The aluminum has mechanical properties enable to a better finishing in welded region. However, it fails eventually when a dynamic load is applied. This study estimated the damage by multiaxial fatigue present in welded joints of bicycle frames, the methodologies of analysis are based on Findley's and Dang Van's Methods. Seven experiments were performed on two pipes with welded joints. These pipes were choosen according to Bike S/A data, numerical simulations in bicycle frame and previous analysis of specialized literature. Therefore, lifespan result of frame, in function the distance, is $3\times 10^4$ km - $5.5\times 10^4$ km.

Condition monitoring of multirotor drones is challenging due to slight motor asynchrony and variations in rotational speed, which can cause detectors to misclassify states despite unchanged operating conditions. This work proposes a semi-supervised pipeline for anomaly detection in drone vibration signals, designed to attenuate the effect of rotational speed. Vibration data from nine signals (five healthy and four faulty) were collected at different speeds. The pipeline includes signal envelope extraction, computation of 51 features, detrending, and feature selection, yielding four key attributes. Six anomaly detectors were evaluated: a fuzzy ensemble (Meta-Fuzzy), Isolation Forest (IF), One-Class SVM (OCSVM), Extended-IF 1, Autoencoder (AE) and Variational Autoencoder (VAE). IF achieved the best accuracy (94% for the three-class setup and 93.6% for the two-class setup), followed by Meta-Fuzzy (92.81% and 93.64%). The main contribution lies in developing an integrated, lightweight, and experimentally validated methodology that unifies signals from multiple speeds into a single healthy class, maintaining high accuracy and paving the way for future onboard implementations.