Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors

Skowron, Maciej; Wolkiewicz, Marcin; Orłowska-Kowalska, Teresa; Kowalski, C. T.

doi:10.3390/en12122392

Cited by 27 publications

(33 citation statements)

References 41 publications

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“…There are several topical works presenting applications of the ANN for detection of short circuits, electrical damages and defects as well as for testing materials of required mechanical, electrical and burning properties [13][14][15].…”

Section: Zastosowanie Sztucznych Sieci Neuronowych Do Identyfikacji Polimerów Na Podstawie Ich Palnościmentioning

confidence: 99%

Application of the artificial neural networks for identification of polymers on the basis of their flammability

Rabiej¹

2020

Polimery

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The work presents the structure and principle of operation of the artificial neuron network constructed for identification of a polymer on the basis of its flammability. The characteristic properties of burning of a polymer are saved in a special form in a database. The network creates a binary standard for each polymer from the database, coding data by means of the signals of the values 1, 0, -1. The network memorizes data related to each polymer detecting the similarities and differences between them and determines the weights which reflect the importance of particular features of its burning process.

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Section: Zastosowanie Sztucznych Sieci Neuronowych Do Identyfikacji Polimerów Na Podstawie Ich Palnościmentioning

confidence: 99%

Application of the artificial neural networks for identification of polymers on the basis of their flammability

Rabiej¹

2020

Polimery

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“…The fault detection systems focused on electrical circuits damages, in most cases are based on the phase current signal [3,12,13] or axial flux [2,9] analyses due to the ease of their measurement, as well as their high sensitivity to emerging damages. In addition, the diagnostics of electric machines uses phase voltages [1], temperature [7], electromagnetic torque [15] and signals from the control structure of the drive system [16].…”

Section: Introductionmentioning

confidence: 99%

Application of simplified convolutional neural networks for initial stator winding fault detection of the PMSM drive using different raw signal data

Skowron

Orłowska-Kowalska

Kowalski

2021

IET Electric Power Appl

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Permanent magnet synchronous motors (PMSM) have become one of the most substantial components of modern industrial drives. These motors, like all the others, can unfortunately undergo various failures, causing production line downtime and resulting losses. Accordingly, it is necessary to develop fault diagnostic techniques which detect the damages at the earliest possible stage. This study presents a method of detecting incipient faults of the PMSM stator windings using direct signal analysis and a convolutional neural network (CNN). During the tests, the structures of CNN were optimised to constitute a balance between the high efficiency of fault detection and a small number of network parameters. The effectiveness of the CNNs with inputs constituted by different electrical signals measured in the drive system is compared. Three raw data signals are tested as CNN inputs, namely: stator phase currents, phase-to-phase voltages and axial flux, without data preprocessing. The article aims to show the possibility of detecting the incipient interturn short circuits in the PMSM stator winding based on the information obtained directly from the measured signals as well as to present the influence of the drive operating conditions and the type of measurement signals used on the structure and performance of the developed CNNs.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Observation of changes occurring in AC machines is carried out using signals available for measurement on the tested object. The most commonly used are currents [2,3], voltages [4,5], vibrations [6,7] as well as flux [8,9] and temperature [10,11]. The idea of analytical methods for assessing the technical condition of the machine is based on the extraction of damage symptoms in measured diagnostic signals.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor

et al. 2020

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In this paper, the idea of using a convolutional neural network (CNN) for the detection and classification of induction motor stator winding faults is presented. The diagnosis inference of the stator inter-turn short-circuits is based on raw stator current data. It offers the possibility of using the diagnostic signal direct processing, which could replace well known analytical methods. Tests were carried out for various levels of stator failures. In order to assess the sensitivity of the applied CNN-based detector to motor operating conditions, the tests were carried out for variable load torques and for different values of supply voltage frequency. Experimental tests were conducted on a specially designed setup with the 3 kW induction motor of special construction, which allowed for the physical modelling of inter-turn short-circuits in each of the three phases of the machine. The on-line tests prove the possibility of using CNN in the real-time diagnostic system with the high accuracy of incipient stator winding fault detection and classification. The impact of the developed CNN structure and training method parameters on the fault diagnosis accuracy has also been tested.

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Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors

Cited by 27 publications

References 41 publications

Application of the artificial neural networks for identification of polymers on the basis of their flammability

Application of the artificial neural networks for identification of polymers on the basis of their flammability

Application of simplified convolutional neural networks for initial stator winding fault detection of the PMSM drive using different raw signal data

Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor

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