LAUSR

The early fault detection in the rotary electrical machines,such as induction motors (IMs), has been growing in modern industry. IMs have been widely used in industrial applications due to its easy installation, reliability, and low cost. However, the increasing usage of IMs also increases the need for timely maintenance in order to ensure their operation and a longer service life. This article proposes a new diagnosis methodology based on maximal overlap discrete wavelet transform and a lightweight 1-D convolutional neural network (CNN) architecture, in order to detect mechanical and electrical faults and their combination, in adjustable speed drive (ASD)-powered IMs. Specifically, single and combined faults were studied from the next: Outer raceway bearing (mechanical), turn-to-turn short-circuit, and phase-to-ground short circuit (electrical). The presented study was developed using current signals acquired from stators of IMs of 1 hp. The current signals are measured at powered conditions introduced by a power grid with a constant frequency at 60 Hz, and an ASD at three different frequencies. The proposed diagnostic methodology reaches more than 99% of accuracy.