Fault diagnostics of DC motor using acoustic signals and MSAF-RATIO30-EXPANDED

Głowacz, Adam

doi:10.1515/aee-2016-0051

Cited by 14 publications

(6 citation statements)

References 35 publications

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“…Importantly, CM and FD based on the AE of RM have been growing over recent years. AE is widely used in rotating electric motors CM and FD 186–188 for both electrical and mechanical faults including, the shaft misalignment, 189 the bearing, 190–192 the rotor 193,194 and the stator. 195 In addition, AE is extensively used in CM and FD for the gearbox.…”

Section: Introduction Of CM and Fd Of Im Based On Aementioning

confidence: 99%

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

AlShorman

Alkahatni

Masadeh

et al. 2021

Advances in Mechanical Engineering

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109

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Nowadays, condition-based maintenance (CBM) and fault diagnosis (FD) of rotating machinery (RM) has a vital role in the modern industrial world. However, the remaining useful life (RUL) of machinery is crucial for continuous monitoring and timely maintenance. Moreover, reduced maintenance costs, enhanced safety, efficiency, reliability, and availability are the main important industrial issues to maintain valuable and high-cost machinery. Undoubtedly, induction motor (IM) is considered to be a pivotal component in industrial machines. Recently, acoustic emission (AE) becomes a very accurate and efficient method for fault, leaks and fatigue detection and monitoring techniques. Moreover, CM and FD based on the AE of IM have been growing over recent years. The proposed research study aims to review condition monitoring (CM) and fault diagnosis (FD) studies based on sound and AE for four types of faults: bearings, rotor, stator, and compound. The study also points out the advantages and limitations of using sound and AE analysis in CM and FD. Existing public datasets for AE based analysis for CM and FD of IM are also mentioned. Finally, challenges facing AE based CM and FD for RM, especially for IM, and possible future works are addressed in this study.

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Section: Introduction Of CM and Fd Of Im Based On Aementioning

confidence: 99%

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

AlShorman

Alkahatni

Masadeh

et al. 2021

Advances in Mechanical Engineering

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109

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“…Recognition using impact acoustic emission (AE) could be an appropriate method. References [28,29,30,31,32,33,34] have reported that this method has been successfully utilized for separation of empty and damaged nuts with high efficiencies of up to 40 pieces/s [29], and this method could find tiny structural failures in rotary facilities and machine parts with high accuracy [31,32,33,34]. Impact AE signals are easily captured and can be processed rapidly by universal computer hardware.…”

Section: Introductionmentioning

confidence: 99%

Study on Impact Acoustic—Visual Sensor-Based Sorting of ELV Plastic Materials

Huang

Tian

Ren

et al. 2017

Sensors

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This paper concentrates on a study of a novel multi-sensor aided method by using acoustic and visual sensors for detection, recognition and separation of End-of Life vehicles’ (ELVs) plastic materials, in order to optimize the recycling rate of automotive shredder residues (ASRs). Sensor-based sorting technologies have been utilized for material recycling for the last two decades. One of the problems still remaining results from black and dark dyed plastics which are very difficult to recognize using visual sensors. In this paper a new multi-sensor technology for black plastic recognition and sorting by using impact resonant acoustic emissions (AEs) and laser triangulation scanning was introduced. A pilot sorting system which consists of a 3-dimensional visual sensor and an acoustic sensor was also established; two kinds commonly used vehicle plastics, polypropylene (PP) and acrylonitrile-butadiene-styrene (ABS) and two kinds of modified vehicle plastics, polypropylene/ethylene-propylene-diene-monomer (PP-EPDM) and acrylonitrile-butadiene-styrene/polycarbonate (ABS-PC) were tested. In this study the geometrical features of tested plastic scraps were measured by the visual sensor, and their corresponding impact acoustic emission (AE) signals were acquired by the acoustic sensor. The signal processing and feature extraction of visual data as well as acoustic signals were realized by virtual instruments. Impact acoustic features were recognized by using FFT based power spectral density analysis. The results shows that the characteristics of the tested PP and ABS plastics were totally different, but similar to their respective modified materials. The probability of scrap material recognition rate, i.e., the theoretical sorting efficiency between PP and PP-EPDM, could reach about 50%, and between ABS and ABS-PC it could reach about 75% with diameters ranging from 14 mm to 23 mm, and with exclusion of abnormal impacts, the actual separation rates were 39.2% for PP, 41.4% for PP/EPDM scraps as well as 62.4% for ABS, and 70.8% for ABS/PC scraps. Within the diameter range of 8-13 mm, only 25% of PP and 27% of PP/EPDM scraps, as well as 43% of ABS, and 47% of ABS/PC scraps were finally separated. This research proposes a new approach for sensor-aided automatic recognition and sorting of black plastic materials, it is an effective method for ASR reduction and recycling.

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“…The early detection of faults prevents financial loss and downtimes. In the literature researchers developed methods of fault detection based on acoustic [1][2][3][4][5][6][7][8][9][10][11][12][13], thermal [14][15][16][17][18][19][20] and vibration signals [21][22][23][24][25][26][27][28][29]. An acoustic signal is difficult to process because microphone records many sounds from environment.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers

Głowacz¹,

Głowacz²,

Głowacz³

et al. 2017

Archives of Metallurgy and Materials

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A degradation of metallurgical equipment is normal process depended on time. Some factors such as: operation process, friction, high temperature can accelerate the degradation process of metallurgical equipment. In this paper the authors analyzed three phase induction motors. These motors are common used in the metallurgy industry, for example in conveyor belt. The diagnostics of such motors is essential. An early detection of faults prevents financial loss and downtimes. The authors proposed a technique of fault diagnosis based on recognition of currents. The authors analyzed 4 states of three phase induction motor: healthy three phase induction motor, three phase induction motor with 1 faulty rotor bar, three phase induction motor with 2 faulty rotor bars, three phase induction motor with faulty ring of squirrel-cage. An analysis was carried out for original method of feature extraction called MSAF-RATIO15 (Method of Selection of Amplitudes of Frequencies -Ratio 15% of maximum of amplitude). A classification of feature vectors was performed by Bayes classifier, Linear Discriminant Analysis (LDA) and Nearest Neighbour classifier. The proposed technique of fault diagnosis can be used for protection of three phase induction motors and other rotating electrical machines. In the near future the authors will analyze other motors and faults. There is also idea to use thermal, acoustic, electrical, vibration signal together.

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Fault diagnostics of DC motor using acoustic signals and MSAF-RATIO30-EXPANDED

Cited by 14 publications

References 35 publications

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

Study on Impact Acoustic—Visual Sensor-Based Sorting of ELV Plastic Materials

Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers

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