Pradeep Shetty

A Hybrid Prognostic Model Formulation and Health Estimation of Auxiliary Power Units

Prognostic health monitoring is an important element of condition-based maintenance and logistics support. The accuracy of prediction and the associated confidence in prediction greatly influence overall performance and subsequent actions either for maintenance or logistics support. Accuracy of prognosis is directly dependent on how closely one can capture the system and component interactions. Traditionally, such models assume a constant and univariate prognostic formulation-that is, components degrade at a constant rate and are independent of each other. Our objective in this paper is to model the degrading system as a collection of prognostic states (health vectors) that evolve continuously over time. The proposed model includes an age dependent deterioration distribution, component interactions, as well as effects of discrete events arising from line maintenance actions and/or abrupt faults. Mathematically, the proposed model can be summarized as a continuously evolving dynamic model, driven by non-Gaussian input and switches according to the discrete events in the system. We develop this model for aircraft auxiliary power units, but it can be generalized to other progressive deteriorating systems. The system identification and recursive state estimation scheme for the developed non-Gaussian model under a partially specified distribution framework has been deduced. The diagnostic/prognostic capabilities of our model and algorithms have been demonstrated using simulated and field data.

A hybrid prognostic model formulation system identification and health estimation of auxiliary power units

Prognostic health monitoring (PHM) is an important element of condition-based maintenance and logistics support. The accuracy of prediction and the associated confidence in prediction, greatly influences overall performance and subsequent actions either for maintenance or logistics support. Accuracy of prognosis is directly dependent on how closely one can capture the system and component interactions. Traditionally, such models assume constant and univariate prognostic formulation - that is, components degrade at a constant rate and are independent of each other. Our objective in this paper is to model the degrading system as a collection of prognostic states (health vectors) that evolve continuously over time. The proposed model includes an age dependent deterioration distribution, component interactions, as well as effects of discrete events arising from line maintenance actions and/or abrupt faults. Mathematically, the proposed model can be summarized as a continuously evolving dynamic model, driven by non-Gaussian input and switches according to the discrete events in the system. We develop this model for aircraft auxiliary power units (APU), but it can be generalized to other progressive deteriorating systems. We derive the system identification and recursive state estimation scheme for the developed non-Gaussian model under a partially specified distribution framework. The diagnostic/prognostic capabilities of our model and algorithms have been demonstrated using simulated and field data

A combined fbm and PPCA based signal model for on-line recognition of PD signal

The problem of on-line recognition and retrieval of relatively weak industrial signal such as Partial Discharges (PD), buried in excessive noise has been addressed in this paper. The major bottleneck being the recognition and suppression of stochastic pulsive interference (PI), due to, overlapping broad band frequency spectrum of PI and PD pulses. Therefore, on-line, on-site, PD measurement is hardly possible in conventional frequency based DSP techniques. We provide new methods to model and recognize the PD signal, on-line. The observed noisy PD signal is modeled as linear combination of systematic and random components employing probabilistic principal component analysis (PPCA). Being a natural signal, PD exhibits long-range dependencies. Therefore, we model the random part of the signal with fractional Brownian motion (fBm) process and pdf of the underlying stochastic process is obtained. The PD/PI pulses are assumed as the mean of the process and non-parametric analysis based on smooth FIR filter is undertaken. The method proposed by the Author found to be effective in recognizing and retrieving the PD pulses, automatically, without any user interference.