Cloud-Based Parallel Machine Learning for Tool Wear Prediction

2018 ◽  
Vol 140 (4) ◽  
Author(s):  
Dazhong Wu ◽  
Connor Jennings ◽  
Janis Terpenny ◽  
Soundar Kumara ◽  
Robert X. Gao
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Tool Wear ◽  
Random Forests ◽  
Predictive Models ◽  
Parallel Machine ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Processing Scheme ◽  
Cloud Computing System

The emergence of cloud computing, industrial internet of things (IIoT), and new machine learning techniques have shown the potential to advance prognostics and health management (PHM) in smart manufacturing. While model-based PHM techniques provide insight into the progression of faults in mechanical components, certain assumptions on the underlying physical mechanisms for fault development are required to develop predictive models. In situations where there is a lack of adequate prior knowledge of the underlying physics, data-driven PHM techniques have been increasingly applied in the field of smart manufacturing. One of the limitations of current data-driven methods is that large volumes of training data are required to make accurate predictions. Consequently, computational efficiency remains a primary challenge, especially when large volumes of sensor-generated data need to be processed in real-time applications. The objective of this research is to introduce a cloud-based parallel machine learning algorithm that is capable of training large-scale predictive models more efficiently. The random forests (RFs) algorithm is parallelized using the MapReduce data processing scheme. The MapReduce-based parallel random forests (PRFs) algorithm is implemented on a scalable cloud computing system with varying combinations of processors and memories. The effectiveness of this new method is demonstrated using condition monitoring data collected from milling experiments. By implementing RFs in parallel on the cloud, a significant increase in the processing speed (14.7 times in terms of increase in training time) has been achieved, with a high prediction accuracy of tool wear (eight times in terms of reduction in mean squared error (MSE)).

scholarly journals A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests

2017 ◽  
Vol 139 (7) ◽  
Author(s):  
Dazhong Wu ◽  
Connor Jennings ◽  
Janis Terpenny ◽  
Robert X. Gao ◽  
Soundar Kumara
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Tool Wear ◽  
Random Forests ◽  
Manufacturing Systems ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Tool Wear Prediction

Manufacturers have faced an increasing need for the development of predictive models that predict mechanical failures and the remaining useful life (RUL) of manufacturing systems or components. Classical model-based or physics-based prognostics often require an in-depth physical understanding of the system of interest to develop closed-form mathematical models. However, prior knowledge of system behavior is not always available, especially for complex manufacturing systems and processes. To complement model-based prognostics, data-driven methods have been increasingly applied to machinery prognostics and maintenance management, transforming legacy manufacturing systems into smart manufacturing systems with artificial intelligence. While previous research has demonstrated the effectiveness of data-driven methods, most of these prognostic methods are based on classical machine learning techniques, such as artificial neural networks (ANNs) and support vector regression (SVR). With the rapid advancement in artificial intelligence, various machine learning algorithms have been developed and widely applied in many engineering fields. The objective of this research is to introduce a random forests (RFs)-based prognostic method for tool wear prediction as well as compare the performance of RFs with feed-forward back propagation (FFBP) ANNs and SVR. Specifically, the performance of FFBP ANNs, SVR, and RFs are compared using an experimental data collected from 315 milling tests. Experimental results have shown that RFs can generate more accurate predictions than FFBP ANNs with a single hidden layer and SVR.

Data-Driven Prognostics Using Random Forests: Prediction of Tool Wear

2017 ◽  
Author(s):  
Dazhong Wu ◽  
Connor Jennings ◽  
Janis Terpenny ◽  
Robert Gao ◽  
Soundar Kumara
Keyword(s):  
Artificial Intelligence ◽  
Tool Wear ◽  
Random Forests ◽  
Manufacturing Systems ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Support Vector ◽  
Model Based ◽  
Smart Manufacturing Systems

Manufacturers have faced an increasing need for the development of predictive models that help predict mechanical failures and remaining useful life of a manufacturing system or its system components. Model-based or physics-based prognostics develops mathematical models based on physical laws or probability distributions, while an in-depth physical understanding of system behaviors is required. In practice, however, some of the distributional assumptions do not hold true. To overcome the limitations of model-based prognostics, data-driven methods have been increasingly applied to machinery prognostics and maintenance management, transforming legacy manufacturing systems into smart manufacturing systems with artificial intelligence. While earlier work demonstrated the effectiveness of data-driven approaches, most of these methods applied to prognostics and health management (PHM) in manufacturing are based on artificial neural networks (ANNs) and support vector regression (SVR). With the rapid advancement in artificial intelligence, various machine learning algorithms have been developed and widely applied in many engineering fields. The objective of this research is to explore the ability of random forests (RFs) to predict tool wear in milling operations. The performance of ANNs, SVR, and RFs are compared using an experimental dataset. The experimental results have shown that RFs can generate more accurate predictions than ANNs and SVR in this experiment.

