Nozzles Classification in a High-Pressure Water Jet System

Annoni, Massimiliano; Cristaldi, L.; Lazzaroni, M.; Ferrari, S.

doi:10.1109/tim.2009.2019702

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…The experimental analysis here presented extends the results discussed by Annoni et al in [11], [12] and [13], by taking into account a larger set of fault conditions and by investigating the multi-scale nature of the pressure signal. The paper also extends the previous work of Colosimo et al [16] on PCA-based process monitoring, by proposing the EMD-PCA technique.…”

supporting

confidence: 68%

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

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Pennacchi

Colosimo

2014

Int J Adv Manuf Technol

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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 presence of actual faults, are used to demonstrate the performances provided by the proposed approach.

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supporting

confidence: 68%

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

Grasso

Pennacchi

Colosimo

2014

Int J Adv Manuf Technol

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“…Moreover, a procedure based on the analysis and classification of the instantaneous power signal features is presented in [61], while [62][63][64] show how the electrical power signal includes all the necessary information to characterise the system working condition and predict incoming faulty behaviours [65][66][67].…”

Section: Pump Electric Power Signalmentioning

confidence: 99%

A Review of Waterjet Cutting Research towards microAWJ and the Definition of the Waterjet Digital Twin

Annoni

2024

Materials

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This review paper aimed to draw the red line passing through almost 25 years of research on waterjet cutting carried out at WJ_Lab, the waterjet laboratory of the Department of Mechanical Engineering of Politecnico di Milano. The purpose was not to just historically analyse the obtained scientific results by themselves but to make them even more useful by introducing the concept of the waterjet digital twin passing through the accuracy improvements due to microAWJ. This strategy effectively creates synergy among the topics and gives the opportunity to researchers in this field to both have an example of how research in industrial manufacturing processes can be guided by scientific and industrial needs, at least from the author’s point of view, and to appreciate how it can be made useful for further improvements by introducing a powerful concept as the digital twin.

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“…A number of studies has been also focused on condition monitoring of cutting head components -orifice and mixing tube -(e.g., see Annoni et al 2008;Annoni et al 2009;Jurisevic et al 2004). However, limited attention has been devoted to the condition monitoring of UHP pump components, which is extremely relevant for condition-based maintenance purposes.…”

Section: A Real Case Study In Waterjet Cuttingmentioning

confidence: 99%

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

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Colosimo

Pacella

2014

International Journal of Production Research

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Continuous advances of sensor technology and real-time computational capability is 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) reduce data dimensionality, to make the overall signal analysis system efficient and actually implementable in industrial environment, and ii) fuse together 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 datasets, 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 data monitoring.The developed methodologies are demonstrated with both simulated and real data. IntroductionThe development of low-cost, non-intrusive and smart sensors on one hand, and the continuous improvement of real-time computational capability on the other hand, make a large amount of data potentially available in industry. In this frame, sensor signals acquired during the process provide a suitable source of information to develop an inprocess quality control and to allow a faster implementation of corrective actions. In several applications, the acquired signals present cyclically repeating patterns; in those cases the suite of profile monitoring techniques (Woodall et al., 2004;Williams et al., 2007) provides the natural framework to evaluate the stability over time of processquality. An overview of parametric and nonparametric approaches for profile data as well as application domains investigated at this time can be found in the recent book edited by Noorossana et al. (2012).This paper focuses on the specific case of monitoring profiles that are signal data.On this topic, the first seminal paper on signal profile monitoring is due to Jin and Shi (1999), who suggested using wavelet analysis to monitor tonnage signals in stamping The largest portion of profile monitoring literature focuses on single signal analysis, regardless the strong industrial interest for multi-signal applications. Data-rich environments in industry, in fact, are leading to an increasing demand for multi-sensor data fusion methods to solve quality-related problems. The most widely studied applications in literature include stability analysis and chatter detection (Kuljanic et al., 2009;Inasaki, 1999) and tool condition monitoring (Cho et al., 2010;Wang et al., 2007;Chen and Jen, 2000;Bahr et al., 1997;Bhattacharyya and Sengupta, 2009;Lezanski, 2001;Shi and Gindy, 2007;Aliustaoglu et al. 2009). However, only few authors studied profile monitoring approaches in the field of sensor fusion. Among them, Kim et al. In this study, we co...

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Nozzles Classification in a High-Pressure Water Jet System

Cited by 4 publications

References 9 publications

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

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

A Review of Waterjet Cutting Research towards microAWJ and the Definition of the Waterjet Digital Twin

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

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