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Induction motors fed through variable speed drives (VSD) are widely used in different industrial processes. Nowadays, the industry demands the integration of smart sensors to improve the fault detection in order to reduce cost, maintenance and power consumption. Induction motors can develop one or more faults at the same time that can be produce severe damages. The combined fault identification in induction motors is a demanding task, but it has been rarely considered in spite of being a common situation, because it is difficult to identify two or more faults simultaneously. This work presents a smart sensor for online detection of simple and multiple-combined faults in induction motors fed through a VSD in a wide frequency range covering low frequencies from 3 Hz and high frequencies up to 60 Hz based on a primary sensor being a commercially available current clamp or a hall-effect sensor. The proposed smart sensor implements a methodology based on the fast Fourier transform (FFT), RMS calculation and artificial neural networks (ANN), which are processed online using digital hardware signal processing based on field programmable gate array (FPGA).

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Gearboxes are important components in industrial applications and its condition monitoring is relevant in industriesfor reducing costs and minimizing maintenance downtime. Diagnosing wear in gearboxes saves time to prepare appropriate corrective actions, and to ensure that the system does not deteriorate critically. Nowadays, for condition monitoring in gearboxes, vibration analysis is commonly used due to its high reliability. In this work, a reliable methodology for diagnosing different levels of wear in a gearbox through vibration signals, and supported by a theoretical model, is proposed. The theoretical model is based on calculating the characteristic frequencies of the gearbox, with the aim for locating the spectral components of the faults in the vibration signal. Experimentation is done to a healthy gearbox and three wear levels. Results show the reliability of this method that makes it suitable to be used in diagnosing industrial machinery such as in automotive manufacturing applications.

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