Characterization of brushed DC motor with brush fault using thermal assessment

Direct current motors (DC motor) are used in the small electric devices commonly. Brushed DC motors are cheap and easy to install, thus their popularity. Although the popularity, faults occur which make diagnosis and detection of faults very important. It avoids financial loss and unexpected shutdown operation causes by these faults. This paper is a present characterization of brushed DC motor with brush fault using thermal signature analysis. To organize the character, the temperature profile of DC motor was analysed using the K-type thermocouple with data logger. The thermocouples were mounted on 4 part of the DC motor, casing, permanent magnet, brush and bearing. The temperature data of DC motor with faulty brush and healthy DC motor were measured by thermocouple and recorded using data logger in real time until steady state temperature, under different load. The analysis on the steady state temperature of brush fault can be conclude through recognisable of characteristics temperature difference with a healthy motor

Adaptive incremental ensemble of extreme learning machines for fault diagnosis in induction motors

This paper proposes an adaptive incremental ensemble of extreme learning machines for fault diagnosis. The diagnostic system contains a data processing unit which aims to progressively generate discriminant features from the vibration signals for decision making. The decision making unit receives a few sets of labeled discriminant features in a chunk by chunk manner, incrementally learns the features-faults relations, dynamically diagnoses multiple bearing defects, and adaptively adjusts itself to learn new concept classes. This adaptive ensemble system is based on incremental learning of multiple extreme learning machines that are able to consult together and adjust themselves based on their confidence in the decision making. Extreme learning machines are used to construct the hybrid ensemble due to their good controllability and fast learning rate. Experimental results show the efficiency of the hybrid diagnostic system. The proposed diagnostic system is applied to diagnosing bearing defects in an induction motor