This article describes a power transmission system applicable to vehicles. It consists of an oscillating, ratcheting-type, continuously variable transmission (CVT) system governed by an inertia mechanism. The inertia-regulating mechanism adds an additional degree of freedom and gives the system a dynamic character. The transmission consists of three different subsystems. The first of these converts the rotation of the engine or motor into an oscillating angular velocity movement and regulates the amplitude of this movement. The oscillating rotation from the first subsystem is used to drive a second subsystem, which acts as a regulating device by means of an inertial mechanism. The oscillating movement at the output of the second subsystem is rectified in the third, resulting in a unidirectional angular velocity. As a result, a unidirectional torque is generated at the output of the CVT, commensurate with the operating condition of the transmission, and this is capable of overcoming a torque resistance. A prototype of this transmission was built and tested to check the experimental results against those predicted by a series of computational simulations. As a result, the experimental graphs that characterize the operation of this type of transmission system were obtained, demonstrating its ability to function in an efficient manner.
A methodology to support and automate the prediction of maintenance intervention alerts in transport linear infrastructures is a very useful tool for maintenance planning and managing. This piece of work goes along this track combining the current and predicted state condition of the assets, unit components of the infrastructure, with operational and historical maintenance data, to derive information about the needed maintenance operations to avoid later severe degradation. By means of data analytics and machine learning techniques, the proposed methodology generates a prioritized listing, ranked on severity levels, corresponding to the pre-alerts and alerts generated by all assets of the transport infrastructure. The methodology is applied and tested to a real case consisting of a road network with different section classes. The analysis of the results shows that the algorithms and tools developed have good predicting capabilities.
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