Creating adaptive predictions for packaging-critical quality parameters using advanced analytics and machine learning

TAPPI Journal ◽  
2019 ◽  
Vol 18 (11) ◽  
pp. 679-689
Author(s):  
CYDNEY RECHTIN ◽  
CHITTA RANJAN ◽  
ANTHONY LEWIS ◽  
BETH ANN ZARKO
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Predictive Analytics ◽  
Paper Industry ◽  
Quality Parameters ◽  
Fast Change ◽  
The Face ◽  
Model Tuning ◽  
Reducing Costs ◽  
Time Critical

Packaging manufacturers are challenged to achieve consistent strength targets and maximize production while reducing costs through smarter fiber utilization, chemical optimization, energy reduction, and more. With innovative instrumentation readily accessible, mills are collecting vast amounts of data that provide them with ever increasing visibility into their processes. Turning this visibility into actionable insight is key to successfully exceeding customer expectations and reducing costs. Predictive analytics supported by machine learning can provide real-time quality measures that remain robust and accurate in the face of changing machine conditions. These adaptive quality “soft sensors” allow for more informed, on-the-fly process changes; fast change detection; and process control optimization without requiring periodic model tuning. The use of predictive modeling in the paper industry has increased in recent years; however, little attention has been given to packaging finished quality. The use of machine learning to maintain prediction relevancy under everchanging machine conditions is novel. In this paper, we demonstrate the process of establishing real-time, adaptive quality predictions in an industry focused on reel-to-reel quality control, and we discuss the value created through the availability and use of real-time critical quality.

scholarly journals Use of Artificial Intelligence for Face Detection with Face Mask in Real Time to Control the Entrance to an Entity

2021 ◽  
Vol 11 (11) ◽  
pp. 68-75
Author(s):  
Enrique Lee Huamaní ◽  
◽  
Lilian Ocares Cunyarachi
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Face Detection ◽  
Face Mask ◽  
Economic Activities ◽  
Facial Mask ◽  
The Face ◽  
The Individual ◽  
A Company ◽  
Selection Of

Due to the pandemic caused by Covid-19, daily life has changed significantly. For this reason, biosecurity measures have been implemented to prevent the spread of the virus as an effective way to reactivate economic activities. In this sense, the present paper focuses on real-time face detection as a measure of control at the entrance to an entity, thus avoiding the spread of the virus while recognizing the identity of workers despite the use of masks and thus reducing the risk of entry of individuals outside the organization. Therefore, the objective is to contribute to the security of a company through the application of machine learning methodology. The selection of methodology is justified due to the adaptation of the same according to the interests of this project. Consequently, algorithms were used in a progressive manner, obtaining as a result the control system that was intended, since each particularity of the face of the individual was recognized in relation to its corresponding identification. Finally, the results of this article benefit the security of organizations regardless of their field or sector. Keywords— Control, Detection, Facial Recognition, Facial Mask, Face recognition, Machine learning.

scholarly journals A Real-Time Machine Learning Based Public Transport Bus-Passenger Information System

2021 ◽  
Author(s):  
Menzi Skhosana ◽  
Absalom Ezugwu
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Public Transport ◽  
Public Transportation ◽  
Web Application ◽  
Predictive Analytics ◽  
Current Status ◽  
Transport Sector ◽  
The Public ◽  
Cost Efficient

The era of Big Data and the Internet of Things is upon us, and it is time for developing countries to take advantage of and pragmatically apply these ideas to solve real-world problems. Many problems faced daily by the public transportation sector can be resolved or mitigated through the collection of appropriate data and application of predictive analytics. In this body of work, we are primarily focused on problems affecting public transport buses. These include the unavailability of real-time information to commuters about the current status of a given bus or travel route; and the inability of bus operators to efficiently assign available buses to routes for a given day based on expected demand for a particular route. A cloud-based system was developed to address the aforementioned. This system is composed of two subsystems, namely a mobile application for commuters to provide the current location and availability of a given bus and other related information, which can also be used by drivers so that the bus can be tracked in real-time and collect ridership information throughout the day, and a web application that serves as a dashboard for bus operators to gain insights from the collected ridership data. These were integrated with a machine learning model trained on collected ridership data to predict the daily ridership for a given route. Our novel system provides a holistic solution to problems in the public transport sector, as it is highly scalable, cost-efficient and takes full advantage of the currently available technologies in comparison with other previous work in this topic.

