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Control valves play a vital role in process production. In practical applications, control valves are prone to blockage and leakage faults. At the small control valve openings, the vibration signals exhibit the drawbacks of significant interference and weak fault characteristics, which causes subpar fault diagnosis performance. To address the issue...
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Citations
... Li et al. [8] achieved vibration fault feature extraction of rolling bearings based on VMD. However, although VMD can effectively separate mixed multi-component signals, its decomposition effect is greatly influenced by the number of modes and penalty factors [9]. During the working process of the cutting head, the energy structure of the vibration signal will change with the change of the fault form of the cutting head. ...
Cutting head is a part of the roadheader prone to failure. Its health monitoring and fault diagnosis can ensure the safe and efficient operation of the roadheader. The cutting head has been under the complex working condition of variable load for a long time. Therefore, the vibration signal of the cutting head has non-linear time-varying modulation characteristics, which causes serious interference to the fault identification of the cutting head. Hence, this study proposes a feature extraction method based on rime algorithm (RIME) optimized variational mode decomposition (VMD) and refined composite multi-scale fluctuation dispersion entropy (RCMFDE). Meanwhile, it employs the advantages of Deep Belief Networks (DBN) in nonlinear high-dimensional data processing to classify and recognize the failure modes of the cutting head. Firstly, the paper obtains the optimal parameter combinations of the VMD algorithm through the RIME algorithm. It uses the optimized VMD to adaptively decompose the cutting vibration signal and get a series of intrinsic modal functions (IMF). The paper combined the correlation coefficients to screen the optimal eigencomponent. Then, for the feature IMF component, this study explores the impact of the embedding dimension and category number of RCMFDE on the feature extraction performance. It calculates the RCMFDE of vibration signals from different cutting heads and uses them as the eigenvector. Finally, the paper uses the DBN model to train and test the cutting vibration features and realize the fault pattern recognition of the cutting head. The simulation and experimental results show that the proposed method can effectively extract the fault characteristics of cutting vibration signals, and the recognition accuracy reaches 99.164%. Compared with other methods, it has better recognition accuracy and robustness and can provide a new research idea for monitoring the health status of the cutting head.
... There is a fascinating habit among dung beetles in which they form dung balls and then roll them to the ideal position. For the dung ball to roll straight, the dung beetle relies on celestial cues [21]. The dung beetles update their position as they roll the ball. ...
... The absolute difference between dimensions X r2 and X r3 forces uniform movement, reducing population diversity and hindering search agent evolution. ( 1) 1, 2, 3 t r r r X t X X X r r r i (21) where 1 r , 2 r , 3 r is a randomly selected index. Figure 6. ...
... Update the new position according to formulas (20) and (21) to generate a new solution. The core of the proposed IDBO-SVM algorithm is to utilize the global search ability and classification performance of the IDBO algorithm to complete accurate fault classification. ...
Due to high probability of blade faults, bearing faults, sensor faults, and communication faults in pitch systems during the long-term operation of wind turbine components, and the complex operation environment which increases the uncertainty of fault types, this paper proposes a fault diagnosis method for wind turbine components based on an Improved Dung Beetle Optimization (IDBO) algorithm to optimize Support Vector Machine (SVM). Firstly, the Halton sequence is initially employed to populate the population, effectively mitigating the issue of local optima. Secondly, the subtraction averaging optimization strategy is introduced to accelerate the dung beetle algorithm in solving complex problems and improve its global optimization ability. Finally, incorporating smooth development variation helps improve data quality and the accuracy of the model. The experimental results indicate that the IDBO-optimized SVM (IDBO-SVM) achieves a 96.7% fault diagnosis rate for wind turbine components. With the proposed IDBO-SVM method, fault diagnosis of wind turbine components is more accurate and stable, and its practical application is excellent.
To solve the problem of difficulty in extracting and identifying fault types during turbine rotor operation, a fault diagnosis method based on improved subtraction mean optimizer (NGSABO) algorithm to optimize variational mode decomposition (VMD) and CNN-BiLSTM neural network is proposed. Firstly, three improvements are made to the subtraction average optimizer algorithm. Secondly, the optimal VMD parameter combination of NGSABO adaptive selection mode decomposition number K and penalty factor α is used to decompose the rotor fault signal, and the minimum sample entropy is used as the fitness function for feature extraction. Combining convolutional neural network and bidirectional long short-term memory network to identify and classify features. Compared with other methods, this method has outstanding performance in the diagnosis of single and coupled rotor faults. The accuracy of fault diagnosis reaches 98.5714%, which has good practical application value.
