Application of Bare-Bones Cuckoo Search Algorithm for Generator Fault Diagnosis

2021 ◽  
Vol 14 ◽  
Author(s):  
Yan Xiong ◽  
Jiatang Cheng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Search Algorithm ◽  
Back Propagation ◽  
Vibration Signal ◽  
Electrical Energy ◽  
Cuckoo Search ◽  
Fault Classification ◽  
Solution Quality ◽  
Bare Bones

Background: The generator is a mechanical device that converts other forms of energy into electrical energy. It is widely used in industrial and agricultural production and daily life. Methods: To improve the accuracy of generator fault diagnosis, a fault classification method based on the bare-bones cuckoo search (BBCS) algorithm combined with an artificial neural network is proposed. For this BBCS method, the bare-bones strategy and the modified Levy flight are combined to alleviate premature convergence. After that, the typical fault features are obtained according to the vibration signal and current signal of the generator, and a hybrid diagnosis model based on the back-propagation (BP) neural network optimized by the proposed BBCS algorithm is established. Results: Experimental results indicate that BBCS exhibits better convergence performance in terms of solution quality and convergence rate. Furthermore, the hybrid diagnosis method has higher classification accuracy and can effectively identify generator faults. Conclusion: The proposed method seems effective for generator fault diagnosis.

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

scholarly journals A Hybrid Forecasting Model Based on Empirical Mode Decomposition and the Cuckoo Search Algorithm: A Case Study for Power Load

2016 ◽  
Vol 2016 ◽  
pp. 1-28 ◽  
Author(s):  
Jiani Heng ◽  
Chen Wang ◽  
Xuejing Zhao ◽  
Jianzhou Wang
Keyword(s):  
Neural Network ◽  
Search Algorithm ◽  
Back Propagation ◽  
Cuckoo Search ◽  
Back Propagation Neural Network ◽  
Cuckoo Search Algorithm ◽  
Mode Decomposition ◽  
Power Load ◽  
Hybrid Forecasting

Power load forecasting always plays a considerable role in the management of a power system, as accurate forecasting provides a guarantee for the daily operation of the power grid. It has been widely demonstrated in forecasting that hybrid forecasts can improve forecast performance compared with individual forecasts. In this paper, a hybrid forecasting approach, comprising Empirical Mode Decomposition, CSA (Cuckoo Search Algorithm), and WNN (Wavelet Neural Network), is proposed. This approach constructs a more valid forecasting structure and more stable results than traditional ANN (Artificial Neural Network) models such as BPNN (Back Propagation Neural Network), GABPNN (Back Propagation Neural Network Optimized by Genetic Algorithm), and WNN. To evaluate the forecasting performance of the proposed model, a half-hourly power load in New South Wales of Australia is used as a case study in this paper. The experimental results demonstrate that the proposed hybrid model is not only simple but also able to satisfactorily approximate the actual power load and can be an effective tool in planning and dispatch for smart grids.

scholarly journals Secure Intelligent Vehicular Network Using Fog Computing

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 455 ◽  
Author(s):  
Samuel Kofi Erskine ◽  
Khaled M. Elleithy
Keyword(s):  
Neural Network ◽  
Ad Hoc ◽  
Search Algorithm ◽  
Fog Computing ◽  
Denial Of Service ◽  
Back Propagation ◽  
Cuckoo Search ◽  
Back Propagation Neural Network ◽  
Vehicular Network ◽  
Message Delivery

VANET (vehicular ad hoc network) has a main objective to improve driver safety and traffic efficiency. The intermittent exchange of real-time safety message delivery in VANET has become an urgent concern due to DoS (denial of service) and smart and normal intrusions (SNI) attacks. The intermittent communication of VANET generates huge amount of data which requires typical storage and intelligence infrastructure. Fog computing (FC) plays an important role in storage, computation, and communication needs. In this research, fog computing (FC) integrates with hybrid optimization algorithms (OAs) including the Cuckoo search algorithm (CSA), firefly algorithm (FA), firefly neural network, and the key distribution establishment (KDE) for authenticating both the network level and the node level against all attacks for trustworthiness in VANET. The proposed scheme is termed “Secure Intelligent Vehicular Network using fog computing” (SIVNFC). A feedforward back propagation neural network (FFBP-NN), also termed the firefly neural, is used as a classifier to distinguish between the attacking vehicles and genuine vehicles. The SIVNFC scheme is compared with the Cuckoo, the FA, and the firefly neural network to evaluate the quality of services (QoS) parameters such as jitter and throughput.

