This book covers the background theory of fluid power and indicates the range of concepts needed for a modern approach to condition monitoring and fault diagnosis. The theory is leavened by 15-years-worth of practical measurements by the author, working with major fluid power companies, and real industrial case studies. Heavily supported with examples drawn from real industrial plants – the methods in this book have been shown to work.
This book shows how neural networks are applied to computational mechanics. Part I presents the fundamentals of neural networks and other machine learning method in computational mechanics. Part II highlights the applications of neural networks to a variety of problems of computational mechanics. The final chapter gives perspectives to the applications of the deep learning to computational mechanics.