Induction motor rotor fault diagnosis using three-phase current intersection signal

Khelfi, Hamid; Hamdani, S.

doi:10.1007/s00202-019-00894-7

Cited by 31 publications

(9 citation statements)

References 23 publications

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“…where, Fis represents spurious frequency, Fs is the frequency of the power supply, Fv characteristic frequency of the fault and k ∈ ℕ. Moreover, non-invasive current signature analysis have multiple advantages including easy implementation, inexpensiveness, and no interference with other signals of motor [4]. Thus, it is has been point of attention for the researchers in this domain.…”

Section: Current Signature Analysismentioning

confidence: 99%

“…Three phase induction motors (IMs) have been widely employed in various industries including railways, aeronautics, petrochemical, gas plants, and automotive industry [1,2]. According to the research reports the power consumption by induction motor in the industries is about 60% of total power generated by any country [3,4], which implies that IMs are an essential part of industry [5,6]. Among various types of motors, the squirrel cage motors have been widely used in industrial applications owing to its reliable operation and cost-effectiveness.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learninag based Non-invasive and Real-Time Fault Detection System for 3-Phase Induction Motors

Fahim

Soother²,

Harijan³

et al. 2021

SJET

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Induction motor plays a major role in industry. Despite of its strong structure, induction motors are often prone to faults. There are different types of faults that occurs in the induction motor such as bearing faults, winding faults, etc. Thus motors in major applications require continuous and effective monitoring. In this paper, a stand-alone and non-invasive condition monitoring system that can monitor the condition of 3-phase induction motor using motor current signatures with aid of deep learning (DL) approaches. The proposed system extracts the features using non-invasive current sensors it further samples the features using an analog to digital converter (ADC) and organizes the data acquired from ADC using Raspberry-pi microcomputer. The current data acquired from induction motor is used to train and test the DL models including Multilayer Perceptron (MLP), Long Short-term Memory (LSTM), and One-Dimensional Convolutional Neural Networks (1DCNN). The comparative analysis is demonstrated and the LSTM model as best fault classifier among all with accuracy up to 100%. Finally, the real-time testing of the device showed that the developed system can effectively monitor the conditions of motor using non-invasive current sensors.

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Section: Current Signature Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Deep Learninag based Non-invasive and Real-Time Fault Detection System for 3-Phase Induction Motors

Fahim

Soother²,

Harijan³

et al. 2021

SJET

View full text Add to dashboard Cite

show abstract

“…Current signals and vibration signal information are used in this reference for the reason that these extracted signals are useful for discovering mechanical and electrical failures in induction machines. In the paper [ 16 ], a spectral analysis and ZSC procedure are performed to locate the rotor fault. These advanced methods are demonstrated theoretically and confirmed experimentally by testing the motor under varying load.…”

Section: Introductionmentioning

confidence: 99%

An experimental method for diagnostic of incipient broken rotor bar fault in induction machines

Sabir

Ouassaid

Ngote

2022

Heliyon

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“…In the literature, one can find applications of various signals and methods. Stator current-based analysis methods [4][5][6][7][8][9], zero-sequence component of stator current [10][11][12][13][14][15], zero-sequence component of voltage [6,13,[16][17][18][19], and zero-sequence component of axial flux [20][21][22][23][24][25][26][27], were all taken into account. These methods allow for the detection of various rotor cage asymmetries.…”

Section: Introductionmentioning

confidence: 99%

A New Effective Method of Induction Machine Condition Assessment Based on Zero-Sequence Voltage (ZSV) Symptoms

Duda

Sułowicz

2020

Energies

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Non-invasive diagnostic methods for electric machines’ diagnostics, which can be used during their operation in a drive system, are needed in many branches of the production industry. For the reliable condition assessment of electric machines, especially those operating in drive systems, various tools and methods have been suggested. One diagnostic method that has not been fully recognized and documented is a diagnostic method based on zero-sequence voltage component (ZSV) applications for the condition assessment of induction machines. In this paper, the application of ZSV in induction machine diagnostics is proposed. A factor that speaks in favor of applying this signal in such diagnostics is the high sensitivity of the signal to damage occurrence, and the distinct change of extracted symptoms in the case of asymmetry. It is possible to obtain a high signal amplitude, which simplifies its processing and the elaboration of reliable diagnostic factors. This ZSV-based method is also able to be applied to big machines used in industry. Due to the saturation effects visible in the ZSV signal, new diagnostic symptoms can appear, which allows for an easier condition assessment of certain machines. The usefulness of the described diagnostic method in machine condition assessment was shown through an equivalent circuit modeling process, finite element analysis, and laboratory tests of the machine.

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Induction motor rotor fault diagnosis using three-phase current intersection signal

Cited by 31 publications

References 23 publications

A Deep Learninag based Non-invasive and Real-Time Fault Detection System for 3-Phase Induction Motors

A Deep Learninag based Non-invasive and Real-Time Fault Detection System for 3-Phase Induction Motors

An experimental method for diagnostic of incipient broken rotor bar fault in induction machines

A New Effective Method of Induction Machine Condition Assessment Based on Zero-Sequence Voltage (ZSV) Symptoms

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