Research and Development of a Chaotic Signal Synchronization Error Dynamics-Based Ball Bearing Fault Diagnostor

Kuo, Yan-Fu; Hsieh, Chin-Tsung; Yau, Her‐Terng; Li, Yu-Chung

doi:10.3390/e16105358

Cited by 20 publications

(17 citation statements)

References 27 publications

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“…The dynamic error of the system can be obtained from subtracting the rated ideal signal system from the testing signal system. The above two formulas are rewritten in matrix form [30], [31] to facilitate subtraction of the two systems, as shown in (11) and (12).…”

Section: Dynamic Error Of the Fractional Order The Chen-lee Chaotic Smentioning

confidence: 99%

An Automatic Intelligent Diagnostic Mechanism for the Milling Cutter Wear

Jian

et al. 2020

IEEE Access

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Section: Dynamic Error Of the Fractional Order The Chen-lee Chaotic Smentioning

confidence: 99%

An Automatic Intelligent Diagnostic Mechanism for the Milling Cutter Wear

Jian

et al. 2020

IEEE Access

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“…Traditional Lorenz chaotic system can be expressed by Equation (1) and the chaos synchronization method for fault diagnosis can be found in the references [35] and will not be discussed here:…”

Section: Master-slave Chaos Dynamic Error System and Extenics Engineementioning

confidence: 99%

Blade Fault Diagnosis in Small Wind Power Systems Using MPPT with Optimized Control Parameters

Chen

Hung

2015

Energies

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Abstract:A systematic experiment verification of Chaos Embedded Sliding Mode Extremum Seeking Control for maximum power point tracking and a method for detecting possible faults in small wind turbine systems in advance are proposed in this paper. The chaotic logistic map is used to replace the random function in the particle swarm optimization algorithm for faster searching the optimal control parameter U . From the experimental results, it is verified that the Chaos Embedded Sliding Mode Extremum Seeking Control scheme has a better dynamic response than traditional Extremum Seeking Control scheme and Hill-Climbing Search scheme for maximum power point tracking. In the proposed scheme for fault detection, a chaotic synchronization method is used to transform the maximum power point tracking signal into a chaos synchronization error distribution diagram. It is then taken as the characteristic for fault diagnosis purposes. Finally, an extension theory pattern recognition technique is applied to diagnose the fault. Notably, the use of the chaotic dynamic errors as the fault diagnosis characteristic reduces the number of extracted features required, and therefore greatly reduces both the computation time and the hardware implementation cost. From the experimental results, it is shown that the fault diagnosis rate of the proposed method exceeds 98% not only in non-real-time but also in real-time of faults detection of the blades.

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“…The key to fault diagnosis is choosing the appropriate method to identify the type, quantity, location, and severity of fault based on the fault mechanism [48,49]. Extracting mechanical fault feature information from the fault vibration signal using a suitable signal analysis method is one of the most important steps.…”

Section: Fault Diagnosis Methodsmentioning

confidence: 99%

A New Feature Extraction Method Based on EEMD and Multi-Scale Fuzzy Entropy for Motor Bearing

Zhao

Sun

Deng

et al. 2016

Entropy

183

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Abstract:Feature extraction is one of the most important, pivotal, and difficult problems in mechanical fault diagnosis, which directly relates to the accuracy of fault diagnosis and the reliability of early fault prediction. Therefore, a new fault feature extraction method, called the EDOMFE method based on integrating ensemble empirical mode decomposition (EEMD), mode selection, and multi-scale fuzzy entropy is proposed to accurately diagnose fault in this paper. The EEMD method is used to decompose the vibration signal into a series of intrinsic mode functions (IMFs) with a different physical significance. The correlation coefficient analysis method is used to calculate and determine three improved IMFs, which are close to the original signal. The multi-scale fuzzy entropy with the ability of effective distinguishing the complexity of different signals is used to calculate the entropy values of the selected three IMFs in order to form a feature vector with the complexity measure, which is regarded as the inputs of the support vector machine (SVM) model for training and constructing a SVM classifier (EOMSMFD based on EDOMFE and SVM) for fulfilling fault pattern recognition. Finally, the effectiveness of the proposed method is validated by real bearing vibration signals of the motor with different loads and fault severities. The experiment results show that the proposed EDOMFE method can effectively extract fault features from the vibration signal and that the proposed EOMSMFD method can accurately diagnose the fault types and fault severities for the inner race fault, the outer race fault, and rolling element fault of the motor bearing. Therefore, the proposed method provides a new fault diagnosis technology for rotating machinery.

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Research and Development of a Chaotic Signal Synchronization Error Dynamics-Based Ball Bearing Fault Diagnostor

Cited by 20 publications

References 27 publications

An Automatic Intelligent Diagnostic Mechanism for the Milling Cutter Wear

An Automatic Intelligent Diagnostic Mechanism for the Milling Cutter Wear

Blade Fault Diagnosis in Small Wind Power Systems Using MPPT with Optimized Control Parameters

A New Feature Extraction Method Based on EEMD and Multi-Scale Fuzzy Entropy for Motor Bearing

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