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In the field of axles, life forecasting can be used to predict the lifespan of the axle and to determine more effective maintenance schedules. The life forecasting can have a significant positive impact on the safe operation of train axles, which can be used to predict the lifespan of the axle and to determine more effective maintenance schedules. Traditional methods for predicting the life of the shaft are based on experimental and statistical analysis, but these methods can be expensive and time-consuming.

The LSTM-based Lifetime Forecasting method uses machine learning technology and historical data on the axles to more accurately predict remaining lifespan, thereby reducing costs and improving efficiency. This invention is innovative in that it introduces a semi-monitoring technique for estimating an axle RUL’s full life based on YOLOv5 and LSTM training. A large number of unmarked data are trained along with YOLOv5 and LSTM to determine interrupted data to RUL predictions. The use of a laboratory-collected sound crack on the 5 million, 7 million and 10 million signal data collection and RMSE MAE values, for verifying the performance of the model, respectively, for the full cycle of life of 5, 7 million, 10 million vehicles, is predicted with a strong difference between the predictable life of 5M and the remaining value of the forecast model.

Structural components in aircraft are often required to be operative beyond their original design service objectives (DSO). Vital issues for aging infrastructures are estimation and prediction, with confidence, of residual life, reliability, and availability, given the service history. Uncertainty increasingly is considered to be a major factor. Demanding high reliability exacerbates the role of uncertainty. One aspect of life cycle management for aging aircraft was investigated by replicating laboratory specimens subjected to fatigue loading that is typical for a class of military aircraft wing skins. Samples were fabricated from 7075-T6 plate aluminum alloy similar to that used for wing panels. A total of 15 specimens were tested. Tests were terminated when the fatigue life expended (FLE) reached a prescribed value of 100%, 150%, or 200% of the component DSO. Then, microscopy was used to quantify the size of fatigue cracks within high stress regions of simulated fastener holes in laboratory specimens. Cumulative distribution functions (cdfs) for geometrical properties of cracks and constituent particles in the alloy were characterized statistically as input for residual life estimations and for life cycle management analyses. Insights into crack initiation and growth are also provided.

Acrylic bone cement is a poly(methyl methacrylate)-based material that ensures short-term stability of orthopedic implants after surgery. Its long-term performance can be affected by many factors (e.g., composition, cement mixing and delivery method, temperature, humidity). Furthermore, patient activities produce a spectrum of cyclic loads that generate microdamage within the acrylic bone cement mantle. Therefore, pre-clinical studies on fatigue damage of acrylic bone cements are essential for predicting the long-term stability of cemented implants. There are several methods for analyzing damage of acrylic bone cement. However, they present a number of limitations. The aim of this study was to validate the use of a high-resolution scanner to analyze the presence of microcracks in acrylic bone cement. The proposed method met predetermined criteria to overcome limitations of previous methods, ensuring approximate spatial resolution of 5 microns, reduction of image acquisition time, and reduction of artifacts due to operator and/or environment during image acquisition. Additionally, the described method was applied to three types of acrylic bone cement specimens that previously were subjected to a fatigue test. The presented method enables the accurate assessment of fatigue damage induced during cycling loading, including quantification of the number, length, type and position of cement cracks.