Empirical mode decomposition of pressure signal for health condition monitoring in waterjet cutting

• Published in: International journal of advanced manufacturing technology 2014-04, Vol.72 (1-4), p.347-364
• Main Authors: Grasso, M., Pennacchi, P., Colosimo, B. M.
• Format: Article
• Language: English
• Subjects: Abrasive cutting; Abrasives; CAE) and Design; Computer-Aided Engineering (CAD; Cutting wear; Cylinders; Data acquisition; Empirical analysis; Engineering; Fault detection; Faults; Industrial and Production Engineering; Machinery condition monitoring; Maintenance management; Mechanical Engineering; Media Management; Orifices; Original Article; Principal components analysis; Signal monitoring; Tubes; Water pressure; Workpieces
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-014-5671-4

Waterjet/abrasive waterjet cutting is a flexible technology that can be exploited for different operations on a wide range of materials. Due to challenging pressure conditions, cyclic pressure loadings, and aggressiveness of abrasives, most of the components of the ultra-high pressure (UHP) pump and the cutting head are subject to wear and faults that are difficult to predict. Therefore, the continuous monitoring of machine health conditions is of great industrial interest, as it allows implementing condition-based maintenance strategies, and providing an automatic reaction to critical faults, as far as unattended processes are concerned. Most of the literature in this frame is focused on indirect workpiece quality monitoring and on fault detection for critical cutting head components (e.g., orifices and mixing tubes). A very limited attention has been devoted to the condition monitoring of critical UHP pump components, including cylinders and valves. The paper investigates the suitability of the water pressure signal as a source of information to detect different kinds of fault that may affect both the cutting head and the UHP pump components. We propose a condition monitoring approach that couples empirical mode decomposition (EMD) with principal component analysis to detect any pattern deviation with respect to a reference model, based on training data. The EMD technique is used to separate high-frequency transient patterns from low-frequency pressure ripples, and the computation of combined mode functions is applied to cope with the mode-mixing effect. Real industrial data, acquired under normal working conditions and in the presence of actual faults, are used to demonstrate the performances provided by the proposed approach.