Sensorless harmonic speed control and detection of bearing faults in repetitive mechanical systems

Phung, Van Trang; Pacas, Mario

doi:10.1109/ifeec.2017.7992294

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…However, the virtual signals, being a part of the control structure have been also used to diagnose mechanical-type damages. Motor bearing inner-and outer-race faults can be distinguished using the approach shown in [177]. Because it is applied for a general repetitive mechanical mechanism, a special harmonic speed controller is proposed instead of a traditional PI regulator.…”

Section: Mechanical Faultsmentioning

confidence: 99%

Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges

et al. 2022

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The issues of monitoring and fault diagnosis of drives with permanent magnet synchronous motors (PMSM) are currently very topical due to the increasing use of these drives in safety-critical devices. Every year, more and more articles on this subject are published. Therefore, the aim of this article is to update the overview of diagnostic methods and techniques for PMSM drives. Each of the main chapters of the article focuses on a specific element of the drive system (motor, power converter, measuring sensors), with particular emphasis on the components of the motor (stator windings, magnets, bearings, rotor). The main sections on PMSM fault diagnosis are divided according to the type of methods used to obtain the symptoms of the damage. In addition, a review of methods using the analysis of control structure signals for the diagnosis of damage to a vector-controlled motor is presented, as well as the latest achievements of researchers in the field of shallow and deep neural networks for the detection and classification of PMSM drives failures. Based on the presented literature analyses, some development trends and challenges related to the development of diagnostics and fault-tolerant control of PMSM drives are discussed in the conclusion part.

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Section: Mechanical Faultsmentioning

confidence: 99%

Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges

et al. 2022

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“…A dangerous working environment or artificial vision can hardly meet the requirements of using machine vision in the field, which can not only eliminate the interference of human subjective factors but also reduce the detection and classification errors. The use of machine vision for large-scale repetitive tasks can also significantly improve productivity and facilitate the automatic processing of relevant information [1].…”

Section: Introductionmentioning

confidence: 99%

Image feature processing based on fuzzy recognition

2022

2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022)

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Machine vision inspection combines a fast and accurate computer with image processing technology, which is of great help to improve the ability of intelligent image recognition. The ultimate goal of the recognition and classification system is to identify the detected defects quickly and accurately. The defects with obvious characteristics can be identified in a short time due to their great differences. Due to the imperfect recognition technology and the low robustness of the algorithm, the detection is inaccurate and the difficulty of the recognition effect is increased. In this paper, based on the targets with similar characteristics, the membership function of the target to be recognized is constructed after extracting the characteristic parameters of the recognized target. The characteristic function of the fuzzy set is established to calculate the membership degree of the target sample belonging to the fuzzy set, and finally, the classification of the characteristic sample is recognized according to the maximum membership principle. The experimental results show that the method of this paper is based on pattern recognition and has good results, which has guiding significance for all kinds of image feature recognition and processing and will greatly reduce the intensity and workload of manual processing.

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“…Using signals from the control structure, the authors of [27] determined the active and reactive power consumed by the PMSM motor during different operating states based on the current and voltage vector in the 𝑑-𝑞 reference system. Also, the use of signals from the control structure to diagnose other (high resistance connection, misalignment, damaged bearing) faults in the PMSM is presented in [14] and [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Archives of Electrical Engineering

2022

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Modern drives with Permanent Magnet Synchronous Motors (PMSMs) require both efficient control structure to ensure excellent dynamics and effective diagnostic algorithms to detect the motor faults that can occur. This paper shows the combination of both mentioned aspects -the direct-axis based signals of the Field Oriented Control (FOC) structure are proposed as diagnostic signals to allow diagnosing the interturn short-circuit failure that can appear inside stator windings. The amplitudes of second order harmonics are selected as the fault indicators. Different modelling methods are analysed and compared in detail in this paper: an analytical mathematical model, a Finite Element Method (FEM)based model and next verified using a laboratory setup. The results obtained using all the mentioned models proved that the proposed fault indices are increasing significantly with the number of shorted turns and are independent on the load torque level.

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Sensorless harmonic speed control and detection of bearing faults in repetitive mechanical systems

Cited by 6 publications

References 10 publications

Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges

Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges

Image feature processing based on fuzzy recognition

Archives of Electrical Engineering

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