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.

scholarly journals Real-Time Cutting Force/Torque Prediction During Turning

2012 ◽  
Vol 6 (5) ◽  
pp. 669-674 ◽  
Author(s):  
Kazuto Enomoto ◽  
◽  
Masaya Takei ◽  
Yasuhiro Kakinuma
Keyword(s):  
Cutting Force ◽  
Real Time ◽  
Process Monitoring ◽  
Disturbance Observer ◽  
Shear Angle ◽  
Force Estimation ◽  
Monitoring Methods ◽  
Machining Processes ◽  
The Cost

The automation of machining processes requires highly accurate process monitoring. However, the use of additional sensors leads to a significant increase in the cost and reduces the stiffness and reliability of mechanical systems. Hence, we propose a system called the cutting force observer, which uses a sensor-less and real-time cutting force estimation methodology based on the disturbance observer theory. Monitoring methods using the cutting force observer may enhance the productivity during turning. One of the parameters that significantly affect the cutting process is the shear angle. The determination of the shear angle is very important as it can be used for identifying the machining conditions. In this study, an external sensor-less monitoring system of the shear angle during turning is developed, and its performance is evaluated.

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.

Evaluation of two point of care technologies for measuring monoclonal antibody therapeutic-A concentrations in blood

Bioanalysis ◽  
2020 ◽  
Vol 12 (20) ◽  
pp. 1449-1458
Author(s):  
Saloumeh K Fischer ◽  
Kathi Williams ◽  
Ian Harmon ◽  
Bryan Bothwell ◽  
Hua Xu ◽  
...  
Keyword(s):  
Monoclonal Antibody ◽  
Real Time ◽  
Point Of Care ◽  
Turnaround Time ◽  
Monitoring Methods ◽  
Sample Collection ◽  
Time Data ◽  
Serum Samples ◽  
Quick Turnaround Time

Aim: Current blood monitoring methods require sample collection and testing at a central lab, which can take days. Point of care (POC) devices with quick turnaround time can provide an alternative with faster results, allowing for real-time data leading to better treatment decisions for patients. Results/Methodology: An assay to measure monoclonal antibody therapeutic-A was developed on two POC devices. Data generated using 75 serum samples (65 clinical & ten spiked samples) show correlative results to the data generated using Gyrolab technology. Conclusion: This case study uses a monoclonal antibody therapeutic-A concentration assay as an example to demonstrate the potential of POC technologies as a viable alternative to central lab testing with quick results allowing for real-time decision-making.

scholarly journals Landslide Geohazard Monitoring, Early Warning and Stabilization Control Methods

2014 ◽  
Vol 36 (1) ◽  
pp. 3-13 ◽  
Author(s):  
Zbigniew Bednarczyk
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Warning System ◽  
Landslide Monitoring ◽  
High Plasticity ◽  
Monitoring Methods ◽  
Time Data ◽  
Stabilization Control ◽  
Starting Point ◽  
On Line

Abstract This paper is a presentation of landslide monitoring, early warning and remediation methods recommended for the Polish Carpathians. Instrumentation included standard and automatic on-line measurements with the real-time transfer of data to an Internet web server. The research was funded through EU Innovative Economy Programme and also by the SOPO Landslide Counteraction Project. The landslides investigated were characterized by relatively low rates of the displacements. These ranged from a few millimetres to several centimetres per year. Colluviums of clayey flysch deposits were of a soil-rock type with a very high plasticity and moisture content. The instrumentation consisted of 23 standard inclinometers set to depths of 5-21 m. The starting point of monitoring measurements was in January 2006. These were performed every 1-2 months over the period of 8 years. The measurements taken detected displacements from several millimetres to 40 cm set at a depth of 1-17 m. The modern, on-line monitoring and early warning system was installed in May 2010. The system is the first of its kind in Poland and only one of several such real-time systems in the world. The installation was working with the Local Road Authority in Gorlice. It contained three automatic field stations for investigation of landslide parameters to depths of 12-16 m and weather station. In-place tilt transducers and innovative 3D continuous inclinometer systems with sensors located every 0.5 m were used. It has the possibility of measuring a much greater range of movements compared to standard systems. The conventional and real-time data obtained provided a better recognition of the triggering parameters and the control of geohazard stabilizations. The monitoring methods chosen supplemented by numerical modelling could lead to more reliable forecasting of such landslides and could thus provide better control and landslide remediation possibilities also to stabilization works which prevent landslides.

scholarly journals A Scalable Approach for Short-Term Predictions of Link Traffic Flow by Online Association of Clustering Profiles

2020 ◽  
Vol 21 (2) ◽  
pp. 119-124
Author(s):  
Alessandro Attanasi ◽  
Marco Pezzulla ◽  
Luca Simi ◽  
Lorenzo Meschini ◽  
Guido Gentile
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Time Window ◽  
Management Control ◽  
Spatial Correlations ◽  
Time Data ◽  
Short Term ◽  
Data Set ◽  
Affinity Propagation Algorithm ◽  
Short Term Prediction

AbstractShort-term prediction of traffic flows is an important topic for any traffic management control room. The large availability of real-time data raises not only the expectations for high accuracy of the forecast methodology, but also the requirements for fast computing performances. The proposed approach is based on a real-time association of the latest data received from a sensor to the representative daily profile of one among the clusters that are built offline based on an historical data set using Affinity Propagation algorithm. High scalability is achieved ignoring spatial correlations among different sensors, and for each of them an independent model is built-up. Therefore, each sensor has its own clusters of profiles with their representatives; during the short-term forecast operation the most similar representative is selected by looking at the last data received in a specified time window and the proposed forecast corresponds to the values of the cluster representative.

