Enhancement of physics-of-failure prognostic models with system level features

Abstract

-To truly optimize the deployment of DoD assets, there exists a fundamental need for predictive tools that can reliably estimate the current and reasonably predict the future capacity of complex systems. Prognosis, as in all true predictions, has inherent uncertainty, which has been treated through probabilistic modeling approaches.The novelty in the current prognostic tool development is that predictions are made through the fusion of stochastic physics-of-failure models, relevant system or component level health monitoring data and various inspection results. Regardless of the fidelity of a prognostic model or the quantity and quality of the seeded fault or run-to-failure data, these models should be adaptable based on system health features such as vibration, temperature, and oil analysis. The inherent uncertainties and variability in material capacity and localized environmental conditions, as well as the realization that complex physics-of-failure understanding will always possess some uncertainty, all contribute to the stochastic nature of prognostic modeling. However, accuracy can be improved by creating a prognostic architecture instilled with the ability to account for unexpected damage events, fuse with diagnostic results, and statistically calibrate predictions based on inspection information and real-time system level features. In this paper, the aforementioned process is discussed and implemented first on controlled failures of single spur gear teeth and then on a helical gear contained within a drivetrain system. The stochastic, physics-offailure models developed are validated with transitional run-to-failure data developed at Penn State ARL. Future work involves applying the advanced prognostics process to helicopter gearboxes.

A review of physics-based models in prognostics: Application to gears and bearings of rotating machinery

“…A more common approach for prognostics consists of a PbM based on Paris law 27 to model the degradation mechanism and estimate the crack growth rate, which is an indicator of the RUL. Kacprzynski et al 76 proposed the combination of Paris law along with a simplified two-dimensional (2D) finite element analysis (FEA) for RUL estimation. This approach was extended by Li and Lee, 32 who developed a dynamic model of a transmission to estimate the loads, a FEA to infer the stresses and an algorithm based on Paris law to obtain the RUL.…”

“…Orchard et al 30 applied both PF and an extended Kalman filter (EKF) on a crack growth model based on Paris law to estimate crack length in the plate of the gearbox of a helicopter. Pais 76 who estimated the RUL of helicopter gears by modelling the first region (crack initiation) using Manson-Coffin law presented above to estimate the number of cycles until a crack initiates and using Paris law to estimate the crack growth afterwards. The spall degradation formula presented in equation (11) has been applied for RUL estimation of rolling bearings by Orsagh et al 34 and Li et al, 36,37 while Slack and Sadeghi 120 developed a more sophisticated explicit finite element (FE) spalling model that predicts the number of cycles until failure by modelling the crack initiation in the sub-surface and its propagation.…”

“…A more accurate model, the Manson and Halford model, considers the elastic and plastic component of the strain, thus being able to represent LCF and HCF degradation mechanisms, as shown in equation (8), where D represents the ductility, s u is the ultimate tensile strength and E is the Young's modulus. 126 Kacprzynski et al 76 evaluated the plastic strain of a helicopter gear tooth using an FEA model and later applies Manson-Coffin law to evaluate the number of cycles until crack initiation occurs.…”

“…Permanent deformation consists of plastic deformation, which occurs Localized defects [65][66][67][68][69] Crack detection 70,71 Wear [72][73][74] Crack length estimation 32,[75][76][77] Damage location 26,[78][79][80][81][82] Tooth breakage 11,62,83 Geometrical imperfections 26,55 Contaminants 84…”

Study on the prediction methods of aero engine fault parameters

“…The domestic researchers have made the trends and predict of the shaft wear and lubrication performance, the results show well. Abroad is mainly aimed at monitoring and research of engine health system, and the main engine company in the EHM project has made significant progress [2][3][4]. But on the whole, aero engine fault feature prediction and fault pre diagnosis do not develop well, so in order to fit the maintenance requirements, further studying on the prediction of fault feature parameters and the corresponding fault pre diagnosis is necessary.…”