies as measured by AIC-based criteria

Model for early detection of non-compliance of process parameters in manufacturing systems

Doktorska disertacija se bavi razvojem konceptualnog modela za rano otkrivanje neusaglašenosti procesnih parametara (RONP) u proizvodnim sistemima. RONP model predstavlja hibridni model baziran na upotrebi fazi ekspertnih sistema i metoda napredne analitike, čiji je razvoj podeljen u sedam faza primenom i prilagođavanjem metodologije proučavanja podataka. Verifikacija modela je urađena u procesnoj industriji za proizvodnju podnih obloga od vinila gde je i eksperimentalno potvrđena njegova primenljivost.The Ph. D. thesis deals with the development of a conceptual model for early detection of non-compliance of process parameters in manufacturing systems. The model represents a hybrid model based on the use of fuzzy expert systems and advanced analytics methods. The development of the model is divided into seven phases by applying and adapting the data minig methodology. The verification of the model was done in the process industry for the production of vinyl flooring, where its applicability was experimentally confirmed