Fault diagnosis of railway freight car wheelset based on deep belief network and cuckoo search algorithm

2021 ◽  
pp. 095440972110291
Author(s):  
Honghui Li ◽  
Hongkun Wang ◽  
Ziwen Xie ◽  
Mengqi He
Keyword(s):  
Fault Diagnosis ◽  
High Speed ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Deep Belief Network ◽  
Belief Network ◽  
Railway Freight ◽  
Hybrid Simulated Annealing ◽  
Technical Issues

As the key running part of the railway freight transportation system, the wheel not only bears the load of the vehicle, but also ensures the running and steering of the car body on the rails. The frequent high-speed friction with the rail and brake is the main reason for early failure of wheelset tread. Therefore, real-time status monitoring and early fault diagnosis of wheel treads have become key technical issues that must be solved in the reform of the railway freight maintenance system. In this paper, an adaptive hybrid Simulated Annealing Cuckoo Search algorithm (SA-ACS) is proposed and applied to the Deep Belief Network (DBN). The SA-ACS-DBN algorithm is used to improve the training speed and convergence accuracy of the diagnosis model. Finally, it is found through the comparison experiment of wheel tread fault data that the data results prove the feasibility of the SA-ACS-DBN model in the application of wheelset fault diagnosis.

scholarly journals A Novel Cuckoo Search Optimized Deep Auto-Encoder Network-Based Fault Diagnosis Method for Rolling Bearing

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jinyu Tong ◽  
Jin Luo ◽  
Haiyang Pan ◽  
Jinde Zheng ◽  
Qing Zhang
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Search Algorithm ◽  
Feature Learning ◽  
Cuckoo Search ◽  
Rolling Bearing ◽  
Cuckoo Search Algorithm ◽  
Learning Ability ◽  
Penalty Term ◽  
Experimental Data Analysis

To enhance the performance of deep auto-encoder (AE) under complex working conditions, a novel deep auto-encoder network method for rolling bearing fault diagnosis is proposed in this paper. First, multiscale analysis is adopted to extract the multiscale features from the raw vibration signals of rolling bearing. Second, the sparse penalty term and contractive penalty term are used simultaneously to regularize the loss function of auto-encoder to enhance the feature learning ability of networks. Finally, the cuckoo search algorithm (CS) is used to find the optimal hyperparameters automatically. The proposed method is applied to the experimental data analysis. The results indicate that the proposed method could more effectively distinguish fault categories and severities of rolling bearings under different working conditions than other methods.

scholarly journals A novel nonlinear observer for fault diagnosis of induction motor

2020 ◽  
Vol 14 ◽  
pp. 174830262092272
Author(s):  
Lingzhi Yi ◽  
Yue Liu ◽  
Wenxin Yu ◽  
Jian Zhao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Induction Motor ◽  
Bp Neural Network ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Adaptive Observer ◽  
Nonlinear Observer ◽  
Feedback Gain

In order to accurately diagnose the fault of induction motor, a fault diagnosis of nonlinear observer method based on BP neural network and Cuckoo Search algorithm is proposed. It is a new method which mixes analytical model and artificial neural network; firstly, the induction motor model is divided into linear and nonlinear parts, and BP neural network is used to approximate the nonlinear part. Then an adaptive observer is established, in which a simple and effective method for selecting the feedback gain matrix is offered. Cuckoo Search algorithm is utilized to improve the convergence speed and approximation accuracy in BP Neural Network. Compared with some other algorithms, the simulation results show that the proposed method has higher prediction accuracy. The designed nonlinear observer can estimate the current and speed accurately. Finally, the experiment of winding fault is implemented, and the online fault detection of induction motor is realized by analyzing the current residual errors.

Vibration Fault Diagnosis for Hydroelectric Generating Units Based on the Modified Cuckoo Search Algorithm and Evidence Theory

2019 ◽  
Vol 13 (3) ◽  
pp. 281-288
Author(s):  
Jiatang Cheng ◽  
Li Ai ◽  
Yan Xiong
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Search Algorithm ◽  
Evidence Theory ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
System Structure ◽  
Hybrid Scheme ◽  
Independent Evidence

Background: In view of the complex system structure and uncertain factors in the fault diagnosis of hydroelectric generating units (HGU), it is a difficult problem to design the diagnosis method rationally. Objective: An attempt is made to employ multi-source feature information to improve the accuracy of fault diagnosis, and the effectiveness of the proposed scheme is verified by using a diagnostic example. Methods: Through the research on recent papers and patents related to fault diagnosis of the HGU, a hybrid scheme based on the modified cuckoo search algorithm, back-propagation (BP) neural network and evidence theory are proposed. For this modified version named cuckoo search with fitness information (CSF), the step factor is adaptively tuned using the fitness value. Next, three diagnostic models based on BP neural network trained by CSF are used for primary diagnosis. These diagnostic results are then used as the independent evidence, and the fusion decision is made by using evidence theory. Results: Experimental results show that CSF algorithm is better than the original cuckoo search (CS) and its three variants, and the hybrid method has the highest diagnostic accuracy. Conclusion: The proposed hybrid scheme has strong robustness and fault tolerance, and can effectively classify the vibration faults of hydroelectric generating units

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