Abstract
Three-phase induction motors are extensively used in industrial processes due to their robustness, adaptability to different operating conditions, and low operation and maintenance costs. Induction motor fault diagnosis has received special attention from industry since it can reduce process losses and ensure the reliable operation of industrial systems. Therefore, this paper presents a study on the use of meta-heuristic tools in the diagnosis of bearing failures in induction motors. The extraction of the fault characteristics is performed based on mutual information measurements between the stator current signals in the time domain. Then, the Artificial Bee Colony algorithm is used to select the relevant mutual information values and optimize the pattern classifier input data. To evaluate the classification accuracy under various levels of failure severity, the performance of two different pattern classifiers was compared: The C4.5 decision tree and the multi-layer artificial perceptron neural networks. The experimental results confirm the effectiveness of the proposed approach.
Highlights
Induction machines are widely used in industrial applications due to their simple structure, their robustness in harsh environments, and their adaptability to different types of load, as well as their low acquisition and maintenance cost [1]
A k-fold cross-validation technique was employed to obtain the experimental results to allow the training data set to be sufficiently representative. This technique works by randomly dividing the dataset into k subsets of the same size, and in each iteration, k − 1 subsets are used for training the classifiers, and the remaining subset is used for validation
The C4.5 decision tree is presented as a promising proposal for the diagnosis of bearing failures in the three-phase induction motors (TIMs) fed by the MM440 inverter
Summary
Induction machines are widely used in industrial applications due to their simple structure, their robustness in harsh environments, and their adaptability to different types of load, as well as their low acquisition and maintenance cost [1]. In the work of Nayana and Geethanjali [14] previously described, the PSO and WDE algorithms allowed the identification of the most relevant features extracted through the feature selection tools used in the methodology This approach presented classification rates of approximately 96% even in situations where the TIMs were subject to variations in the load, demonstrating the system’s promising diagnostic capacity. The contribution of this work consists of developing an alternative methodology for the diagnosis of bearing failures in three-phase induction motors based on the optimization of the input matrix of the pattern classifier This approach selects the most relevant attributes of mutual information measurements between the signal processing stage’s current signals using the ABC algorithm.
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