A New Real-Time Process Monitor Method Based on MRC

2014 ◽  
Vol 971-973 ◽  
pp. 1481-1484
Author(s):  
Ke He Wu ◽  
Long Chen ◽  
Yi Li
Keyword(s):  
Information System ◽  
Real Time ◽  
Process Monitoring ◽  
Time Process ◽  
Monitoring Methods ◽  
Traditional Process ◽  
Kernel Level ◽  
Process Monitor

In order to ensure safe and stable running of applications, this paper analyses the limitation of traditional process-monitoring methods, and then designs a new real-time process monitor method based on Mandatory Running Control (MRC) technology. This method not only can monitor the processes, but also can control them from system kernel level to improve the reliability and safety of applications, so as to ensure the security and stability of information system.

Measuring the extent of exfoliation in polymer/clay nanocomposites using real-time process monitoring methods

Polymer ◽  
2005 ◽  
Vol 46 (24) ◽  
pp. 10908-10918 ◽  
Author(s):  
Anthony J. Bur ◽  
Yu-Hsin Lee ◽  
Steven C. Roth ◽  
Paul R. Start
Keyword(s):  
Real Time ◽  
Process Monitoring ◽  
Clay Nanocomposites ◽  
Time Process ◽  
Monitoring Methods

Synthesis of Tailored Segmented Polyurethanes Utilizing Continuous-Flow Reactors and Real-Time Process Monitoring

2021 ◽  
Author(s):  
Xabier Lopez de Pariza ◽  
Tim Erdmann ◽  
Pedro L. Arrechea ◽  
Leron Perez ◽  
Charles Dausse ◽  
...  
Keyword(s):  
Real Time ◽  
Process Monitoring ◽  
Continuous Flow ◽  
Time Process ◽  
Flow Reactors ◽  
Segmented Polyurethanes ◽  
Continuous Flow Reactors

scholarly journals Real-Time Process Monitoring Based on Multivariate Control Chart for Anomalies Driven by Frequency Signal via Sound and Electrocardiography Cases

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1510
Author(s):  
Chih-Hung Jen ◽  
Chien-Chih Wang
Keyword(s):  
Real Time ◽  
Process Monitoring ◽  
Sound Frequency ◽  
Detection Accuracy ◽  
Control Line ◽  
Time Process ◽  
Intrinsic Mode Functions ◽  
Low Frequencies ◽  
Linear Discriminant

Recent developments in network technologies have led to the application of cloud computing and big data analysis to industrial automation. However, the automation of process monitoring still has numerous issues that need to be addressed. Traditionally, offline statistical processes are generally used for process monitoring; thus, problems are often detected too late. This study focused on the construction of an automated process monitoring system based on sound and vibration frequency signals. First, empirical mode decomposition was combined with intrinsic mode functions to construct different sound frequency combinations and differentiate sound frequencies according to anomalies. Then, linear discriminant analysis (LDA) was adopted to classify abnormal and normal sound frequency signals, and a control line was constructed to monitor the sound frequency. In a case study, the proposed method was applied to detect abnormal sounds at high and low frequencies, and a detection accuracy of over 90% was realized. In another case study, the proposed method was applied to analyze electrocardiography signals and was similarly able to identify abnormal situations. Thus, the proposed method can be applied to real-time process monitoring and the detection of abnormalities with high accuracy in various situations.

Real Time Process Monitoring with an Integrated Fibre Monitor

1994 ◽  
pp. 395-398
Author(s):  
Chris Peters ◽  
Julian D. C. Jones ◽  
Daoning Su
Keyword(s):  
Real Time ◽  
Process Monitoring ◽  
Time Process

Ultrawideband Wireless Sensor Network for Real Time Process Monitoring

2018 ◽  
Author(s):  
Alexander S. Dmitriev ◽  
Lev V. Kuzmin ◽  
Anton I. Ryshov ◽  
Yuri V. Andreyev ◽  
Maxim G. Popov
Keyword(s):  
Wireless Sensor Network ◽  
Real Time ◽  
Sensor Network ◽  
Process Monitoring ◽  
Wireless Sensor ◽  
Time Process

The Use of Adaptive PCA-Based Condition Monitoring Methods in Machining Processes

2012 ◽  
Author(s):  
Marco Grasso ◽  
Bianca Maria Colosimo ◽  
Giovanni Moroni
Keyword(s):  
Real Time ◽  
Process Monitoring ◽  
Reference Model ◽  
Time Window ◽  
End Milling ◽  
Monitoring Methods ◽  
Time Data ◽  
Machining Processes ◽  
Adaptive Approach ◽  
Adaptive Monitoring

In different manufacturing applications the assessment of the health conditions of a machine tool, together with the quality and stability of the process, requires the capability of dealing with response variables described in terms of profile data. In the frame of in-process monitoring of sensor signals this is the case, for instance, of monitoring either series production of large lots of parts or machining processes characterized by cyclic signals, where both the condition of the machine components and the final quality of the worked piece may be correlated with the stability of repeating signal profiles in time. However, as far as real time data acquisition is concerned, and when measurements are performed with high sampling frequency, data are likely to be auto-correlated, and hence it is of fundamental importance to develop adaptive monitoring tools robust with respect to non-steady state conditions. The paper deals with the utilization of profile monitoring approaches for in-process monitoring of manufacturing operations and investigates their applicability to the problem of monitoring auto-correlated signals. In particular Principal Component Analysis (PCA) is applied in combination with an adaptive approach based on a moving time window for continuously revise the reference model is evaluated and discussed. A real case study is used to test the performances of the method: the task is to detect tool chipping and breakage in end milling operations by means of real-time monitoring of cutting force signals. The evolution of tool wear imposes a trend in observed signals which leads to the need for an adaptive approach to properly isolate the breakage event from the slow pattern change due to wear mechanism.

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