Fault detection in rotating machinery using kernel-based probability density estimation

Abstract

In this paper, a method is presented which allows abnorm al or unexpected operating conditions to be identi® ed from measured response data. Potential applications of such a technique cover a wide range of engineering situations where a de® nite, early warning of an abnorm al state is essential, but where classi® cation of the particular abnorm ality is of lesser importance. In the technique described, unexpected operating conditions are identi® ed by the presence of measured data which are signi® cantly di erent from those known to correspond to normal operating conditions or responses. T he proposed approach to the detection of such novel data is based upon probability density function (PDF) estimation using a kernel method. T he theory behind this approach is presented for the multivariate case, and the need for data pre-processing in practical applications of PDF estimation is highlighted. T he kernel method is demonstrated on simulated data sets. Finally, vibration response data from an electric motor with three levels of phase imbalance are used to illustrate the application of the PDF estimation method to the detection of abnorm al conditions. T he proposed method yields a clear indication of the existence of a fault in the AC motor, with no prior knowledge of the particular fault state.

Modeling and Diagnosis of Friction and Wear in Industrial Robots

“…Large values of this test quantity would indicate a change in behavior. This idea has been suggested by ; Desforges et al (2000); where flexible density models are used.…”

Fault Detection Based on Multi-Dimensional KDE and Jensen–Shannon Divergence

“…However, f (x) is unknown in Equation (20), and therefore, ∫ tr ∂ 2 f (x) ∂x∂x T dx is also unknown. An approximate value of the bandwidth parameter h m can be obtained by replacing f (x) withf K (x) in Equation (16). Furthermore, an iterative algorithm can be used to calculate a more accurate bandwidth parameter.…”

“…This method can effectively solve the strong coupling between data; however, it will lose some information in the process of PCA dimension reduction, and it leads to a reduction in the fault detection effect. In reference Itani et al [13], Kong et al [14], Jones and Sheather [15], Desforges et al [16], a bearing fault detection method based on KDE is proposed. These studies analyzed the feasibility of KDE method in fault detection, and combined different classification methods for experiments.…”

Fault diagnosis of nonlinear and large-scale processes using novel modified kernel Fisher discriminant analysis approach

“…A lot of fault diagnosis methods were proposed and applied in the actual process (Desforges, Jacob, & Ball, 2000;Jakob & Henrik, 2010;Jia, Martin, & Morris, 2000), which have established line lists which were much refined compared to pre-launch estimates and would set us in a good position for future observation and analysis. With the development of new sensors and data gathering techniques, large amounts of process data are available in modern industrial processes.…”