Fault Diagnosis of Water Pump Based on Probabilistic Neural Network

2011 ◽  
Vol 338 ◽  
pp. 421-424
Author(s):  
Tie Jun Li ◽  
Yan Chun Zhao ◽  
Xin Li ◽  
Cheng Shi Zhu ◽  
Jian Rong Ning
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Basic Principle ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Vibration Signal ◽  
Back Propagation Neural Network ◽  
Signal Spectrum ◽  
Water Pump ◽  
Time Diagnosis

The basic principle of probabilistic neural network (PNN) is introduced, which is used in the fault diagnosis of water pump in this paper. The multiple and fractional frequencies in the fault vibration signal spectrum are taken as the feature vectors, and the samples of the fault are established. The probabilistic neural network is trained based on the symptom diagnosis. The result shows that probabilistic neural network can overcome the local optimization of back propagation neural network (BPNN) and meet the requirements for fast diagnosis and high precision diagnosis during fault diagnosis process, so probabilistic neural network can be used in the real time diagnosis, and the fault diagnosis based on probabilistic neural network is feasible.

scholarly journals A Novel Deep Learning Method for Intelligent Fault Diagnosis of Rotating Machinery Based on Improved CNN-SVM and Multichannel Data Fusion

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1693 ◽  
Author(s):  
Gong ◽  
Chen ◽  
Zhang ◽  
Zhang ◽  
Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Back Propagation ◽  
Rotating Machinery ◽  
Fault Classification ◽  
Support Vector ◽  
Intelligent Fault Diagnosis ◽  
K Nearest Neighbor

Intelligent fault diagnosis methods based on deep learning becomes a research hotspot in the fault diagnosis field. Automatically and accurately identifying the incipient micro-fault of rotating machinery, especially for fault orientations and severity degree, is still a major challenge in the field of intelligent fault diagnosis. The traditional fault diagnosis methods rely on the manual feature extraction of engineers with prior knowledge. To effectively identify an incipient fault in rotating machinery, this paper proposes a novel method, namely improved the convolutional neural network-support vector machine (CNN-SVM) method. This method improves the traditional convolutional neural network (CNN) model structure by introducing the global average pooling technology and SVM. Firstly, the temporal and spatial multichannel raw data from multiple sensors is directly input into the improved CNN-Softmax model for the training of the CNN model. Secondly, the improved CNN are used for extracting representative features from the raw fault data. Finally, the extracted sparse representative feature vectors are input into SVM for fault classification. The proposed method is applied to the diagnosis multichannel vibration signal monitoring data of a rolling bearing. The results confirm that the proposed method is more effective than other existing intelligence diagnosis methods including SVM, K-nearest neighbor, back-propagation neural network, deep BP neural network, and traditional CNN.

Diesel Engine Fault Diagnosis Based on Rough Sets

2013 ◽  
Vol 722 ◽  
pp. 276-281
Author(s):  
Hong Xia Pan ◽  
Jing Yi Tian
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Diesel Engine ◽  
Set Theory ◽  
Rough Set ◽  
Cylinder Head ◽  
Rough Set Theory ◽  
Vibration Signal ◽  
Fault Classification ◽  
The Neural Network

This paper introduces the rough set theory and ROSETTA software characteristics, gives a diesel engine fault diagnosis system based on rough set theory and the vibration signal of cylinder head. Taking a certain type large power diesel engine as an example, the first to be extracted from the cylinder head vibration signal wavelet packet de-noising and time-frequency domain analysis, constructed eigenvalue for fault diagnosis, then use ROSETTA software reduction feature attributes, finally completed fault pattern classification through the neural network. By comparing the output results of the neural network before and after processing by the ROSETTA software, show that rough set theory can optimize the feature attributes, effectively reduce the input of the neural network nodes, and improve the fault classification accuracy.

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