Model-based stator interturn short-circuit fault detection and diagnosis in induction motors

Duvvuri, Sssr Sarathbabu; Detroja, Ketan P.

doi:10.1109/iciteed.2015.7408935

Cited by 16 publications

(4 citation statements)

References 18 publications

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“…On the one hand, model-based techniques are usually used for the identification of motor parameters in order to monitor their deviation from the nominal values [18][19][20][21]. On the other hand, in the field of signal-based methods, there are alternatives such as the Park's Vector monitoring for stator short-circuit [22,23], stator imbalance [24] and rotor bar breakage [25] detection.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Fault Detection and Diagnosis of Faulty Power Connections of Induction Machines

et al. 2021

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Induction machines have been key components in the industrial sector for decades, owing to different characteristics such as their simplicity, robustness, high energy efficiency and reliability. However, due to the stress and harsh working conditions they are subjected to in many applications, they are prone to suffering different breakdowns. Among the most common failure modes, bearing failures and stator winding failures can be found. To a lesser extent, High Resistance Connections (HRC) have also been investigated. Motor power connection failure mechanisms may be due to human errors while assembling the different parts of the system. Moreover, they are not only limited to HRC, there may also be cases of opposite wiring connections or open-phase faults in motor power terminals. Because of that, companies in industry are interested in diagnosing these failure modes in order to overcome human errors. This article presents a machine learning (ML) based fault diagnosis strategy to help maintenance assistants on identifying faults in the power connections of induction machines. Specifically, a strategy for failure modes such as high resistance connections, single phasing faults and opposite wiring connections has been designed. In this case, as field data under the aforementioned faulty events are scarce in industry, a simulation-driven ML-based fault diagnosis strategy has been implemented. Hence, training data for the ML algorithm has been generated via Software-in-the-Loop simulations, to train the machine learning models.

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Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Fault Detection and Diagnosis of Faulty Power Connections of Induction Machines

et al. 2021

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“…This technique just requires current sensors, which are available in most IM drive systems to provide good controllability. This proposed method provides better estimates for stator inter-turn fault detection as mentioned in [22]. Furthermore, a new model-based fault detection and isolation (FDI) strategy is proposed in [23] for field-oriented control, IM drives.…”

Section: Introductionmentioning

confidence: 99%

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

Harzelli¹,

Benakcha²,

Ameid³

et al. 2021

J. Model. Optim.

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In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM.

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“…The common faults of motors are winding insulation faults [8][9][10], among which the ISCF in stator coil is more common and can seriously affect the normal operation of motors [11,12]. Therefore, it is significant to detect the ISCF [13,14] and the set of faulty windings in the DRPMSM online.…”

Section: Introductionmentioning

confidence: 99%

On-Line Detection of Coil Inter-Turn Short Circuit Faults in Dual-Redundancy Permanent Magnet Synchronous Motors

Chen

Yong-huan

2018

Energies

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In the aerospace and military fields, with high reliability requirements, the dual-redundancy permanent magnet synchronous motor (DRPMSM) with weak thermal coupling and no electromagnetic coupling is needed. A common fault in the DRPMSM is the inter-turn short circuit fault (ISCF). However, research on how to diagnose ISCF and the set of faulty windings in the DRPMSM is lacking. In this paper, the structure of the DRPMSM is analyzed and mathematical models of the motor under normal and faulty conditions are established. Then an on-line ISCF detection scheme, which depends on the running modes of the DRPMSM and the average values for the difference of the d-axis voltages between two sets of windings in the latest 20 sampling periods, is proposed. The main contributions of this paper are to analyze the calculation for the inductance of each part of the stator windings and propose the on-line diagnosis method of the ISCF under various operating conditions. The simulation and experimental results show that the proposed method can quickly and effectively diagnose ISCF and determine the set of faulty windings of the DRPMSM.

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Model-based stator interturn short-circuit fault detection and diagnosis in induction motors

Cited by 16 publications

References 18 publications

Machine Learning-Based Fault Detection and Diagnosis of Faulty Power Connections of Induction Machines

Machine Learning-Based Fault Detection and Diagnosis of Faulty Power Connections of Induction Machines

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

On-Line Detection of Coil Inter-Turn Short Circuit Faults in Dual-Redundancy Permanent Magnet Synchronous Motors

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