Various applications of AI techniques (expert systems, neural networks and fuzzy logic) presented in the literature prove that such technologies are well suited to cope with on-line diagnostics tasks for induction machines. The features of these techniques and the improvements that they introduce in the diagnostic process are recalled, showing that, in order to obtain indication on the fault extent, faulty machine models are still essential. The models must trade off between simulation result effectiveness and simplicity. With reference to rotor electrical faults of induction machines, a new and simple model which includes the speed ripple effect is developed. This model leads to a new diagnostic index, independent of the machine operating condition and inertia value, that allows the implementation of the diagnostic system with a minimum configuration intelligence.
AI techniques in induction machines diagnosis including the speed ripple effect / Filippetti, F.; Franceschini, G.; Tassoni, C.; Vas, P.. - 1:(1996), pp. 655-662. (Intervento presentato al convegno Conference Record of the 1996 IEEE Industry Applications 31th IAS Annual Meeting. Part 1 (of 4) tenutosi a San Diego, CA, USA, null nel 1996).
AI techniques in induction machines diagnosis including the speed ripple effect
Franceschini, G.;
1996
Abstract
Various applications of AI techniques (expert systems, neural networks and fuzzy logic) presented in the literature prove that such technologies are well suited to cope with on-line diagnostics tasks for induction machines. The features of these techniques and the improvements that they introduce in the diagnostic process are recalled, showing that, in order to obtain indication on the fault extent, faulty machine models are still essential. The models must trade off between simulation result effectiveness and simplicity. With reference to rotor electrical faults of induction machines, a new and simple model which includes the speed ripple effect is developed. This model leads to a new diagnostic index, independent of the machine operating condition and inertia value, that allows the implementation of the diagnostic system with a minimum configuration intelligence.
Pubblicazioni consigliate
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris
