Showing papers by "Marco Grasso published in 2014"

Profile monitoring via sensor fusion: the use of PCA methods for multi-channel data

Abstract: Continuous advances of sensor technology and real-time computational capability are leading to data-rich environments to improve industrial automation and machine intelligence. When multiple signals are acquired from different sources (i.e. multi-channel signal data), two main issues must be faced: (i) the reduction of data dimensionality to make the overall signal analysis system efficient and actually applicable in industrial environments, and (ii) the fusion of all the sensor outputs to achieve a better comprehension of the process. In this frame, multi-way principal component analysis (PCA) represents a multivariate technique to perform both the tasks. The paper investigates two main multi-way extensions of the traditional PCA to deal with multi-channel signals, one based on unfolding the original data-set, and one based on multi-linear analysis of data in their tensorial form. The approaches proposed for data modelling are combined with appropriate control charting to achieve multi-channel profile da...

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

Abstract: 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.

In-Process Quality Characterization of Grinding Processes: A Sensor-Fusion Based Approach

Abstract: The quality assessment of manufacturing processes has been traditionally based on sample measures performed on the process output. This leads to the common “product-based Statistical Process Control (SPC)” framework. However, there are applications of actual industrial interest where post-process quality measurement procedures involve time-consuming inspections strongly related to the operator’s experience and/or based on expensive equipment. Cylindrical grinding of large rolls may be one of them. The assessment of the final acceptability of a ground cylinder, in terms of surface finish, is a challenging task with traditional measuring tools, and it often depends on operator’s visual inspections and on his subjective evaluations. In this frame, a paradigm shift is required to substitute troublesome post-process monitoring procedures with in-process and signal-based ones. The paper reviews the quality control issues in surface quality monitoring of big ground rolls where process vibrations (i.e. chatter) are one of major concerns. A multi-sensor approach for vibration onset detection, based on the use of a multi-channel implementation of the Principal Component Analysis, is proposed. The potential benefits, the implementation issues, and the main criticalities are discussed by analysing data of a real industrial application.Copyright © 2014 by ASME