By Vasile Palade, Cosmin Danut Bocaniala
This ebook offers the latest matters and study leads to commercial fault analysis utilizing clever options. It specializes in computational intelligence purposes to fault analysis with real-world purposes utilized in varied chapters to validate the several analysis tools. The publication contains one bankruptcy facing a unique coherent fault analysis dispensed method for complicated systems.
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Extra info for Computational Intelligence in Fault Diagnosis
Neural Computing & Applications 14(2):149-165 2. Ayoubi M (1994) Fault diagnosis with dynamic neural structure and application to a turbocharger. In: Proceedings of 1st IFAC Symposium SAFEPROCESS’94, Espoo, Finland, vol. 2, pp. 618-623 3. Babuska R (2002) Neuro-fuzzy methods for modeling and identification. In: Abraham A, Jain LC and Kacprzyk J (eds) Recent Advances in Intelligent Paradigms and Applications, pp. 161-186, Springer-Verlag, Heidelberg 4. Bartys M, Patton RJ, Syfert M, De las Heras S and Quevedo J (2004) Introduction to the DAMADICS Actuator FDI Benchmark Study.
Beard R V (1971) Failure accommodation in linear system through selfreorganization (PhD thesis). MIT, Massachusetts, USA 7. Bendtsen JD and Izadi-Zamanabadi R (2002) FDI using neural networks – application to ship benchmark engine gain. In: Preprints of the 15th IFAC World Congress, Barcelona, Spain 8. Bocaniala CD, Sa da Costa J and Palade V (2004) A Novel Fuzzy Classification Solution for Fault Diagnosis. International Journal of Fuzzy and Intelligent Systems 15(3-4):195-206 9. Bocaniala CD, Sa da Costa J and Palade V (2005) Fuzzy-based refinement of the fault diagnosis task in industrial devices.
Sensor noise is assumed to follow a normal distribution whose standard deviation in terms of percentage deviation from the nominal value can be used as a parameter representative of the noise level. 5 are considered sufficiently accurate and realistic. The performance simulations are undertaken mainly using Turbomatch, a steady-state performance simulation code developed at Cranfield University. 8% PCN1 (which identifies the percentage of accomplishing the design point condition by low-pressure shaft speed N1).