Condition monitoring and fault diagnosis of planetary gearboxes: A review

This paper introduces an on-line particle-filtering (PF)-based framework for fault diagnosis and failure prognosis in non-linear, non-Gaussian systems. This framework considers the implementation of two autonomous modules. A fault detection and identification (FDI) module uses a hybrid state-space model of the plant and a PF algorithm to estimate the state probability density function (pdf) of the system and calculates the probability of a fault condition in realtime. Once the anomalous condition is detected, the available state pdf estimates are used as initial conditions in prognostic routines. The failure prognostic module, on the other hand, predicts the evolution in time of the fault indicator and computes the pdf of the remaining useful life (RUL) of the faulty subsystem, using a non-linear state-space model (with unknown time-varying parameters) and a PF algorithm that updates the current state estimate. The outcome of the prognosis module provides information about the precision and accuracy of long-term predictions, RUL expectations and 95% confidence intervals for the condition under study. Data from a seeded fault test for a UH-60 planetary gear plate are used to validate the proposed approach.

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A particle-filtering approach for on-line fault diagnosis and failure prognosis

Uncertainty quantification and model validation of fatigue crack growth prediction

Particle filters (PF) have been established as the de facto state of the art in failure prognosis. They combine advantages of the rigors of Bayesian estimation to nonlinear prediction while also providing uncertainty estimates with a given solution. Within the context of particle filters, this paper introduces several novel methods for uncertainty representations and uncertainty management. The prediction uncertainty is modeled via a rescaled Epanechnikov kernel and is assisted with resampling techniques and regularization algorithms. Uncertainty management is accomplished through parametric adjustments in a feedback correction loop of the state model and its noise distributions. The correction loop provides the mechanism to incorporate information that can improve solution accuracy and reduce uncertainty bounds. In addition, this approach results in reduction in computational burden. The scheme is illustrated with real vibration feature data from a fatigue-driven fault in a critical aircraft component.

Advances in uncertainty representation and management for particle filtering applied to prognostics

An explanation of frequency features enabling detection of faults in equally spaced planetary gearbox

This paper introduces the design of an integrated framework for on-board fault diagnosis and failure prognosis of a helicopter transmission component, and describes briefly its main modules. It suggests means to (1) validate statistically and pre-process sensor data (vibration), (2) integrate model-based diagnosis and prognosis, (3) extract useful features or condition indicators from data de-noised by blind deconvolution, and (4) combine Bayesian estimation algorithms and measurements to detect and identify the fault and predict remaining useful life with specified confidence and minimum false alarms.

M.D. Koelemay

Papers

An integrated approach to helicopter planetary gear fault diagnosis and failure prognosis