Walsh–Hadamard Domain-Based Intelligent Online Fault Diagnosis of Broken Rotor Bars in Induction Motors

Mechanical failures are the most common defects in induction motors (IMs). They derive from the continuous starts and stops with high-load torque. They can cause interruptions in production lines with great economic losses. Therefore, online monitoring systems, allowing a continuous monitoring for fault diagnosis in early stages, have been intended for many researches in last years. Broken rotor bars (BRBs) are common failures, which are among the most difficult to detect since the induction motor works with apparent normality without giving any signal about the defect. Several methodologies have been proposed to detect BRBs in IMs. However, they require high-computational-complexity algorithms for parameterizing the current signal, making it difficult to achieve an online implementation of the corresponding monitoring system. Hence, aiming to overcome the limitations provoked by complex parameterizations of the current signal and to improve the fault classification accuracy, in this work, a novel methodology, based on a hardware processing system implemented on a field-programmable gate array (FPGA), for electric current signal analysis in the Walsh–Hadamard domain is proposed to achieve online intelligent BRB diagnosis. The
$k$
-nearest neighbors (
$k$
-NN) algorithm is used for classifying different operational conditions from the Walsh–Hadamard transform (WHT) of the electric-current signal. Experimentally obtained results from applying the proposed method on acquired real data demonstrate that the introduced approach reaches a remarkable high efficiency on detecting and classifying BRBs.