Application of Artificial Intelligence and Machine Learning to Detect Drilling Anomalies Leading to Stuck Pipe Incidents

2021 ◽  
Author(s):  
Paulinus Abhyudaya Bimastianto ◽  
Shreepad Purushottam Khambete ◽  
Hamdan Mohamed Alsaadi ◽  
Suhail Mohammed Al Ameri ◽  
Erwan Couzigou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Data Science ◽  
Predictive Analytics ◽  
Drilling Process ◽  
Time Data ◽  
Surface Parameters ◽  
Normal Behavior ◽  
Real Time Data ◽  
Productive Time

Abstract This project used predictive analytics and machine learning-based modeling to detect drilling anomalies, namely stuck pipe events. Analysis focused on historical drilling data and real-time operational data to address the limitations of physics-based modeling. This project was designed to enable drilling crews to minimize downtime and non-productive time through real-time anomaly management. The solution used data science techniques to overcome data consistency/quality issues and flag drilling anomalies leading to a stuck pipe event. Predictive machine learning models were deployed across seven wells in different fields. The models analyzed both historical and real-time data across various data channels to identify anomalies (difficulties that impact non-productive time). The modeling approach mimicked the behavior of drillers using surface parameters. Small deviations from normal behavior were identified based on combinations of surface parameters, and automated machine learning was used to accelerate and optimize the modeling process. The output was a risk score that flags deviations in rig surface parameters. During the development phase, multiple data science approaches were attempted to monitor the overall health of the drilling process. They analyzed both historical and real-time data from torque, hole depth and deviation, standpipe pressure, and various other data channels. The models detected drilling anomalies with a harmonic model accuracy of 80% and produced valid alerts on 96% of stuck pipe and tight hole events. The average forewarning was two hours. This allowed personnel ample time to make corrections before stuck pipe events could occur. This also enabled the drilling operator to save the company upwards of millions of dollars in drilling costs and downtime. This project introduced novel data aggregation and deep learning-based normal behavior modeling methods. It demonstrates the benefits of adopting predictive analytics and machine learning in drilling operations. The approach enabled operators to mitigate data issues and demonstrate real-time, high-frequency and high-accuracy predictions. As a result, the operator was able to significantly reduce non-productive time.

scholarly journals Real Time Face Recognition in Group Images using LBPH

2019 ◽  
Vol 8 (2) ◽  
pp. 1362-1367
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Face Recognition ◽  
Real Time ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Testing Dataset ◽  
Negative Images ◽  
The Face ◽  
Haar Cascade

Face recognition is a beneficial work in computer vision based applications. The goal of the proposed system is to provide complete face recognitions system capable of working a group of images. The faces are detected and verified the identity of an individual using a machine learning algorithm. The haar cascade detects the face from a group of images for training and testing dataset. The dataset contained positive and negative images for training and testing. The LBPH algorithm recognizes the faces from input images. The proposed system detects and recognizes faces with 98% accuracy

scholarly journals Machine learning based soft sensor model for BOD estimation using intelligence at edge

2021 ◽  
Author(s):  
Bhawani Shankar Pattnaik ◽  
Arunima Sambhuta Pattanayak ◽  
Siba Kumar Udgata ◽  
Ajit Kumar Panda
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Real Time ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Soft Sensor ◽  
Quality Monitoring ◽  
Sensor Model ◽  
Statistical Measures ◽  
Computational Performance

AbstractReal-time water quality monitoring is a complex system as it involves many quality parameters to be monitored, the nature of these parameters, and non-linear interdependence between themselves. Intelligent algorithms crucial in building intelligent systems are good candidates for building a reliable and convenient monitoring system. To analyze water quality, we need to understand, model, and monitor the water pollution in real time using different online water quality sensors through an Internet of things framework. However, many water quality parameters cannot be easily measured online due to several reasons such as high-cost sensors, low sampling rate, multiple processing stages by few heterogeneous sensors, the requirement of frequent cleaning and calibration, and spatial and application dependency among different water bodies. A soft sensor is an efficient and convenient alternative approach for water quality monitoring. In this paper, we propose a machine learning-based soft sensor model to estimate biological oxygen demand (BOD), a time-consuming and challenging process to measure. We also propose a system architecture for implementing the soft sensor both on the cloud and edge layers, so that the edge device can make adaptive decisions in real time by monitoring the quality of water. A comparative study between the computational performance of edge and cloud nodes in terms of prediction accuracy, learning time, and decision time for different machine learning (ML) algorithms is also presented. This paper establishes that BOD soft sensors are efficient, less costly, and reasonably accurate with an example of a real-life application. Here, the IBK ML technique proves to be the most efficient in predicting BOD. The experimental setup uses 100 test readings of STP water samples to evaluate the performance of the IBK technique, and the statistical measures are reported as correlation coefficient = 0.9273, MAE = 0.082, RMSE = 0.1994, RAE = 17.20%, RRSE = 37.62%, and edge response time = 0.15 s only.

scholarly journals Robolution: Real Time Predictive Analytics for Industrial Robots

2020 ◽  
Vol 9 (3) ◽  
pp. 923-926
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Predictive Analytics ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Industrial Robots ◽  
Financial Loss ◽  
The Given

Robots have been playing a very important role in our day-to-day lives and will be a necessity in the coming future. Whenever we hear automation, the first thing that strikes our mind is a robot performing the given task. But if a robot fails to do the task, it could cost an individual or corporate a huge financial loss. In this study, we have learned the working of various robots and drawbacks that hold them back. For this work, we did make a study of drives used in the robot and after that applied the machine learning algorithms to predict the classification of whether the robot will function properly or not, based on the data of drive(s).

