Machine learning based multi class fault diagnosis tool for voltage source inverter driven induction motor

Jyothi, R.; Holla, Tejas; K, Uma Rao; Jayapal, R.

doi:10.11591/ijpeds.v12.i2.pp1205-1215

Cited by 6 publications

(6 citation statements)

References 24 publications

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“…Various gating techniques are available to trigger the inverter IGBT switches. With the implementation of SVPWM gating techniques and Third Harmonic Injection Techniques, the stator current harmonics can be reduced within the acceptable limits to maintain the power quality in the system [16].…”

Section: Variable Frequency Drivementioning

confidence: 99%

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Condition Monitoring and Feature Extraction of Stator Current Phasors for Enhanced Fault Diagnosis in AC Drive

Jyothi¹,

Holla²,

NS³

et al. 2021

IJEAT

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AC drives are employed mainly in process plants for various applications. In most industrial applications, Induction motor drives are preferred as they are robust, reliable, and efficient. Process industries have seen a paradigm shift from manual control to automatic control. Advancements in power electronics technology have led to smooth control of the induction motor using variable frequency drives over an entire speed range. Variable Frequency Drives (VFD) comprises of Voltage source inverter and a three phase squirrel cage induction motor. Various electric faults that are incipient in the VFD cause an abrupt change in circuit parameters resulting in insulation damage, reduced efficiency, and leading to catastrophic failure of the entire system. Hence, continuous monitoring of the system parameters such as stator current, speed, and the vibration of the machine is essential to diagnose incipient faults in the system. AI techniques have been effectively used in the fault diagnosis of electrical systems. In the proposed work, simulation results of machine learning-based fault diagnosis techniques are presented. Real-time IoT-based condition monitoring of the Variable Frequency Drive is also implemented for enhanced fault diagnosis of various incipient electrical faults in AC drives. The experimental results obtained are validated with the simulation data.

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Section: Variable Frequency Drivementioning

confidence: 99%

“…16 classes are considered, including the health system. Data of output DC voltage, inverter output voltage, Stator currents and their phase angles, total harmonic distortion of output voltage, current, speed, vibration are all extracted from the simulation model and the hardware set up also.…”

mentioning

confidence: 99%

Condition Monitoring and Feature Extraction of Stator Current Phasors for Enhanced Fault Diagnosis in AC Drive

Jyothi¹,

Holla²,

NS³

et al. 2021

IJEAT

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“…There are a number of works aimed to carry out a comparative analysis of the effectiveness of these methods for various [2]- [5]. In this paper the development of current analysis method is proposed allowing to investigate SCIMs working in long steady-state conditions, typical for many technological processes without their stopping both at supplying from autonomous voltage inverter [6] and at supplying from grid [7]. Various methods of signal processing are used to analyze current and voltage peaks: frequency (fast Fourier transform), time (time series analysis, Park transform), frequency-time (wavelet transform), and intelligence (neural networks, fuzzy logic and genetic algorithms).…”

Section: Introductionmentioning

confidence: 99%

Neural network based detecting induction motor defects supplied by unbalanced grid

Dobroskok

Skakun

Belskii

et al. 2023

IJPEDS

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<span lang="EN-US">Predictive diagnosis of motor defects can reduce repair and downtime costs of electrical equipment. This paper presents the results of a research on the effectiveness of the defect classification and recognition system based on convolutional neural network to detect defects in a squirrel-cage induction motor based on the stator supply voltage and output phase currents when supplied from an industrial grid of limited power with possible voltage asymmetry and harmonic distortion taken into account. In this work a simulation model, implemented in MATLAB/Simulink, is proposed to investigate unbalanced conditions. The mathematical model has been verified on a physical test bench and then used to create a database of currents from measured asymmetric grid voltages in the presence of defects such as short circuit between turns of one phase, rotor bar breakage, rotor eccentricity and bearing defects. The classification quality of all defects in the neural network was 72%, with the exclusion of intercoil short circuit defect and the merging of different bearing defects, the quality of the model was achieved 91%. A further increase in the quality of the defect recognition system is due to a building up modification of the neural network architecture.</span>

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“…Recently, researchers are exploring more on data-driven FDD due to its simple yet reliable method. This method has gained much interest among researchers in many areas, such as in air conditioning systems [10]- [14], power generation systems [15]- [19] and motor drive systems [20], [21]. The method is simple to develop because it only requires historical data to train and validate its operational data.…”

Section: Introductionmentioning

confidence: 99%

Parameter selection in data-driven fault detection and diagnosis of the air conditioning system

Sulaiman

Abdullah

et al. 2022

IJEECS

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Data-driven fault detection and diagnosis system (FDD) has been proven as simple yet powerful to identify soft and abrupt faults in the air conditioning system, leading to energy saving. However, the challenge is to obtain reliable operation data from the actual building. Therefore, a lab-scaled centralized chilled water air conditioning system was successfully developed in this paper. All necessary sensors were installed to generate reliable operation data for the data-driven FDD. Nevertheless, if a practical system is considered, the number of sensors required would be extensive as it depends on the number of rooms in the building. Hence, parameters impact in the dataset were also investigated to identify critical parameters for fault classifications. The analysis results had identified four critical parameters for data-driven FDD: the rooms' temperature (TTCx), supplied chilled water temperature (TCHWS), supplied chilled water flow rate (VCHWS) and supplied cooled water temperature (TCWS). Results showed that the data-driven FDD successfully diagnosed all six conditions correctly with the proposed parameters for more than 92.3% accuracy; only 0.6-3.4% differed from the original dataset's accuracy. Therefore, the proposed parameters can reduce the number of sensors used for practical buildings, thus reducing installation costs without compromising the FDD accuracy.

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Machine learning based multi class fault diagnosis tool for voltage source inverter driven induction motor

Cited by 6 publications

References 24 publications

Condition Monitoring and Feature Extraction of Stator Current Phasors for Enhanced Fault Diagnosis in AC Drive

Condition Monitoring and Feature Extraction of Stator Current Phasors for Enhanced Fault Diagnosis in AC Drive

Neural network based detecting induction motor defects supplied by unbalanced grid

Parameter selection in data-driven fault detection and diagnosis of the air conditioning system

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