Current and Stray Flux Combined Analysis for the Automatic Detection of Rotor Faults in Soft-Started Induction Motors

Abstract

Induction motors (IMs) have been extensively used for driving a wide variety of processes in several industries. Their excellent performance, capabilities and robustness explain their extensive use in several industrial applications. However, despite their robustness, IMs are susceptible to failure, with broken rotor bars (BRB) being one of the potential faults. These types of faults usually occur due to the high current amplitude flowing in the bars during the starting transient. Currently, soft-starters have been used in order to reduce the negative effects and stresses developed during the starting. However, the addition of these devices makes the fault diagnosis a complex and sometimes erratic task, since the typical fault-related patterns evolutions are usually irregular, depending on particular aspects that may change according to the technology implemented by the soft-starter. This paper proposes a novel methodology for the automatic detection of BRB in IMs under the influence of soft-starters. The proposal relies on the combined analysis of current and stray flux signals by means of suitable indicators proposed here, and their fusion through a linear discriminant analysis (LDA). Finally, the LDA output is used to train a feed-forward neural network (FFNN) to automatically detect the severity of the failure, namely: a healthy motor, one broken rotor bar, and two broken rotor bars. The proposal is validated under a testbench consisting of a kinematic chain driven by a 1.1 kW IM and using four different models of soft-starters. The obtained results demonstrate the capabilities of the proposal, obtaining a correct classification rate (94.4% for the worst case).

Automatic Classification of Rotor Faults in Soft-Started Induction Motors, Based on Persistence Spectrum and Convolutional Neural Network Applied to Stray-Flux Signals

“…With regards to the accuracy of the methodologies, some of the works in Table 9 achieved a rate of 100% [ 22 , 23 , 49 ], but all of them were focused on DOL starting, and they were analyzing current signals. On the other hand, those works focused on soft-started induction motors and achieved, in both cases, an overall accuracy of 94.40%, analyzing the stray-flux [ 27 ] and the combination of stray-flux and current [ 28 ]. Both of them relied on the STFT as the time–frequency analysis tool, which displays noisy time–frequency maps when soft starters are used, making it more difficult to identify the typical patterns related to broken bars.…”

“…The accuracy rate achieved in this work was 94.40%. By their side, in [ 28 ], the authors used Linear Discriminant Analysis (LDA) and an FFNN, applied to a combination of current and stray-flux signals, to detect the presence and the severity of bar breakages in an IM driven by soft starters. In this case, the accuracy rate achieved was 94.40%.…”

Normalized-Model Reference System for Parameter Estimation of Induction Motors

“…We start by providing the background for the electrical parameter estimation studied herein. We begin by considering a constant operation speed, which allows applying the Laplace transform to the electrical subsystem (1) and (2). By substituting the rotor flux Ψ rαβ (s) into the current I sαβ (s) and regrouping terms, we obtain the following transfer function [34]:…”

“…It does not need knowledge of the motor-load parameters. The electronic drive covers soft starters [2] and variable speed drives following details regarding the motor-load parameters knowledge.…”

Experimental study of a teeth flux sensor for detection, location and severity evaluation of induction machine stator faults

“…or thermal (insulation degradation) (Pusca et al , 2022; Irhoumah et al , 2021; Almounajjed et al , 2021). One of the primary faults affecting induction motors is faults in the stator windings (Navarro-Navarro et al , 2022; Lee et al , 2021b; Park et al , 2020b). However, one of the difficulties researchers encounter is distinguishing faults such as inter-turn shorts, unbalanced voltage supplies and rotor eccentricity(Lee et al , 2021).…”