Application of an improved Levenberg-Marquardt back propagation neural network to gear fault level identification

Chip fault is one of the most frequently occurring damage modes in gears. Identifying different chip levels, especially for incipient chip is a challenge work in gear fault analysis. In order to classify the different gear chip levels automatically and accurately, this paper developed a fast and accurate method. In this method, features which are specially designed for gear damage detection are extracted based on a revised time synchronous averaging algorithm to character the gear conditions. Then, a modified Levenberg-Marquardt training back propagation neural network is utilized to identify the gear chip levels. In this modified neural network, damping factor and dynamic momentum are optimized simultaneously. Fisher iris data which is the machine learning public data is used to validate the performance of the improved neural network. Gear chip fault experiments were conducted and vibration signals were captured under different loads and motor speeds. Finally, the proposed methods are applied to identify the gear chip levels. The classification results of public data and gear chip fault experiment data both demonstrated that the improved neural network gets a better performance in accuracy and speed compared to the neural networks which are trained by El-Alfy’s and Norgaard’s Levenberg-Marquardt algorithm. Therefore, the proposed method is more suitable for on-line condition monitoring and fault diagnosis

Application of an improved Levenberg-Marquardt back propagation neural network to gear fault level identification

Chip fault is one of the most frequently occurring damage modes in gears. Identifying different chip levels, especially for incipient chip is a challenge work in gear fault analysis. In order to classify the different gear chip levels automatically and accurately, this paper developed a fast and accurate method. In this method, features which are specially designed for gear damage detection are extracted based on a revised time synchronous averaging algorithm to character the gear conditions. Then, a modified Levenberg-Marquardt training back propagation neural network is utilized to identify the gear chip levels. In this modified neural network, damping factor and dynamic momentum are optimized simultaneously. Fisher iris data which is the machine learning public data is used to validate the performance of the improved neural network. Gear chip fault experiments were conducted and vibration signals were captured under different loads and motor speeds. Finally, the proposed methods are applied to identify the gear chip levels. The classification results of public data and gear chip fault experiment data both demonstrated that the improved neural network gets a better performance in accuracy and speed compared to the neural networks which are trained by El-Alfy’s and Norgaard’s Levenberg-Marquardt algorithm. Therefore, the proposed method is more suitable for on-line condition monitoring and fault diagnosis

Engineering Education and Research Using MATLAB

MATLAB is a software package used primarily in the field of engineering for signal processing, numerical data analysis, modeling, programming, simulation, and computer graphic visualization. In the last few years, it has become widely accepted as an efficient tool, and, therefore, its use has significantly increased in scientific communities and academic institutions. This book consists of 20 chapters presenting research works using MATLAB tools. Chapters include techniques for programming and developing Graphical User Interfaces (GUIs), dynamic systems, electric machines, signal and image processing, power electronics, mixed signal circuits, genetic programming, digital watermarking, control systems, time-series regression modeling, and artificial neural networks

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Attitudes towards old age and age of retirement across the world: findings from the future of retirement survey

The 21st century has been described as the first era in human history when the world will no longer be young and there will be drastic changes in many aspects of our lives including socio-demographics, financial and attitudes towards the old age and retirement. This talk will introduce briefly about the Global Ageing Survey (GLAS) 2004 and 2005 which is also popularly known as “The Future of Retirement”. These surveys provide us a unique data source collected in 21 countries and territories that allow researchers for better understanding the individual as well as societal changes as we age with regard to savings, retirement and healthcare. In 2004, approximately 10,000 people aged 18+ were surveyed in nine counties and one territory (Brazil, Canada, China, France, Hong Kong, India, Japan, Mexico, UK and USA). In 2005, the number was increased to twenty-one by adding Egypt, Germany, Indonesia, Malaysia, Poland, Russia, Saudi Arabia, Singapore, Sweden, Turkey and South Korea). Moreover, an additional 6320 private sector employers was surveyed in 2005, some 300 in each country with a view to elucidating the attitudes of employers to issues relating to older workers. The paper aims to examine the attitudes towards the old age and retirement across the world and will indicate some policy implications

Attitudes towards old age and age of retirement across the world: findings from the future of retirement survey

The 21st century has been described as the first era in human history when the world will no longer be young and there will be drastic changes in many aspects of our lives including socio-demographics, financial and attitudes towards the old age and retirement. This talk will introduce briefly about the Global Ageing Survey (GLAS) 2004 and 2005 which is also popularly known as “The Future of Retirement”. These surveys provide us a unique data source collected in 21 countries and territories that allow researchers for better understanding the individual as well as societal changes as we age with regard to savings, retirement and healthcare. In 2004, approximately 10,000 people aged 18+ were surveyed in nine counties and one territory (Brazil, Canada, China, France, Hong Kong, India, Japan, Mexico, UK and USA). In 2005, the number was increased to twenty-one by adding Egypt, Germany, Indonesia, Malaysia, Poland, Russia, Saudi Arabia, Singapore, Sweden, Turkey and South Korea). Moreover, an additional 6320 private sector employers was surveyed in 2005, some 300 in each country with a view to elucidating the attitudes of employers to issues relating to older workers. The paper aims to examine the attitudes towards the old age and retirement across the world and will indicate some policy implications