Artificial neural network based fault diagnostics for three phase induction motors under similar operating conditions

Abstract

This paper describes an Artificial Neural Network (ANN) based fault diagnosis methodology for Induction Motors (IM) operating under the same conditions for various speeds and loads. In this study, ten different IM fault conditions are considered. We considered five mechanical faults (bearing fault, unbalanced rotor, misaligned rotor, bowed rotor, rotor with broken bar), four electrical faults (phase unbalance fault with two levels of severity, stator winding fault with two levels of severity), and one healthy motor condition. The current and vibration signals were considered in this work as these signals are generally considered to be the most efficient for the detection of mechanical and electrical faults in IM when used simultaneously. A machine fault simulator was used for the generation of vibration and current signals from different fault conditions. An ANN model was developed in which raw time domain vibration (in three directions) as well as current (in three phases) data are used simultaneously as input and then the fault diagnosis (training and testing) is performed. In this work, the fault diagnosis was attempted when testing was done for the same operating conditions as training. The developed fault diagnosis methods were found to be robust for various operating conditions (speeds and loads) of the IM.

A Low-Cost Microcontroller-Based Normal and Abnormal Conditions Classification Model for Induction Motors Using Self-Organizing Feature Maps (SOFM)

“…Similarly, the voltage and rotor speed weight maps display a significant correlation. This observation aligns with the insights from Equation (11), further consolidating a robust linkage between these two variables. The SOM's topology, the pictorial representation of the weight maps, and the corroborative analysis drawn from the corresponding equations underscore the interconnectedness and interdependence of the input variables within the SOM framework.…”

“…The features chosen for constructing the SOM include rotor speed, phase currents for stator a, b, and c, and electromagnetic torque. The correlation between current and torque is significantly high, as validated by Equation ( 8), and a similar correlation is observed between voltage and rotor speed as per Equation (11). In this visual representation of weights, it is essential to highlight the role of the first-principles model.…”

Pattern recognition and diagnosis of short and open circuit faults inverter in induction motor drive using neural networks

“…In the literature, the diagnosis of open circuit faults using neural networks is considered as a pattern recognition problem [9][10][11][12][13]. The most commonly used methods consist of two key steps; feature extraction and fault classification.…”

Artificial neural network–based fault diagnosis for induction motors under similar, interpolated and extrapolated operating conditions

“…Chouhan et al 32 performed the diagnosis for electrical and mechanical faults of IM using ANN. In this work, they have done training and testing of the ANN at the same operating condition and found satisfactory results.…”

“…Therefore, we need to consider diagnosis when the data are available on limited operating conditions. 35 After reviewing research papers on AI-dependent defect detection of IM, it is found that the ANN-based IM fault diagnosis for limited operating conditions is lacking in the literature.…”