Comparative Analysis of High Frequencies for the Broken Bar Fault Diagnosis Using MCSA and Park’s Vector Demodulation

Oñate, William; Gallardo, Yessenia; Pérez, Ramón; Caiza, Gustavo

doi:10.1007/978-981-16-4884-7_10

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…The fault diagnosis of mechanical equipment needs to monitor, diagnose and predict the state of equipment to ensure the stable operation of the machine [1][2][3]. With the deepening of the depth and breadth of industrialization, the safe and stable operation of mechanical equipment and its component mechanical system is becoming more and more important in industrial production.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the generalization ability of the obtained model is weak and can only solve specific problems. In general, traditional machine learning has the following disadvantages: (1) It requires professional knowledge and a mathematical basis to design and extract features, which is greatly influenced by manual work; (2) The extracted features are shallow features with weak generalization ability; (3) Model training stage and feature extraction stage are separated, and the whole stage cannot be optimized at the same time; (4) Weak data processing ability and difficult to adapt to the background of big data.…”

Section: Introductionmentioning

confidence: 99%

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A comprehensive review of mechanical fault diagnosis methods based on convolutional neural network

Hou,

Lu,

Zhong

et al. 2023

J. vibroeng.

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Mechanical fault diagnosis can prevent the deterioration of mechanical equipment failures and is important for the stable operation of mechanical equipment. Firstly, this paper reviews three basic methods of fault diagnosis and common methods of data-driven fault diagnosis, focusing on the characteristics and advantages of deep learning and convolutional neural networks. Then, the basic structure and working principle of CNN (Convolutional Neural Networks) and some basic methods to achieve better training results are introduced. In the next place, from data processing, data fusion, sample set construction, and so on, it is reviewed that the method of fault diagnosis based on CNN and their application scenarios and advantages and disadvantages; for another, the related knowledge and concepts of transfer learning are introduced, and some current application scenarios and advantages and disadvantages of mechanical fault diagnosis techniques combining migration learning and convolutional neural networks are reviewed. Finally, the current difficulties and challenges of convolutional neural networks are discussed, and the research directions have been prospected for CNN applied to the field of fault diagnosis. Although there is quite some similar literature reviewed, this review aims to introduce the basic methods of fault diagnosis, which draw forth the basic applications of the fault diagnosis of data-driven, CNN in the domain of fault diagnosis, and the application scenarios and advantages and disadvantages of combining TL (Transfer Learning) and CNN in fault diagnosis, as well as some problems and prospects. It helps researchers to have a basic understanding of this.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A comprehensive review of mechanical fault diagnosis methods based on convolutional neural network

Hou,

Lu,

Zhong

et al. 2023

J. vibroeng.

View full text Add to dashboard Cite

show abstract

“…Instantaneous power, magnetic flux, torque, and vibrational signals are some of the other indicators used to diagnose rotor problems [15,16]. In addition, methodologies for distinguishing between broken bar defects and moment loads have engaged a considerable number of scholars in recent years [17,18]. e intricacy of the system that has to be diagnosed is also a factor.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Broken Bars in V/F Control Induction Motor Drive Using Wavelets and EEV Estimation for Electric Vehicle Applications

R¹,

Irudayaraj

Devarajan³

et al. 2022

International Transactions on Electrical Energy Systems

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The induction motor (IM) defect diagnosis has been an important field of research in recent years. The development in control circuits for IM has piqued the interest of industrialists and researchers. This paper presents a method for detecting and quantifying broken rotor bar (BRB) faults via wavelets and energy Eigen value (EEV) estimation in voltage/frequency control-fed IM. The fast Fourier transform (FFT) extracts the signal’s amplitude and frequency components, while the discrete wavelet transform (DWT) decomposes it. In this paper, the energy estimation for each level of breakdown and the method to overcome the diagnose faults are explained. The EEV of the motor current of the signal determines the fault’s severity and provides a better method for identifying the faults. The usage of a single current sensor is a gain of this technology. With a fluctuating load, we can identify the issue and the number of broken bars via online. After processing of DWT, the faulty BRB’s stator current signal is suppressed to 91% in amplitude when compared to existing techniques. Simulation and experimental results have proved that the proposed method’s stability, durability, and resilience.

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“…Among them, motor current signature analysis (MCSA) [13,14,15,16] has gained an increasing interest, because it can operate on-line, detecting different and possibly simultaneous types of fault, and can be implemented using non-invasive [17] low-cost current sensors, and fast processing algorithms, especially the fast Fourier transform (FFT). MCSA is based on the detection of a set of harmonic components generated by each type fault, whose frequencies constitute a unique fault signature [18]. These frequencies, for some of the most frequent IM faults [19], are given in Table 1, where p is the number of pole pairs, f 1 is the supply frequency, s is the rotor slip, and N b , D b , D c and β are the parameter of the bearing (number of balls, bearing and cage diameter, and contact angle, respectively).…”

Section: Introductionmentioning

confidence: 99%

Locally optimized chirplet spectrogram for condition monitoring of induction machines in transient regime

et al. 2022

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Comparative Analysis of High Frequencies for the Broken Bar Fault Diagnosis Using MCSA and Park’s Vector Demodulation

Cited by 4 publications

References 16 publications

A comprehensive review of mechanical fault diagnosis methods based on convolutional neural network

A comprehensive review of mechanical fault diagnosis methods based on convolutional neural network

Diagnosis of Broken Bars in V/F Control Induction Motor Drive Using Wavelets and EEV Estimation for Electric Vehicle Applications

Locally optimized chirplet spectrogram for condition monitoring of induction machines in transient regime

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