Neural Network-Based Diagnostic Tool for Detecting Stator Inter-Turn Faults in Line Start Permanent Magnet Synchronous Motors

Maraaba, Luqman S.; Al-Hamouz, Z.; Milhem, Abdulaziz S.; Abido, M. A.

doi:10.1109/access.2019.2923746

Cited by 30 publications

(21 citation statements)

References 47 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to evaluate and compare the performance metrics of the proposed model with other models, two learning models are selected for comparison purposes with the same dataset. Table 3 shows a brief comparison between the proposed model and the MLFFNN that was introduced in [30] and [31]. The proposed model outperforms MLFFNN in terms of the accuracy and number of classes that can be discovered without using any pre-processing stages.…”

Section: % and 100%mentioning

confidence: 99%

See 1 more Smart Citation

Convolutional Neural Network-Based Inter-Turn Fault Diagnosis in LSPMSMs

et al. 2020

Self Cite

View full text Add to dashboard Cite

Stator inter-turn fault diagnosis system for electric motors is of a considerable concern due to its significant effect on industrial production. In this paper, a new method for detecting the inter-turn fault and quantifying its severity in the line start permanent magnet synchronous motor (LSMPSM) is proposed. The new method depends on monitoring the stator current during steady-state period to detect the fault. The convolutional neural network (CNN) method is proposed to correlate the motor steady-state current with the status of the motor winding conditions and detect any presence of inter-turn faults. The data used in this study is extracted from both an experimental setup of a one-horsepower LSPMSM and the corresponding verified mathematical model through several testing cases under various loading conditions. One of the main features of the proposed technique is that it does not require separate feature extraction phase. The results indicate that the proposed technique is able to detect the inter-turn fault under different loading conditions varies from 0NM to 4NM with accuracy of 97.75% for all defined fault levels. The use of steady-state current for fault detection regardless of motor load enables the proposed technique to detect the fault online without disturbing the system functionality and reliability as well as without adding any extra hardware to the system. INDEX TERMS Convolutional neural network (CNN), diagnosis, fault detection, inter-turn fault, LSPMSM.

show abstract

Section: % and 100%mentioning

confidence: 99%

“…The harmonic components of stator current were used as inputs to the ANN. The detection of inter-turn fault in LSPMSM is recently investigated in [30], [31] where MLFFNN was used in the detection process. The input is a set of time-domain and frequency-domain statistical features.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network-Based Inter-Turn Fault Diagnosis in LSPMSMs

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The field winding leakage flux linkage is essential to evaluate its energy buffer functionality, it can be calculated as (14). The average motor field flux linkage can be represented as (15). Where L f is the field winding self-inductance.…”

Section: B Steady State Analysismentioning

confidence: 99%

“…by information integration includes motor position [6]- [8], inertia [9], [10], inductance [11], [12], fault status [13]- [15] etc. It eliminates the requirement of additional sensors, thus reducing the cost and the volume.…”

Section: Introductionmentioning

confidence: 99%

Magnetic Functional Integrated DC Motor Drive

Shen

2020

IEEE Access

View full text Add to dashboard Cite

This paper presents the concept of magnetic functional integrated DC motor drive. It uses the field winding of a wound-field DC motor as the inductor of its drive circuit. The field winding in a wound-field DC motor not only builds the required magnetic field for the electric-mechanic energy conversion, but also acts as the inductor to buffer the energy for the drive circuit. One magnetic component is shared to realize different functions for motor and its drive circuit, simultaneously. The weight, loss and cost of the drive system can be reduced due to the saving of extra bulky inductors for the drive circuit. This concept can be applied to all DC-DC drive circuits using inductor as its energy buffer. An example of magnetic functional integrated DC motor drive using the buck-boost converter is used to explain the concept in detail. The feasibility and the new features are verified experimentally by comparing with the conventional motor drive. INDEX TERMS Motor drive circuit, wound-field DC machines, functional integration, electromagnetic coupling, circuit modeling.

show abstract

“…Despite the efficiency of these methods to detect and locate the fault, the intensive need of accurate and intelligent incipient faults detection systems emerges, especially with the use of complex and expensive machinery in today's industry. Accordingly, several tools of artificial intelligence are employed to improve the efficiency and effectiveness of fault location [26], especially during the maintenance‐decision process, such as expert systems, fuzzy logic, genetic algorithm, and artificial neural networks (ANNs) [27]. The ANN plays an important role in developing online and offline location tools for motors [28].…”

Section: Introductionmentioning

confidence: 99%

Fault coil location of inter‐turn short‐circuit for direct‐drive permanent magnet synchronous motor using knowledge graph

Gao

et al. 2020

IET Electric Power Applications

View full text Add to dashboard Cite

Inter‐turn short‐circuit fault (ISF) degrades its reliability and may cause serious catastrophes for direct‐drive permanent magnet synchronous motor (DDPMSM). Fault location technology can reduce maintenance time, increase the mean time between failure (MTBF), and then improve the reliability of DDPMSM. Hence, an intelligent fault locating system for DDPMSM is proposed in this paper. This system proposes a knowledge graph (KG) based diagnostic tool for detection and location of the fault coil. First, the fault model of the DDPMSM with multiple branches parallel winding is established, which is used to analyze the fault characteristics of motor. Second, the BDC and BRC are proposed as the fault indicator. The effectiveness and robustness of fault indicator are analyzed. Then, the KG system are designed and established according to the relationship between fault indicator and location of fault coil. Finally, the system is tested by data under different fault and operation conditions. The test results showed that the proposed fault locating system can detect and locate the fault coil in early stage. The minimum ratio of shorted turns to branch turns that can be detected is 0.52%. The minimum ratio of shorted turns to branch turns that can be located is 6.25%.

show abstract

Neural Network-Based Diagnostic Tool for Detecting Stator Inter-Turn Faults in Line Start Permanent Magnet Synchronous Motors

Cited by 30 publications

References 47 publications

Convolutional Neural Network-Based Inter-Turn Fault Diagnosis in LSPMSMs

Convolutional Neural Network-Based Inter-Turn Fault Diagnosis in LSPMSMs

Magnetic Functional Integrated DC Motor Drive

Fault coil location of inter‐turn short‐circuit for direct‐drive permanent magnet synchronous motor using knowledge graph

Contact Info

Product

Resources

About