A novel methodology for fault size estimation of ball bearings using stator current signal

Wang, Chen; Wang, Min; Yang, Bin; Song, Kaiyu; Zhang, Yiling; Liu, Liming

doi:10.1016/j.measurement.2020.108723

Cited by 16 publications

(15 citation statements)

References 48 publications

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“…troller to suppress the velocity fluctuations. The envelope spectrum analysis method was used to detect the fault characteristics (Ming Yang et al, 2020) [Error! Reference source not found.].…”

Section: Artificial Intelligence Signal Analysis Methodsmentioning

confidence: 99%

“…In another study, an induction motor (IM)-related, electromechanical magnetic coupling model based on MWFA was used to extract the harmonic component and amplitude of a fault excitation in the stator current. Then a new evaluation index, FHD (fault excitation harmonic distortion), was used to describe the specific relationship between the fault size and the severity of fault excitation harmonic distortion (Wang, Chen et al, 2021) [ 74 ]. Fault diagnosis by analyzing current characteristics (Victor Avina-Corral et al, 2021) [ 75 ].…”

Section: Detection Methods Based On One-dimensional Signalsmentioning

confidence: 99%

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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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Section: Artificial Intelligence Signal Analysis Methodsmentioning

confidence: 99%

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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“…On the other side, the identification of faults in bearings can be also performed through classical approaches based on MCSA (Motor-Current Signature Analysis); in this sense, in [24], stator current-based bearing fault diagnosis using fractional wavelet denoising and deep learning algorithms is presented; although through the proposed method is possible to identify the damage of outer and inner races of bearings, the application of the methods is focused on the detection of a unique fault severity. Likewise, in [25], a novel methodology for fault size estimation of ball bearings using stator current signals is proposed, where the fault-excited harmonic components and amplitudes in stator current are estimated, and the FHD (Faultexcited Harmonic Distortion) evaluated to achieve the fault size estimation. Even though different fault sizes can be detected, the estimation of undesired frequency components that may appear overlapped with the fault-related components may lead to infeasible detections.…”

Section: Introductionmentioning

confidence: 99%

Condition Monitoring Method for the Detection of Fault Graduality in Outer Race Bearing Based on Vibration-Current Fusion, Statistical Features and Neural Network

et al. 2021

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Bearings are the elements that allow the rotatory movement in induction motors, and the fault occurrence in these elements is due to excessive working conditions. In induction motors, electrical erosion remains the most common phenomenon that damages bearings, leading to incipient faults that gradually increase to irreparable damages. Thus, condition monitoring strategies capable of assessing bearing fault severities are mandatory to overcome this critical issue. The contribution of this work lies in the proposal of a condition monitoring strategy that is focused on the analysis and identification of different fault severities of the outer race bearing fault in an induction motor. The proposed approach is supported by fusion information of different physical magnitudes and the use of Machine Learning and Artificial Intelligence. An important aspect of this proposal is the calculation of a hybrid-set of statistical features that are obtained to characterize vibration and stator current signals by its processing through domain analysis, i.e., time-domain and frequency-domain; also, the fusion of information of both signals by means of the Linear Discriminant Analysis is important due to the most discriminative and meaningful information is retained resulting in a high-performance condition characterization. Besides, a Neural Network-based classifier allows validating the effectiveness of fusion information from different physical magnitudes to face the diagnosis of multiple fault severities that appear in the bearing outer race. The method is validated under an experimental data set that includes information related to a healthy condition and five different severities that appear in the outer race of bearings.

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“…The main disadvantage for current-based BCM are the poor signal-to-noise ratio (SNR), information losses in the magnetic field, saturation harmonics, electrical faults, and interference . Moreover, conventional signal processing techniques for denoising and extracting information from vibration-based signals may work inadequately in current-based signals (Wang et al, 2021). In summary, for the same signal processing technique, the current-based BCM has less accessible information (e.g., indirect measure) and more feature extraction complexity (e.g., poor SNR).…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Bearing Condition Monitoring and Damage Detection Using Machine Learning Algorithms

Barcelos

Mazzoni

Cardoso

2021

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

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Bearing condition monitoring (BCM) and damage identification are usually performed by vibration-based signals and supervised learning algorithms. However, this approach is impractical in many industrial facilities because some industrial motors are unable to provide access to vibration-based signals or they are prevented from performing their functions under damaged conditions. In this context, this work employs a density-based and fractal approach to extract features from current-based signals. These features create an unlabelled database that feeds a fuzzy c-means algorithm to perform BCM and the support vector machines to classify bearing damages in an unsupervised learning approach. Tests with several bearing damages under various load and speed conditions are reported, presenting promising results.

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A novel methodology for fault size estimation of ball bearings using stator current signal

Cited by 16 publications

References 48 publications

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

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

Condition Monitoring Method for the Detection of Fault Graduality in Outer Race Bearing Based on Vibration-Current Fusion, Statistical Features and Neural Network

A New Approach for Bearing Condition Monitoring and Damage Detection Using Machine Learning Algorithms

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