Fault Diagnosis of Motor Bearing Based on Speed Signal Kurtosis Spectrum Analysis

Ren, Biying; Yang, Ming; Na, Chai; Li, Yunsong; Xu, Dianguo

doi:10.1109/icems.2019.8921477

Cited by 7 publications

(5 citation statements)

References 5 publications

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“…Additionally, in a speed closed-loop control system, the effect of speed and load torque change on the amplitude of characteristic diagnostic symptoms was tested. In [139], the speed signal was subjected to FFT and kurtosis analysis and compared with the results obtained from mechanical vibration and stator current analysis over a wide range of PMSM motor speeds. The study showed that the kurtosis spectrum analysis of the speed signal can achieve similar results to those obtained for the vibration method.…”

Section: Signal Analysis Methods For Mechanical Faults Detection a Be...mentioning

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: Signal Analysis Methods For Mechanical Faults Detection a Be...mentioning

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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“…It can extract and analyze the rotor speed and study the motor speed. Based on motor-speed-kurtosis spectrum analysis, a diagnostic method which improves the signal-to-noise ratio (SNR) was presented (Boyang et al, 2019) [ 88 ]. The speed signal is one of the indispensable signals in mechanical motion and has high research value.…”

Section: Detection Methods Based On One-dimensional Signalsmentioning

confidence: 99%

A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors

Yan

Yang

et al. 2022

Sensors

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As a precision mechanical component to reduce friction between components, the rolling bearing is widely used in many fields because of its slight friction loss, strong bearing capacity, high precision, low power consumption, and high mechanical efficiency. This paper reviews several excellent kinds of study and their relevance to the fault detection of rolling bearings. We summarize the fault location, sensor types, bearing fault types, and fault signal analysis of rolling bearings. The fault signal types are divided into one-dimensional and two-dimensional images, which account for 40.14% and 31.69%, respectively, and their classification is clarified and discussed. We counted the proportions of various methods in the references cited in this paper. Among them, the method of one-dimensional signal detection with external sensors accounted for 3.52%, the method of one-dimensional signal detection with internal sensors accounted for 36.62%, and the method of two-dimensional signal detection with external sensors accounted for 19.72%. The method of two-dimensional signal detection with internal sensors accounted for 11.97%. Among these methods, the highest detection rate is 100%, and the lowest detection rate is more than 70%. The similarities between the different methods are compared. The research results summarized in this paper show that with the progress of the times, a variety of new and better research methods have emerged, which have sped up the detection and diagnosis of rolling bearing faults. For example, the technology using artificial intelligence is still developing rapidly, such as artificial neural networks, convolutional neural networks, and machine learning. Although there are still defects, such methods can quickly discover a fault and its cause, enrich the database, and accumulate experience. More and more advanced techniques are applied in this field, and the detection method has better robustness and superiority.

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“…The frequency values of these harmonics for a few examples of motor operating conditions are presented in Table 1. The second input vector was developed based on the HT analysis of the vibration acceleration signal and next FFT of the obtained envelope (ENV), calculated according Equation (12). After applying the Hilbert transformation of the signal, there was a significant increase in the amplitude values of the harmonics with frequencies characteristic for individual failures.…”

Section: Description Of the Laboratory Set-upmentioning

confidence: 99%

“…Equation (12). After applying the Hilbert transformation of the signal, there was a significant increase in the amplitude values of the harmonics with frequencies characteristic for individual failures.…”

Section: Description Of the Laboratory Set-upmentioning

confidence: 99%

“…More often, authors compare the possibility of detecting rolling bearing damages using several diagnostic signals. For example, in Reference [12], three diagnostic signals were tested: stator current, rotor speed, and mechanical vibrations. The symptoms were extracted using FFT and the kurtosis spectrum analysis, which improves the signal-to-noise ratio.…”

Section: Introductionmentioning

confidence: 99%

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Effectiveness Analysis of PMSM Motor Rolling Bearing Fault Detectors Based on Vibration Analysis and Shallow Neural Networks

2021

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Permanent magnet synchronous motors (PMSMs) are becoming more popular, both in industrial applications and in electric and hybrid vehicle drives. Unfortunately, like the others, these are not reliable drives. As in the drive systems with induction motors, the rolling bearings can often fail. This paper focuses on the possibility of detecting this type of mechanical damage by analysing mechanical vibrations supported by shallow neural networks (NNs). For the extraction of diagnostic symptoms, the Fast Fourier Transform (FFT) and the Hilbert transform (HT) were used to obtain the envelope signal, which was subjected to the FFT analysis. Three types of neural networks were tested to automate the detection process: multilayer perceptron (MLP), neural network with radial base function (RBF), and Kohonen map (self-organizing map, SOM). The input signals of these networks were the amplitudes of harmonic components characteristic of damage to bearing elements, obtained as a result of FFT or HT analysis of the vibration acceleration signal. The effectiveness of the analysed NN structures was compared from the point of view of the influence of the network architecture and various parameters of the learning process on the detection effectiveness.

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Fault Diagnosis of Motor Bearing Based on Speed Signal Kurtosis Spectrum Analysis

Cited by 7 publications

References 5 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

A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors

Effectiveness Analysis of PMSM Motor Rolling Bearing Fault Detectors Based on Vibration Analysis and Shallow Neural Networks

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