Quaternion Signal Analysis Algorithm for Induction Motor Fault Detection

Induction motor fault identification is essential to improve efficiency in industrial processes improving costs, production line, and maintenance time. This paper presents a novel motor fault detection methodology based on quaternion signal analysis. The proposed method establishes the quaternion coefficients as the value of motor current measurement, and the variables x, y, and z are the measurements from a triaxial accelerometer mounted on the induction motor chassis. The method obtains the rotation of quaternions and applies quaternion rotation statistics such as mean, cluster shades, and cluster prominence in order to get their features, and these are used to classify the motor state using the proposed tree classification algorithm. This methodology is validated experimentally and compared to other methods to determine the efficiency of this method for feature detection and motor fault identification and classification.