Refined Stochastic Field Models for Jet Engine Vibration and Fault Diagnostics

2000 ◽  
Author(s):  
Dan M. Ghiocel
Keyword(s):  
Probability Distributions ◽  
Novelty Detection ◽  
Statistical Correlation ◽  
Stochastic Field ◽  
Jet Engine ◽  
Engine Vibration ◽  
Amplitude Fluctuations ◽  
Incipient Fault ◽  
Non Gaussian ◽  
Incipient Fault Diagnosis

The paper proposes a refined stochastic fault classifier for jet engine vibration diagnostic based on advanced stochastic concepts. The statistical data to be analyzed are the spectrum profiles of vibration measured at different locations in the engine. The statistical spectrum profiles are idealized by non-homogeneous stochastic fields with non-Gaussian probability distributions. The proposed stochastic classifier is based on the decomposition of the statistical correlation matrix of spectrum profiles using a Karhunen-Loeve (KL) expansion. Two stochastic classifiers are proposed, namely a “global” and a “specific” fault classifier. The “global” KL classifier, which is a scalar quantity, is an efficient anomaly/novelty detection tool for identifying incipient fault diagnosis with small amplitude fluctuations. The “specific” KL classifier, which is a vector quantity, is a refined diagnostic tool for identifying the engine malfunction causes. An illustrative example of a turbofan engine is included.

Incipient fault diagnosis in power transformers by data-driven models with over-sampled dataset

2021 ◽  
Vol 201 ◽  
pp. 107519
Author(s):  
Sofia Moreira de Andrade Lopes ◽  
Rogério Andrade Flauzino ◽  
Ruy Alberto Corrêa Altafim
Keyword(s):  
Fault Diagnosis ◽  
Data Driven ◽  
Power Transformers ◽  
Incipient Fault ◽  
Incipient Fault Diagnosis

Incipient fault diagnosis of limit switch based on a ARMA model

Measurement ◽  
2019 ◽  
Vol 135 ◽  
pp. 473-480 ◽  
Author(s):  
Ling Wang ◽  
Jin Pan ◽  
Yanfeng Gao ◽  
Bingrui Wang ◽  
Kaixing Hong ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Arma Model ◽  
Incipient Fault ◽  
Incipient Fault Diagnosis ◽  
Limit Switch

Non-Gaussian probability distributions of solar wind fluctuations

1994 ◽  
Vol 12 (12) ◽  
pp. 1127-1138 ◽  
Author(s):  
E. Marsch ◽  
C. Y. Tu
Keyword(s):  
Solar Wind ◽  
Probability Distributions ◽  
Time Lag ◽  
Small Scale ◽  
Time Lags ◽  
Mhd Turbulence ◽  
Theoretical Concepts ◽  
Fluid Turbulence ◽  
Long Time ◽  
Non Gaussian

Abstract. The probability distributions of field differences ∆x(τ)=x(t+τ)-x(t), where the variable x(t) may denote any solar wind scalar field or vector field component at time t, have been calculated from time series of Helios data obtained in 1976 at heliocentric distances near 0.3 AU. It is found that for comparatively long time lag τ, ranging from a few hours to 1 day, the differences are normally distributed according to a Gaussian. For shorter time lags, of less than ten minutes, significant changes in shape are observed. The distributions are often spikier and narrower than the equivalent Gaussian distribution with the same standard deviation, and they are enhanced for large, reduced for intermediate and enhanced for very small values of ∆x. This result is in accordance with fluid observations and numerical simulations. Hence statistical properties are dominated at small scale τ by large fluctuation amplitudes that are sparsely distributed, which is direct evidence for spatial intermittency of the fluctuations. This is in agreement with results from earlier analyses of the structure functions of ∆x. The non-Gaussian features are differently developed for the various types of fluctuations. The relevance of these observations to the interpretation and understanding of the nature of solar wind magnetohydrodynamic (MHD) turbulence is pointed out, and contact is made with existing theoretical concepts of intermittency in fluid turbulence.

Sign in / Sign up

Export Citation Format

Share Document