This paper presents a physics-informed, tacholess pipeline for smartphone-grade Noise, Vibration, and Harshness (NVH) diagnosis in electric vehicle powertrains. A configurable generator synthesizes labeled signals with order components (1×/2×/3×), AM/FM modulation, sub-harmonics, impact-driven ring-down near resonance, and realistic white/pink/ambient noise at phone bandwidths. A ridge-guided harmonic comb recenters orders without a tachometer and splits tonal from residual content. Interpretable features—order-invariant ratios ($E_{2\times }/E_{1\times }$, ${\mathrm{S}\mathrm{B}}_1$/$E_{1\times }$, $E_{0.5\times }/E_{1\times }$) and residual descriptors (band-power, kurtosis, cepstrum/WPT)—feed light-compute models. A reproducible benchmark stresses SNR (−5…+10 dB), RPM profiles (ramp/steps/cycles), and simulated domain shift; parameter-to-feature analyses (with Sobol sensitivity and a delta-method identifiability proxy) quantify measurability under phone constraints. Across a five-fold CV, tacholess order tracking increases tonal SNR by ≥+6 dB and yields macro-F1 ≈ 0.86 with Random Forest, while ordinal severity achieves QWK ≈ 0.81 (ECE ≈ 0.06) and regression attains MAE ≈ 0.12 (R² ≈ 0.78). All code, datasets, figures, and tables regenerate from fixed seeds with one-command builds; a data card and a sim-to-real guide are included. The result is an open, low-compute standard that couples reproducibility with physics-aligned interpretability, providing a practical baseline for EV NVH diagnostics with smartphones and a common ground for future field validation.

The objective of this work is to use the autonomous learning methodology as a tool in vehicle maintenance management. In obtaining data, faults in the fuel supply system have been simulated, causing anomalies in the combustion process that are easily detectable by vibrations obtained from a sensor in the engine of an agricultural tractor. To train the classification algorithm, 4 engine states were used: BE (optimal state), MEF1, MEF2, MEF3 (simulated failures). The applied autonomous learning is of the supervised type, where the samples were initially characterized and labeled to create a database for the execution of the training. The results show that the training carried out within the classification algorithm has an efficiency greater than 90%, which indicates that the method used is applicable in the management of vehicle maintenance to predict failures in engine operation.

The present study has the purpose of determining the levels of opacity with the use of diesel and biodiesel based on castor oil at 15\% (B15). For the determination of the opacity an electronic diesel engine test bench was used, the opacity measurements were made using a duly calibrated opacimeter. The Opacimeter consists of a probe which is installed at the end of the exhaust pipe, capturing the gases and yielding their own values for each test, considering the types of fossil fuel or biofuel and engine speed, subjecting several RPMs that go from 1200 to 3000 for each fuel. At the end of the tests, the opacimeter allowed to obtain an opacity of 11.1\% with fossil diesel and 0.386 with B15, obtaining a 96\% reduction in opacity. At the same time, an average opacity of the tests was obtained for each type of fuel, this value being compared with the national standards concerning this study. With these results, it contributes to the conservation of the environment since it emits less amount of soot, in the same way, the respiratory and heart diseases of people are reduced according to the world health organization.

Los estudios de materiales compuestos juegan un papel importante en aplicaciones de ingeniería, metalurgia, mecánica y actualmente una alternativa en la fabricación de piezas automotrices. Las fibras naturales reforzadas con polímeros presentan ciertos beneficios como bajo costo, bajo peso, son renovables y son fáciles de procesar. El objetivo de este trabajo es caracterizar el material compuesto con polímeros reforzados con fibras de bambú, para su aplicación en la fabricación de piezas automotrices. Para los ensayos se fabricó probetas de material compuesto de fibra de bambú en proporciones del 75% y 60% con resina epóxica en proporciones de 25 % y 40% dispuestas en 2 y 3 capas. Los resultados en proporción fibra de bambú (75%) – resina epóxica (25%) mostraron un mejor comportamiento mecánico a flexión en un 7,7 % respecto a otras fibras naturales como fibra natural larga de cabuya con fibra de vidrio, lo que se traduce en un aumento de rigidez de los materiales caracterizados, corroborando así, que es un potencial refuerzo de material híbrido alternativo para aplicaciones automotrices. Los resultados de los esfuerzos de tracción fueron validados mediante el método de elementos finitos FEM.