Bearing fault diagnosis based on EMD and improved Chebyshev distance in SDP image

Sun, Yongjian; Li, Shaohui; Wang, Xiaohong

doi:10.1016/j.measurement.2021.109100

Cited by 121 publications

(50 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In another study, SDP images were used for analysis. Bearing fault diagnosis was based on improved Chebyshev distance, IMF1, in SDP images and on empirical mode decomposition (EMD) of vibration signals (Sun, Yongjian, 2021) [ 111 ]. The specific methods are compared in Table 11 .…”

Section: Detection Methods Based On Two-dimensional Signalsmentioning

confidence: 99%

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

Yan

Yang

et al. 2022

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Detection Methods Based On Two-dimensional Signalsmentioning

confidence: 99%

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

Yan

Yang

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Features 12 to 17 are empirical mode decomposition energy entropy. EMD is a signal analysis method proposed by Dr. Huang in 1998 [45]. It is an adaptive data processing or mining method, which is very suitable for the processing of nonlinear and non-stationary time series.…”

Section: Empirical Mode Decomposition Energy Entropymentioning

confidence: 99%

A Novel End-To-End Feature Selection and Diagnosis Method for Rotating Machinery

Wang

Zhao

Zhang

et al. 2021

Sensors

View full text Add to dashboard Cite

Feature selection is to obtain effective features from data, also known as feature engineering. Traditional feature selection and predictive model learning are separated, and there is a problem of inconsistency of criteria. This paper presents an end-to-end feature selection and diagnosis method that organically unifies feature expression learning and machine prediction learning into one model. The algorithm first combines the prediction model to calculate the mean impact value (MIVs) of the feature and realizes primary feature selection for the prediction model by selecting the feature with a larger MIV. In order to take into account the performance of the feature itself, the within-class and between-class discriminant analysis (WBDA) method is proposed, and combined with the feature diversity strategy, the feature-oriented secondary selection is realized. Eventually, feature vectors obtained by two selections are classified using a multi-class support vector machine (SVM). Compared with the modified network variable selection algorithm (MIVs), the principal component analysis dimensionality reduction algorithm (PCA), variable selection based on compensative distance evaluation technology (CDET), and other algorithms, the proposed method MIVs-WBDA exhibits excellent classification accuracy owing to the fusion of feature selection and predictive model learning. According to the results of classification accuracy testing after dimensionality reduction on rotating machinery status, the MIVs-WBDA method has a 3% classification accuracy improvement under the low-dimensional feature set. The typical running time of this classification learning algorithm is less than 10 s, while using deep learning, its running time will be more than a few hours.

show abstract

“…Xu et al [21] matched the vibration signals with images through the signals collected by five sensors for detecting wind turbine health monitoring, and although the computation time increased, the accuracy was unexpectedly improved. Sun et al [22] processed the characteristic components by the SDP method and applied them to the fault diagnosis of rolling bearings, and the proposed method exhibited good stability. Since the source of the fault vibration signal is multi-directional, a sensor installed at a fixed location will not be able to fully capture the fault signal.…”

Section: Introductionmentioning

confidence: 99%

Multi-Source Information Fusion Fault Diagnosis for Gearboxes Based on SDP and VGG

Chen

Liu

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

A decision-level approach using multi-sensor-based symmetry dot pattern (SDP) analysis with a Visual Geometry Group 16 network (VGG16) fault diagnosis model for multi-source information fusion was proposed to realize accurate and comprehensive fault diagnosis of gearbox gear teeth. Firstly, the SDP technique was used to perform a feature-level fusion of the fault states of gearbox gear collected by multiple sensors, which could initially visualize the vibration states of the gear teeth in different states. Secondly, the SDP images obtained were combined with the deep learning VGG16. In this way, the local diagnostic results of each sensor can be easily obtained. Finally, the local diagnostic results of each sensor were combined with the DS evidence theory to achieve decision-level fusion, which can better realize comprehensive fault detection for gearbox gear teeth. Before fusion, the accuracies of the three sensors were 96.43%, 93.97%, and 93.28%, respectively. When sensor 1 and sensor 2 were fused, the accuracy reached 99.93%, which is 3.52% and 6.34% better than when using sensors 1 and 2, respectively, alone. When sensor 1 and sensor 3 were fused, the accuracy reached 99.96%, marking an improvement of 3.36% and 6.85% over individual use of sensors 1 and 3, respectively. When sensor 2 and sensor 3 were fused, the accuracy reached 99.40%, which is 5.78% and 6.56% better than individual use of sensors 2 and 3, respectively. When the three sensors were fused simultaneously, the accuracy reached 99.98%, which is 3.68%, 6.40%, and 7.18% better than individual use of sensors 1, 2, and 3, respectively.

show abstract

Bearing fault diagnosis based on EMD and improved Chebyshev distance in SDP image

Cited by 121 publications

References 16 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

A Novel End-To-End Feature Selection and Diagnosis Method for Rotating Machinery

Multi-Source Information Fusion Fault Diagnosis for Gearboxes Based on SDP and VGG

Contact Info

Product

Resources

About