NDLI: Gearbox fault diagnosis based on artificial neural network and genetic algorithms

Content Provider	IEEE Xplore Digital Library
Author	Zhixin Yang Wui Ian Hoi Jianhua Zhong
Copyright Year	2011
Description	Author affiliation: Department of Electromechanical Engineering, University of Macau, China (Zhixin Yang; Wui Ian Hoi; Jianhua Zhong)
Abstract	System maintenance for reliable running of key machinery is critical to many industries, where condition monitoring and fault diagnosis is important supporting technology. This paper selects a typical component in rotating machinery, the gearbox, as the target to study a proper monitoring and fault diagnosis method to prevent malfunction and failure. The failure is divided into two levels. One is at the component level that includes various gear faults, and another is at system level that studies machinery statuses include looseness, misalignment and unbalance. A prototype system is built for experiment. Two intelligent methods include artificial neural network (ANN) and genetic algorithms (GAs) are combined to carry out signal classification and analysis. ANNs are one of the common machine learning technologies that used for detecting and diagnosing faults in rotating machinery. To look for a feasible combined solution, this paper tests the effect of back-propagation (BP) network and GAs are used in this paper for selecting the significant input features in a large set of possible features in machine condition monitoring with vibration signals. Considering the performance of machine learning system are hard to predict, and the quality of input signal is a major factor affecting the performance of training and learning of the system itself. Signal preprocessing is executed through feature extraction by wavelet packet transforms (WPT) technology and time domains statistical analysis to generate statistic variables for analysis. With an aim to identify a proper diagnosis approach, the effect of BP network and GAs are verified with case studies.
Starting Page	37
Ending Page	42
File Size	299983
Page Count	6
File Format	PDF
ISBN	9781612843513
e-ISBN	9781612844718
e-ISBN	9781612844725
DOI	10.1109/ICSSE.2011.5961870
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Accuracy Feature selection Gears Genetic algorithm Artificial neural networks Feature extraction Biological cells
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Improving Performance of an Artificial Neural Network Based Gearbox Fault Diagnosis System

Gear Fault Diagnosis with Neural Network Based on Niche Genetic Algorithm

Diagnostic parameters selection of aero-engine based on BP network

Gearbox feature extraction and fault diagnosis based on blind source separation

Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal

Machine learning method with compensation distance technique for gear fault detection

BP neural network-based on fault diagnosis of hydraulic servo-valves

Data mining based on improved neural network and its application in fault diagnosis of steam turbine

Investigation into the Fault Diagnosis of Gear Box Based on NMF and Neural Networks

Gearbox fault diagnosis based on artificial neural network and genetic algorithms

Similar Documents

Improving Performance of an Artificial Neural Network Based Gearbox Fault Diagnosis System

Gear Fault Diagnosis with Neural Network Based on Niche Genetic Algorithm

Diagnostic parameters selection of aero-engine based on BP network

Gearbox feature extraction and fault diagnosis based on blind source separation

Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal

Machine learning method with compensation distance technique for gear fault detection

BP neural network-based on fault diagnosis of hydraulic servo-valves

Data mining based on improved neural network and its application in fault diagnosis of steam turbine

Investigation into the Fault Diagnosis of Gear Box Based on NMF and Neural Networks

Gearbox fault diagnosis based on artificial neural network and genetic algorithms