Current Sensor Fault Diagnosis and Tolerant Control for VSI-Based Induction Motor Drives

The research work in this paper belongs to the application of granular computing, graph theory and its application in fault detection and diagnosis. It is a cross cutting and frontier research field in computer science, information science and graph theory. The results of this paper are of great significance to the application of the fault detection and diagnosis of the ocean boilers system. This research combines granular computing theory and signed directed graph, and proposes a new method of fault diagnosis, and applies it to the fault diagnosis of ocean ship boiler system.

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“…Transforming the rules in the traditional rule library into particle language to the decision rule particles in the grain Library [20].…”

Section: Fig 3 Illustration Of the Refining Examples Based On Granumentioning

confidence: 99%

Research on SDG Fault Diagnosis of Ocean Shipping Boiler System Based on Fuzzy Granular Computing Under Data Fusion

Zhu

Geng

2018

Polish Maritime Research

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“…In consequence, many studies have been carried out to evaluate the occurrence of faults in TIM [3][4][5]. Among then, the non-destructive techniques (NDTs) emerge as a successful alternative for this purpose.…”

Section: Introductionmentioning

confidence: 99%

Undervoltage Identification in Three Phase Induction Motor Using Low-Cost Piezoelectric Sensors and STFT Technique

Carvalho

Lucas

Rocha

et al. 2019

The 6th International Electronic Conference on Sensors and Applications

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Three-phase induction motors (IMs) are electrical machines used on a large scale in industrial applications because they are versatile, robust and low maintenance devices. However, IMs are significantly affected when fed by unbalanced voltages. Prolonged operation under voltage unbalance (VU) conditions degrades performance and shortens machine life by producing imbalances in stator currents that abnormally raise winding temperature. With the development of new technologies and research on non-destructive techniques (NDT) for fault diagnoses in IMs, it is relevant to obtain economically accessible, efficient and reliable sensors capable of acquiring signals that allow the identification of this type of failure. The objective of this study is to evaluate the application of low-cost piezoelectric sensors in the acquisition of acoustic emission (AE) signals and the identification of VU through the analysis of short-term Fourier transform (STFT) spectrograms. The piezoelectric sensor makes NDT feasible, as it is an affordable and inexpensive component. In addition, STFT allows time-frequency analyses of acoustic emission signals. In this NDT, two sensors were coupled on both sides of an induction motor frame. The AE signals obtained during the IM operation were processed and the resulting spectrograms were analyzed to identify the different VU levels. After comparing the AE signals for faulty conditions with the signals for the IM operating at balanced voltages, it was possible to obtain a desired identification that confirmed the successful application of low-cost piezoelectric sensors for VU condition detection in three-phase induction machines.

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“…For open-circuit faults occurred in electrical traction drive system, [9] presents another model-based methodology, using the mixed logical dynamic model and residual generation. In [10], three independent observers for voltage source inverters (VSI) are integrated, taking a-phase, b-phase, and c-phase current as inputs. The observers are capable to detect and localize the faults, and switch the system to tolerant vector control mode when only one healthy sensor is available.…”

Section: Background and Motivationmentioning

confidence: 99%

“…Apart from IGBT open-circuit fault, sensor fault is also one of the main concerns in this thesis. Generally, there are four common types of sensor faults according to the sensor measurement output: stuck fault, drift fault, gain fault, and noise fault, which are expressed mathematically in (2-16) - (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19).…”

Section: Sensor Fault Analysismentioning

confidence: 99%

“…To complete the sliding-window scheme, the definition of credibility should be proposed. As discussed above, ELM/RVFL multi-classification rule is concluded as: (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14) where G refers to the outlier evaluation value, µ denotes the mean, and σ denotes standard deviation of output nodes value, respectively. As shown in the Fig 3.8(a), if the maximum value is an outlier, the credibility G is high and the result is classified as a credible output.…”

Section: The Design Of Sliding-window Classifiermentioning

confidence: 99%

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Data-driven fault diagnosis in the converter system

Xia¹

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Firstly, the author would like to extend sincere appreciation for his supervisor, Asst. Prof. Xu Yan, for his patient advice, guidance and support. Enlightened by inspired communication with Prof. Xu, the novel idea can be generated in this research work. Besides, Prof. Xu's rigorous attitude of research has a great influence on the author, reshaping the author's learning and living styles. Moreover, the author truly appreciates Dr. Gou Bin, Research Fellow of EEE, who has provided considerable instruction and help, when author confronts difficulty. Those valuable advices solved a great deal of author's problems throughout the research work. Finally, yet importantly, the author would like to show his gratitude for family, which gives the author mental and financial support, as solid backing. In addition, the author also wants to thank his friends, who share pleasant time with each other.

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Current Sensor Fault Diagnosis and Tolerant Control for VSI-Based Induction Motor Drives

Cited by 147 publications

References 24 publications

Research on SDG Fault Diagnosis of Ocean Shipping Boiler System Based on Fuzzy Granular Computing Under Data Fusion

Research on SDG Fault Diagnosis of Ocean Shipping Boiler System Based on Fuzzy Granular Computing Under Data Fusion

Undervoltage Identification in Three Phase Induction Motor Using Low-Cost Piezoelectric Sensors and STFT Technique

Data-driven fault diagnosis in the converter system

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