Fault diagnosis in a distributed motor network using Artificial Neural Network

Signature analysis methods have been proven to deliver good results in the laboratory environment and successfully applied to isolated motors. The influence of fault signal on a non-faulty motor may be interpreted as faulty condition of the healthy motor. Therefore, it is difficult to identify a motor fault within a network and precisely identify the type of fault. This paper presents a supervised distributed Artificial Neural Network (ANN) that is able to identify multiple fault types such as broken rotor bar (BRB) or air gap eccentricity faults as well as the location of fault event within an industrial motor networks. Features are extracted from the current signal, based on different frequency components and associated amplitude values with each fault type. A set of significant fault features such as synchronized speed, rotor slip, the amplitude value of each fault frequency components, the Root Mean Square (RMS) and Crest Factor (CF) value are used to train the ANN using Back Propagation (BP) algorithm. The simulation results show that the proposed technique is able to identify the type and location of fault events within a distributed motor network. The proposed architecture works well with the selection of a significant feature sets and accurate fault detection result has been achieved. Classification performance was satisfactory for healthy and faulty conditions including fault type identification.