Fully Automated and Wirelessly Enabled Drillstem Tests: Seven-Zones Campaign Case Study in Sakhalin

2021 ◽  
Author(s):  
Elias Temer ◽  
Deiveindran Subramaniam
Keyword(s):  
Data Acquisition ◽  
Real Time ◽  
Time Window ◽  
Weather Conditions ◽  
Testing Time ◽  
Well Testing ◽  
Well Test ◽  
Time Data ◽  
Operations Control ◽  
Bad Weather

Abstract Well test is one of the crucial steps required to forecast production investments of their fields. However, the operators face many challenges such as reduced capex, exploration budgets, and bad weather conditions that limit the well testing time window. To overcome these challenges, an automated well testing platform enabled a real time monitoring and controlling more zones in a single run for appraisal wells in the Sea of Okhotsk, Russia. This article highlights the test objectives, the job planning, and automated execution of wirelessly enabled operations in very hostile conditions and limited time period. The use of a telemetry system to well test seven zones allowed real-time data acquisition, control of critical downhole equipment, data transmission to the operator's office in town. Various operational cases will be discussed to demonstrate how automated data acquisition and downhole operations control has optimized operations for both the service company and the operator.

scholarly journals Real-Time Monitoring of Tetraselmis suecica in A Saline Environment as Means of Early Water Pollution Detection

Toxics ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 57 ◽  
Author(s):  
Karin Moejes ◽  
Reshma Sherif ◽  
Simone Dürr ◽  
Sheelagh Conlan ◽  
Alex Mason ◽  
...  
Keyword(s):  
Water Pollution ◽  
Real Time ◽  
Low Frequency ◽  
Water Quality Monitoring ◽  
Pollution Monitoring ◽  
Tetraselmis Suecica ◽  
Monitoring Methods ◽  
Time Data ◽  
Current Water ◽  
Reflected Power

Biological water pollution, including organic pollutants and their possible transportation, via biofouling and ballast water, has the potential to cause severe economic and health impacts on society and environment. Current water pollution monitoring methods are limited by transportation of samples to the laboratory for analysis, which could take weeks. There is an urgent need for a water quality monitoring technique that generates real-time data. The study aims to assess the feasibility of three sensing techniques to detect and monitor the concentrations of the model species Tetraselmis suecica in real-time using eleven samples for each method. Results showed UV-Vis spectrophotometer detected increasing concentration of Tetraselmis suecica with R2 = 0.9627 and R2 = 0.9672, at 450 nm and 650 nm wavelengths, respectively. Secondly, low-frequency capacitance measurements showed a linear relationship with increasing concentration of Tetraselmis suecica at 150 Hz (R2 = 0.8463) and 180 Hz (R2 = 0.8391). Finally, a planar electromagnetic wave sensor measuring the reflected power S11 amplitude detected increasing cell density at 4 GHz (R2 = 0.8019).

scholarly journals COVIDSpread: real-time prediction of COVID-19 spread based on time-series modelling

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1110
Author(s):  
Siroos Shahriari ◽  
Taha Hossein Rashidi ◽  
AKM Azad ◽  
Fatemeh Vafaee
Keyword(s):  
Time Series ◽  
Real Time ◽  
Time Window ◽  
Time Series Models ◽  
Data Streaming ◽  
Time Data ◽  
Time Prediction ◽  
Time Series Modelling ◽  
Short Run ◽  
Modelling Techniques

A substantial amount of data about the COVID-19 pandemic is generated every day. Yet, data streaming, while considerably visualized, is not accompanied with modelling techniques to provide real-time insights. This study introduces a unified platform, COVIDSpread, which integrates visualization capabilities with advanced statistical methods for predicting the virus spread in the short run, using real-time data. The platform uses time series models to capture any possible non-linearity in the data. COVIDSpread enables lay users, and experts, to examine the data and develop several customized models with different restrictions such as models developed for a specific time window of the data. COVIDSpread is available here: http://vafaeelab.com/COVID19TS.html.

scholarly journals Real-time time-series modelling for prediction of COVID-19 spread and intervention assessment

2020 ◽  
Author(s):  
Taha Hossein Rashidi ◽  
Siroos Shahriari ◽  
AKM Azad ◽  
Fatemeh Vafaee
Keyword(s):  
Time Series ◽  
Real Time ◽  
Time Window ◽  
Data Streaming ◽  
Time Data ◽  
Social Distancing ◽  
Time Series Modelling ◽  
Short Run ◽  
Modelling Techniques ◽  
Effective Policy

AbstractSubstantial amount of data about the COVID-19 pandemic is generated every day. Yet, data streaming, while considerably visualized, is not accompanied with advanced modelling techniques to provide real-time insights. This study introduces a unified platform which integrates visualization capabilities with advanced statistical methods for predicting the virus spread in the short run, using real-time data. The platform is backed up by advanced time series models to capture any possible non-linearity in the data which is enhanced by the capability of measuring the expected impact of preventive interventions such as social distancing and lockdowns. The platform enables lay users, and experts, to examine the data and develop several customized models with different restriction such as models developed for specific time window of the data. Our policy assessment of the case of Australia, shows that social distancing and travel ban restriction significantly affect the reduction of number of cases, as an effective policy.

Sign in / Sign up

Export Citation Format

Share Document