Fundamentals of Wireless Sensor Networks Using Machine Learning Approaches

2020 ◽  
pp. 233-254
Author(s):  
E. B. Priyanka ◽  
S. Thangavel ◽  
D. Venkatesa Prabu
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Real Time ◽  
Oil And Gas ◽  
Predictive Analytics ◽  
Oil And Gas Industry ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Learning Approaches ◽  
Gas Industry

Big data and analytics may be new to some industries, but the oil and gas industry has long dealt with large quantities of data to make technical decisions. Oil producers can capture more detailed data in real-time at lower costs and from previously inaccessible areas, to improve oilfield and plant performance. Stream computing is a new way of analyzing high-frequency data for real-time complex-event-processing and scoring data against a physics-based or empirical model for predictive analytics, without having to store the data. Hadoop Map/Reduce and other NoSQL approaches are a new way of analyzing massive volumes of data used to support the reservoir, production, and facilities engineering. Hence, this chapter enumerates the routing organization of IoT with smart applications aggregating real-time oil pipeline sensor data as big data subjected to machine learning algorithms using the Hadoop platform.

Real Time Implementation of ESP Predictive Analytics - Towards Value Realization from Data Science

2021 ◽  
Author(s):  
Antonio Andrade Marin ◽  
Issa Al Balushi ◽  
Adnan Al Ghadani ◽  
Hassana Al Abri ◽  
Abdullah Khalfan Said Al Zaabi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Phase Ii ◽  
Data Science ◽  
Historical Data ◽  
Predictive Analytics ◽  
Failure Prediction ◽  
Data Driven ◽  
Early Prediction ◽  
Digital Infrastructure

Abstract Failure Prediction in Oil and Gas Artificial Lift Systems is materializing through the implementation of advanced analytics driven by physics-based models. During the Phase I of this project, two early failure prediction machine learning models were trained offline with historical data and evaluated through a blind test. The next challenge, Phase II, is to operationalize these models on Real-Time and re-assess their accuracy, precision and early prediction (in days) while having the assets focusing on either extending the runtime through optimization, chemical injection, etc. or proactive pump replacement (PPR) for high producers wells with triggered early prediction alarms. The paper details Phase II of live prediction for two assets consisting of 740 wells to enable data-driven insights in engineers’ daily workflow. In Phase I, a collaboration between SMEs and Data Scientists was established to build two failure prediction models for Electrical Submersible Pumps (ESP) using historical data that could identify failure prone wells along with the component at risk with high precision. Phase II entails the development of a Real-Time scoring pipeline to avail daily insights from this model for live wells. To achieve this, PDO leveraged its Digital Infrastructure for extraction of high-resolution measured data for 750 wells daily. A Well Management System (WMS) automatically sustains physics-based ESP models to calculate engineering variables from nodal analysis. Measured and engineered data are sampled, and referencing learnt patterns, the machine learning algorithm (MLA) estimates the probability of failure based on a daily rolling data window. An Exception Based Surveillance (EBS) system tracks well failure probability and highlights affected wells based on business logic. A visualization is developed to facilitate EBS interpretation. All the above steps are automated and synchronized among data historian, WMS and EBS System to operate on a daily schedule. From the Asset, at each highlighted exception, a focus team of well owners and SME initiate a review to correlate the failure probability with ESP signatures to validate the alarm. Aided by physics-based well models, action is directed either towards a) optimization, b) troubleshooting or c) proactive pump replacement in case of inevitable failure conditions. This workflow enables IT infrastructure and Asset readiness to benefit from various modeling initiatives in subsequent phases. Live Implementation of Exceptions from Predictive Analytics is an effective complement to well owners for prioritization of well reviews. Based on alarm validity, risk of failure and underperformance – optimizations, PPRs or workover scheduling are performed with reliability. This methodology would enable a Phase III of scaling up in Real-Time with growing assets wherethe system would be periodically retrained on True Negatives and maintained automatically with minimum manual intervention. It is experienced that a high precision model alone is not enough to reap the benefits of Predictive Analytics. The ability to operate in a production mode and embedding insights into decisions and actions, determines ROI on Data Science initiatives. Digital Infrastructure, a Real Time Well Modeling Platform and Cognitive adaptation of analytics by Well Owners are key for this operationalization that demands reliable data quality, computational efficiency, and data-driven decisions philosophy.

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