Intelligent diagnostic on mill fan system

Abstract

The mill fans (MF) are centrifugal fans of the simplest type with flat radial blades adapted for simultaneous operation both like fans and also like mills. The key variable that could be used for diagnostic purposes is vibration amplitude of MF corpse. However its mode values include a great deal of randomness. Therefore the application of deterministic dependencies with correcting coefficients is non-effective for MF predictive modeling. Standard statistical and probabilistic (Bayesian) approaches are also inapplicable to estimate MF vibration state due to non-stationarity, non-ergodicity and the significant noise level of the monitored vibrations. Adequate for the case methods of computational intelligence (fuzzy logic, neural networks and more general AI techniques -the precedents' method or machine learning (ML)) must be used. The present paper describes promising initial results on applying the Case Based Reasoning (CBR) approach for intelligent diagnostic of the mill fan working capacity using its vibration state.

Consistent Data and Decision Fusion of Heterogeneous Information Denoising in Complex Systems Diagnosis

“…A combination of different intelligent methods is increasingly observed [5,16,8] for using the advantages of each component in the hybrid scheme. Diagnosis of complex technological plants is a key element in the rapidly evolving field of Condition Based Monitoring and Maintenance (CBM) [1,9,10,12,14,16]. The complexity of CBM requires a new approach in decision making, given the great variety of possible solutions and the significant uncertainty [1,9,12].…”

CBR Approach for Technical Diagnostics of Mill Fan System

“…Generally the application of the method with precedents to determine the vibrational S V,CBR MB (k) and the common S CBR MB diagnostic state is shown in [ 14 ]. Block CBR(n, k) is the generally accepted four-stage CBR procedure introduced in [ 7 ] and widely used in the last 15 years [ 10,13,14 ].…”

“…Block CBR(n, k) is the generally accepted four-stage CBR procedure introduced in [ 7 ] and widely used in the last 15 years [ 10,13,14 ]. Estimates of the mill fan diagnostic state may be obtained with a discretization of T CBR = 2h, i.e.…”

“…This paper considers diagnostics of the coal mill fan system of unit 3 boiler 6 at Maritsa East 2 complex. The mill fan structure is presented in [ 14 ]. The boiler which milling system is studied is a Benson type once-though sub-critical boiler.…”

“…The standard statistical and probabilistic (Bayesian) approaches for diagnostics are inapplicable to estimate mill fan vibration state due to non-stationarity, nonergodicity and the significant noise level of the monitored vibrations. Promising results are obtained only using computational intelligence methods (fuzzy logic, neural and neuro-fuzzy networks), [15][16][17][18][19] ].…”