A Review of Machine Learning Methods Applied for Handling Zero-Day Attacks in the Cloud Environment

2020 ◽  
pp. 364-387
Author(s):  
Swathy Akshaya M. ◽  
Padmavathi Ganapathi
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Computing System ◽  
Cloud Environment ◽  
Learning Methods ◽  
Public Network ◽  
Machine Learning Methods ◽  
Remote Server ◽  
Cloud Computing System ◽  
Main Thing

Cloud computing is an emerging technological paradigm that provides a flexible, scalable, and reliable infrastructure and services for organizations. Services of cloud computing is based on sharing; thus, it is open for attacker to attack on its security. The main thing that grabs the organizations to adapt the cloud computing technology is cost reduction through optimized and efficient computing, but there are various vulnerabilities and threats in cloud computing that affect its security. Providing security in such a system is a major concern as it uses public network to transmit data to a remote server. Therefore, the biggest problem of cloud computing system is its security. The objective of the chapter is to review Machine learning methods that are applied to handle zero-day attacks in a cloud environment.

A Bayesian Machine Learning Approach for Efficient Integrity Management of Steel Lazy Wave Risers

2020 ◽  
Author(s):  
Rasoul Hejazi ◽  
Andrew Grime ◽  
Mark Randolph ◽  
Mike Efthymiou
Keyword(s):  
Machine Learning ◽  
Fatigue Failure ◽  
Learning Process ◽  
Predictive Models ◽  
Structural Integrity ◽  
Degradation Mechanism ◽  
Data Driven ◽  
Practical Implementation ◽  
Integrity Management

Abstract In-service integrity management (IM) of steel lazy wave risers (SLWRs) can benefit significantly from quantitative assessment of the overall risk of system failure as it can provide an effective tool for decision making. SLWRs are prone to fatigue failure within their touchdown zone (TDZ). This failure mode needs to be evaluated rigorously in riser IM processes because fatigue is an ongoing degradation mechanism threatening the structural integrity of risers throughout their service life. However, accurately evaluating the probability of fatigue failure for riser systems within a useful time frame is challenging due to the need to run a large number of nonlinear, dynamic numerical time domain simulations. Applying the Bayesian framework for machine learning, through the use of Gaussian Processes (GP) for regression, offers an attractive solution to overcome the burden of prohibitive simulation run times. GPs are stochastic, data-driven predictive models which incorporate the underlying physics of the problem in the learning process, and facilitate rapid probabilistic assessments with limited loss in accuracy. This paper proposes an efficient framework for practical implementation of a GP to create predictive models for the estimation of fatigue responses at SLWR hotspots. Such models are able to perform stochastic response prediction within a few milliseconds, thus enabling rapid prediction of the probability of SLWR fatigue failure. A realistic North West Shelf (NWS) case study is used to demonstrate the framework, comprising a 20” SLWR connected to a representative floating facility located in 950 m water depth. A full hindcast metocean dataset with associated statistical distributions are used for the riser long-term fatigue loading conditions. Numerical simulation and sampling techniques are adopted to generate a simulation-based dataset for training the data-driven model. In addition, a recently developed dimensionality reduction technique is employed to improve efficiency and reduce complexity of the learning process. The results show that the stochastic predictive models developed by the suggested framework can predict the long-term TDZ fatigue damage of SLWRs due to vessel motions with an acceptable level of accuracy for practical purposes.

Application of Machine Learning Techniques for Real Time Rate of Penetration Optimization

2021 ◽  
Author(s):  
Asad Mustafa Elmgerbi ◽  
Clemens Peter Ettinger ◽  
Peter Mbah Tekum ◽  
Gerhard Thonhauser ◽  
Andreas Nascimento
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Real Time ◽  
Predictive Models ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Time Rate ◽  
Rate Of Penetration ◽  
Drilling Parameters ◽  
Learning Techniques

Abstract Over the past decade, several models have been generated to predict Rate of Penetration (ROP) in real-time. In general, these models can be classified into two categories, model-driven (analytical models) and data-driven models (based on machine learning techniques), which is considered as cutting-edge technology in terms of predictive accuracy and minimal human interfering. Nevertheless, most existing machine learning models are mainly used for prediction, not optimization. The ROP ahead of the bit for a certain formation layer can be predicted with such methods, but the limitation of the applications of these techniques is to find an optimum set of operating parameters for the optimization of ROP. In this regard, two data-driven models for ROP prediction have been developed and thereafter have been merged into an optimizer model. The purpose of the optimization process is to seek the ideal combinations of drilling parameters that would lead to an improvement in the ROP in real-time for a given formation. This paper is mainly focused on describing the process of development to create smart data-driven models (built on MATLAB software environment) for real-time rate of penetration prediction and optimization within a sufficient time span and without disturbing the drilling process, as it is typically required by a drill-off test. The used models here can be classified into two groups: two predictive models, Artificial Neural Network (ANN) and Random Forest (RF), in addition to one optimizer, namely genetic algorithm. The process started by developing, optimizing, and validation of the predictive models, which subsequently were linked to the genetic algorithm (GA) for real-time optimization. Automated optimization algorithms were integrated into the process of developing the productive models to improve the model efficiency and to reduce the errors. In order to validate the functionalities of the developed ROP optimization model, two different cases were studied. For the first case, historical drilling data from different wells were used, and the results confirmed that for the three known controllable surface drilling parameters, weight on bit (WOB) has the highest impact on ROP, followed by flow rate (FR) and finally rotation per minute (RPM), which has the least impact. In the second case, a laboratory scaled drilling rig "CDC miniRig" was utilized to validate the developed model, during the validation only the previous named parameters were used. Several meters were drilled through sandstone cubes at different weights on bit, rotations per minute, and flow rates to develop the productive models; then the optimizer was activated to propose the optimal set of the used parameters, which likely maximize the ROP. The proposed parameters were implemented, and the results showed that ROP improved as expected.

