Intelligent Condition Monitoring of Mechatronic Systems

A machine that is beginning to fail says so before it stops. It vibrates differently, it heats, it draws current in a changed pattern, and it leaves wear debris in its lubricant. Condition monitoring is the discipline of listening to those signals and acting on them, and it has been transformed by two developments: sensing has become cheap and pervasive, and machine learning has become capable enough to interpret what the sensors return. This book presents two applied diagnostic studies. The first classifies four bearing conditions across several severity levels from benchmark vibration data using frequency-domain features, variance-based feature selection and a neural classifier. The second builds a bearing classifier without specialist toolboxes and reports both its performance and the conditions under which that performance was obtained. Between them they establish the methodology and the honesty about evaluation that the field needs. The book also looks outward from diagnosis to the systems being monitored and the materials they are made from. A review of next-generation health monitoring argues that conventional practice remains fragmented because each sensing technique observes only part of the picture, and develops a framework fusing mechanical, thermal, electrical and tribological evidence. A further review examines how artificial intelligence is reshaping sustainable materials development, where the properties that determine a component's degradation are decided long before it enters service.