Abstract
In the last few years, induction motor fault detection has provoked great interest among researchers because it is a fundamental element of the electric-power industry, manufacturing enterprise, and services. Hence, considerable efforts have been carried out on developing reliable, low-cost procedures for fault diagnosis in induction motors (IM) since the early detection of any failure may prevent the machine from suffering a catastrophic damage. Therefore, many methodologies based on the IM startup transient current analysis have been proposed whose major disadvantages are the high mathematical complexity and demanding computational cost for their development. In this study, a straightforward procedure was introduced for identifying and classifying faults in IM. The proposed approach is based on the analysis of the startup transient current signal through the current signal homogeneity and the fourth central moment (kurtosis) analysis. These features are used for training a feed-forward, backpropagation artificial neural network used as a classifier. From experimentally obtained results, it was demonstrated that the brought-in scheme attained high certainty in recognizing and discriminating among five induction motor conditions, i.e., a motor in good physical condition (HLT), a motor with one broken rotor bar (1BRB), a motor with two broken rotor bars (2BRB), a motor with damage on the bearing outer race (BRN), and a motor with an unbalanced mechanical load (UNB).
Highlights
Induction motors have stayed for many years as essential components of every electrical and manufacturing process
The proposed approach can recognize and classify the operational condition of an induction motor as in a good state (HLT), a motor with one split rotor bar (1BRB), a motor with two damaged rotor bars (2BRB), a motor with outer race damage in the bearing (BRN), and a motor with an unbalanced mechanical load (UNB) with high certainty, attaining up to 100% of effectiveness, utilizing just two features of a single phase from the three-phase electrical current supply to the squirrel cage induction motor (SCIM), as inputs to a multilayer perceptron Artificial neural networks (ANN). This is different from other approaches reported in the reviewed literature that even require the signal transformation from the time domain into the frequency domain and back to the time domain to carry out the signal processing in order to extract up to 29 features of the current signals from the three phases and the multi-axis vibration signals, in conjunction, in order to be capable of performing the fault detection
Proposed techniques can detect one single induction motor fault with an adequate certainty; most of them rely on the combination of complex mathematical operations that require specific hardware and software for their implementation
Summary
Induction motors have stayed for many years as essential components of every electrical and manufacturing process. Because of their low cost, stiffness, and quality of performing consistently well, they are extensively used around the planet. Broken rotor bars (BRB), bearing faults (BRN), and rotor unbalance (UNB) constitute very common problems, in heavy duty systems [3]. Many efforts have been made to prevent catastrophic failures to occur with the application of several techniques for fault detection; most of them focus on detecting a single, specific fault separately, such as BRB [4], BRN [5], or UNB [6]
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