Self-organizing map approach for classification of mechanical and rotor faults on induction motors

Abstract

Two neural network-based schemes for fault diagnosis and identification on induction motors are presented in this paper. Fault identification is performed using self-organizing maps neural networks. The first scheme uses the information of the motor phase current for feeding the network, in order to perform the diagnosis of load unbalance and shaft misalignment faults. The network is trained using data generated through the simulation of a motor-load system model, which allows including the effects of load unbalance and shaft misalignment. The second scheme is based on the motor's active and reactive instantaneous powers, in order to detect and diagnose faults whose characteristic frequencies are very close each other, such as broken rotor bars and oscillating loads. This network is trained using data obtained through the experimental measurements. Additional experimental data are later applied to both networks in order to validate the proposal. It is demonstrated that the proposed strategies are able to correctly identify, both unbalanced and misaligned load, as well as broken bars and low-frequency oscillating loads, thus avoiding the need for an expert to perform the task.

A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors

“…The mean square error is obtained from the sum of the squared errors for all input patterns used in the training set of the network, according to (18):…”

“…The process of adjusting the weights can be found in [43]. The use of ANN to detect faults in electrical machines has been the subject of considerable recent research [5,[18][19][20][21][22]44,23]. …”

“…This method is characterized by its flexibility and range of architecture. Several studies [18] regarding fault classification in induction motors have recently been published. The authors presented two ANN-based schemes in order to identify and diagnose faults in induction motor rotors through the use of self-organizing maps.…”

Industrial Data-Driven Monitoring Based on Incremental Learning Applied to the Detection of Novel Faults

“…The SOM structure depends on the number of available data points and its complexity in terms of distribution. However, as most of works dealing with SOM algorithm suggest, sheet-type structures composed by a number of neurons following a 1:10 ratio in regard to the available data represents a good tradeoff between structure complexity and performance, and this is the rule proposed to be followed in this work [19][20].…”

Introducing Neural Computing in Governance Research: Applying Self‐Organizing Maps to Configurational Studies

“…They have been used in variety of academic disciplines to study a wide range of problems. Specifically, SOMs have been used in climate science research (Hewitson & Crane, 2002), medical research (Villmann, Hermann, & Geyer, 2000), the geographic information sciences (Agarwal & Skupin, 2008), image classification research (Richardson, Risien, & Shillington, 2003), the environmental sciences (Cartwright, 2002), engineering (Bossio, De Angelo, & Bossio, 2013), and physics (Camplani, Cannas, Fanni, Pautasso, & Sias, 2011).…”