scholarly journals Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease

2018 ◽  
Author(s):  
Andrew J. Steele ◽  
S. Aylin Cakiroglu ◽  
Anoop D. Shah ◽  
Spiros C. Denaxas ◽  
Harry Hemingway ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Practice ◽  
Electronic Health Records ◽  
Random Forests ◽  
Missing Values ◽  
Elastic Net ◽  
Data Driven ◽  
Learning Approaches ◽  
Health Records ◽  
Cox Models

AbstractPrognostic modelling is important in clinical practice and epidemiology for patient management and research. Electronic health records (EHR) provide large quantities of data for such models, but conventional epidemiological approaches require significant researcher time to implement. Expert selection of variables, fine-tuning of variable transformations and interactions, and imputing missing values in datasets are time-consuming and could bias subsequent analysis, particularly given that missingness in EHR is both high, and may carry meaning.Using a cohort of over 80,000 patients from the CALIBER programme, we performed a systematic comparison of several machine-learning approaches in EHR. We used Cox models and random survival forests with and without imputation on 27 expert-selected variables to predict all-cause mortality. We also used Cox models, random forests and elastic net regression on an extended dataset with 586 variables to build prognostic models and identify novel prognostic factors without prior expert input.We observed that data-driven models used on an extended dataset can outperform conventional models for prognosis, without data preprocessing or imputing missing values, and with no need to scale or transform continuous data. An elastic net Cox regression based with 586 unimputed variables with continuous values discretised achieved a C-index of 0.801 (bootstrapped 95% CI 0.799 to 0.802), compared to 0.793 (0.791 to 0.794) for a traditional Cox model comprising 27 expert-selected variables with imputation for missing values.We also found that data-driven models allow identification of novel prognostic variables; that the absence of values for particular variables carries meaning, and can have significant implications for prognosis; and that variables often have a nonlinear association with mortality, which discretised Cox models and random forests can elucidate.This demonstrates that machine-learning approaches applied to raw EHR data can be used to build reliable models for use in research and clinical practice, and identify novel predictive variables and their effects to inform future research.

scholarly journals Predictive Wireless Channel Modeling of MmWave Bands Using Machine Learning

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3114
Author(s):  
Abdallah Mobark Aldosary ◽  
Saud Alhajaj Aldossari ◽  
Kwang-Cheng Chen ◽  
Ehab Mahmoud Mohamed ◽  
Ahmed Al-Saman
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Wireless Communication ◽  
Random Forests ◽  
Communication Systems ◽  
Channel Modeling ◽  
Base Station ◽  
Wireless Channel ◽  
Base Stations ◽  
Data Driven

The exploitation of higher millimeter wave (MmWave) is promising for wireless communication systems. The goals of machine learning (ML) and its subcategories of deep learning beyond 5G (B5G) is to learn from the data and make a prediction or a decision other than relying on the classical procedures to enhance the wireless design. The new wireless generation should be proactive and predictive to avoid the previous drawbacks in the existing wireless generations to meet the 5G target services pillars. One of the aspects of Ultra-Reliable Low Latency Communications (URLLC) is moving the data processing tasks to the cellular base stations. With the rapid usage of wireless communications devices, base stations are required to execute and make decisions to ensure communication reliability. In this paper, an efficient new methodology using ML is applied to assist base stations in predicting the frequency bands and the path loss based on a data-driven approach. The ML algorithms that are used and compared are Multilelayers Perceptrons (MLP) as a neural networks branch and Random Forests. Systems that consume different bands such as base stations in telecommunications with uplink and downlink transmissions and other internet of things (IoT) devices need an urgent response between devices to alter bands to maintain the requirements of the new radios (NR). Thus, ML techniques are needed to learn and assist a base station to fluctuate between different bands based on a data-driven system. Then, to testify the proposed idea, we compare the analysis with other deep learning methods. Furthermore, to validate the proposed models, we applied these techniques to different case studies to ensure the success of the proposed works. To enhance the accuracy of supervised data learning, we modified the random forests by combining an unsupervised algorithm to the learning process. Eventually, the superiority of ML towards wireless communication demonstrated great accuracy at 90.24%.

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