Application of morphological filter and correlation dimension in the fault diagnosis of rotor system

2008 ◽  
Author(s):  
Biqiang Du ◽  
Guiji Tang ◽  
Songling Wang ◽  
Junjie Shi
Keyword(s):  
Fault Diagnosis ◽  
Correlation Dimension ◽  
Rotor System ◽  
Morphological Filter

Probabilistic Model-Based Fault Diagnosis of the Rotor System

2007 ◽  
Author(s):  
Jiye Shao ◽  
Rixin Wang ◽  
Jingbo Gao ◽  
Minqiang Xu
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Network Model ◽  
Rotating Machinery ◽  
Rotor System ◽  
Bayesian Network Model ◽  
Diagnosis System ◽  
Model Based ◽  
Core Components ◽  
Fault Diagnosis System

The rotor is one of the most core components of the rotating machinery and its working states directly influence the working states of the whole rotating machinery. There exists much uncertainty in the field of fault diagnosis in the rotor system. This paper analyses the familiar faults of the rotor system and the corresponding faulty symptoms, then establishes the rotor’s Bayesian network model based on above information. A fault diagnosis system based on the Bayesian network model is developed. Using this model, the conditional probability of the fault happening is computed when the observation of the rotor is presented. Thus, the fault reason can be determined by these probabilities. The diagnosis system developed is used to diagnose the actual three faults of the rotor of the rotating machinery and the results prove the efficiency of the method proposed.

Fault Diagnosis Analysis of Rotor System Based on RBF Neural Network and Dynamic Systems

2012 ◽  
Vol 460 ◽  
pp. 127-130
Author(s):  
Song He Zhang ◽  
Yue Gang Luo ◽  
Bin Wu ◽  
Bing Cheng Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
Amplitude Spectrum ◽  
Rates Of Convergence ◽  
Rotor System ◽  
Rbf Network ◽  
Characteristic Quantity ◽  
Learning Rates ◽  
Fault Characteristic

The RBF network was applied in the rotor system to realize the fault diagnosis aiming the mapping complexity between fault symptoms and fault patterns. It can overcome the problems of low learning rates of convergence and falling easily into part minimums in BP algorithm, and improve the precision of diagnosis. The normalized values of seven frequency ranges in amplitude spectrum were used as the fault characteristic quantity, the RBF network was trained to diagnose the faults of rotor system. The results show that RBF neural network is a valid method of diagnosis of mechanical failure.

scholarly journals Bearing Fault Signal Analysis Based on an Adaptive Multiscale Combined Morphological Filter

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Chun Lv ◽  
Peilin Zhang ◽  
Dinghai Wu ◽  
Bing Li ◽  
Yunqiang Zhang
Keyword(s):  
Fault Diagnosis ◽  
Signal Analysis ◽  
Morphological Filter ◽  
Wavelet Filter ◽  
Bearing Fault ◽  
Singular Value Decomposition Method ◽  
Bearing Fault Diagnosis ◽  
Important Means ◽  
Value Decomposition ◽  
Better Than

Bearing fault signal analysis is an important means of bearing fault diagnosis. To effectively eliminate noise in a fault signal, an adaptive multiscale combined morphological filter is proposed based on the theory of mathematical morphology. Both simulation and experimental results show that the adaptive multiscale combined morphological filter can remove noise more thoroughly and retain details of the fault signal better than the dual-tree complex wavelet filter, traditional morphological filter, adaptive singular value decomposition method (ASVD), and improved switching Kalman filter (ISKF). The adaptive multiscale combined morphological filter considers both positive and negative impulses in the signal; therefore, it has strong adaptability to complex noise in the environment, making it an effective new method for bearing fault diagnosis.

scholarly journals Application of Axis Orbit Image Optimization in Fault Diagnosis for Rotor System

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xinyu Pang ◽  
Jie Shao ◽  
Xuanyi Xue ◽  
Wangwang Jiang
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Fault Diagnosis ◽  
Mathematical Morphology ◽  
Bp Neural Network ◽  
Feature Vector ◽  
Rotor System ◽  
Identification Accuracy ◽  
Frequency Noise ◽  
Axis Orbit

The shape characteristic of the axis orbit plays an important role in the fault diagnosis of rotating machinery. However, the original signal is typically messy, and this affects the identification accuracy and identification speed. In order to improve the identification effect, an effective fault identification method for a rotor system based on the axis orbit is proposed. The method is a combination of ensemble empirical mode decomposition (EEMD), morphological image processing, Hu invariant moment feature vector, and back propagation (BP) neural network. Experiments of four fault forms are performed in single-span rotor and double-span rotor test rigs. Vibration displacement signals in the X and Y directions of the rotor are processed via EEMD filtering to eliminate the high-frequency noise. The mathematical morphology is used to optimize the axis orbit including the dilation and skeleton operation. After image processing, Hu invariant moments of the skeleton axis orbits are calculated as the feature vector. Finally, the BP neural network is trained to identify the faults of the rotor system. The experimental results indicate that the time of identification of the tested axis orbits via morphological processing corresponds to 13.05 s, and the identification accuracy rate ranges to 95%. Both exceed that without mathematical morphology. The proposed method is reliable and effective for the identification of the axis orbit and aids in online monitoring and automatic identification of rotor system faults.

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