Research of RBF Neural Networks Algorithm to Fault Diagnosis of Rotary Machinery

2009 ◽  
Author(s):  
Xiao-yue Wang ◽  
Zhong-kui Zhang
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Rbf Neural Networks ◽  
Rotary Machinery

scholarly journals Fault Diagnosis of Rotary Machines Using Deep Convolutional Neural Network with Wide Three Axis Vibration Signal Input

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 4017 ◽  
Author(s):  
Davor Kolar ◽  
Dragutin Lisjak ◽  
Michał Pająk ◽  
Danijel Pavković
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Input Matrix ◽  
Accelerometer Signal ◽  
Rotary Machinery ◽  
Signal Input

Fault diagnosis is considered as an essential task in rotary machinery as possibility of an early detection and diagnosis of the faulty condition can save both time and money. This work presents developed and novel technique for deep-learning-based data-driven fault diagnosis for rotary machinery. The proposed technique input raw three axes accelerometer signal as high definition 1D image into deep learning layers which automatically extract signal features, enabling high classification accuracy. Unlike the researches carried out by other researchers, accelerometer data matrix with dimensions 6400 × 1 × 3 is used as input for convolutional neural network training. Since convolutional neural networks can recognize patterns across input matrix, it is expected that wide input matrix containing vibration data should yield good classification performance. Using convolutional neural networks (CNN) trained model, classification in one of the four classes can be performed. Additionally, number of kernels of CNN is optimized using grid search, as preliminary studies show that alternating number of kernels impacts classification results. This study accomplished the effective classification of different rotary machinery states using convolutional artificial neural network for classification of raw three axis accelerometer signal input.

scholarly journals A Fault Diagnosis Method for Lithium-Ion Battery Packs Using Improved RBF Neural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Jia Wang ◽  
Shenglong Zhang ◽  
Xia Hu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Rbf Neural Network ◽  
Lithium Ion ◽  
Rbf Neural Networks ◽  
Battery Packs ◽  
Diagnosis Method

With the increasing demand for electric vehicles, the high voltage safety of electric vehicles has attracted significant attention. More than 30% of electric vehicle accidents are caused by the battery system; hence, it is vital to investigate the fault diagnosis method of lithium-ion battery packs. The fault types of lithium-ion battery packs for electric vehicles are complex, and the treatment is cumbersome. This paper presents a fault diagnosis method for the electric vehicle power battery using the improved radial basis function (RBF) neural network. First, the fault information of lithium-ion battery packs was collected using battery test equipment, and the fault levels were then determined. Subsequently, the improved RBF neural networks were employed to identify the fault of the lithium-ion battery pack system using the experimental data. The diagnosis test results showed that the improved RBF neural networks could effectively identify the fault diagnosis information of the lithium-ion battery packs, and the diagnosis accuracy was about 100%.

Stream Turbine Vibration Fault Diagnosis

2013 ◽  
Vol 340 ◽  
pp. 90-94 ◽  
Author(s):  
Hong Sheng Su
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
Local Extremum ◽  
Decision Table ◽  
Rbf Neural Networks ◽  
Global Extremum ◽  
Artificial Fish Swarm Algorithm ◽  
Diagnosis Method

RBF neural networks possessed the excellent characteristics such as insensitive on the initial weights and parameters with artificial fish-swarm algorithm (AFSA) applied, which made it have abilities to get rid of the local extremum and obtain the global extremum, and called as AFSA-RBF neural networks. In this paper, a new stream turbine vibration fault diagnosis method was presented based on AFSA-RBF neural networks. After quantification and reduction of the diagnosis decision table, the simplified decision table served as the learning samples of AFSA-RBF neural network, and the well-trained neural network was then applied to diagnose stream turbine vibration faults. The diagnosis results show that the proposed method possesses higher convergence speed and diagnosis precision, and is a very effective turbine fault diagnosis method.

Sign in / Sign up

Export Citation Format

Share Document