scholarly journals Prediction Model for Occupational Incidents in Chemical and Gas Industries

2019 ◽  
Vol 8 (4) ◽  
pp. 3836-3840
Keyword(s):  
Prediction Model ◽  
Technical Information ◽  
Workplace Safety ◽  
Machine Learning Algorithms ◽  
Streaming Data ◽  
Industrial Safety ◽  
Prediction Algorithm ◽  
Scoring Algorithm ◽  
Inspection Data ◽  
The Right

Understanding occupational incidents is one of the important measures in workplace safety strategy. Analyzing the trends of the occupational incident data helps to identify the potential pain points and helps to reduce the loss. Optimizing the Machine Learning algorithms is a relatively new trend to fit the prediction model and algorithms in the right place to support human beneficial factors. The aim of this research is to build a prediction model to identify the occupational incidents in chemical and gas industries. This paper describes the architecture and approach of building and implementing the prediction model to predict the cause of the incident which can be used as a key index for achieving industrial safety in specific to chemical and gas industries. The implementation of the scoring algorithm coupled with prediction model should bring unbiased data to obtain logical conclusion. The prediction model has been trained against FACTS (Failure and Accidents Technical information system) is an incidents database which have 25,700 chemical industrial incidents with accident descriptions for the years span from 2004 to 2014. Inspection data and sensor logs should be fed on top of the trained dataset to verify and validate the implementation. The outcome of the implementation provides insight towards the understanding of the patterns, classifications, and also contributes to an enhanced understanding of quantitative and qualitative analytics. Cutting edge cloud-based technology opens up the gate to process the continuous in-streaming data, process it and output the desired result in real-time. The primary technology stack used in this architecture is Apache Kafka, Apache Spark Streaming, KSQL, Data frames, and AWS Lambda functions. Lambda functions are used to implement the scoring algorithm and prediction algorithm to write out the results back to AWS S3 buckets. Proof of concept implementation of the prediction model helps the industries to see through the incidents and will layout the base platform for the various safety-related implementations which always benefits the workplace's reputation, growth, and have less attrition in human resources.

scholarly journals SafeOne Machine Learning model to predict industrial incidents in Chemical and Gas Industries

2021 ◽  
Vol 12 (2) ◽  
pp. 1123-1131
Author(s):  
Ganapathy Subramaniam Balasubramanian, Et. al.
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Workplace Safety ◽  
Machine Learning Algorithms ◽  
Industrial Safety ◽  
Machine Learning Model ◽  
Helpful Factors ◽  
The Right ◽  
Aws Lambda ◽  
Safety Strategy

Understanding activity incidents is one of the necessary measures in workplace safety strategy. Analyzing the trends of the activity incident information helps to spot the potential pain points and helps to scale back the loss. Optimizing the Machine Learning algorithms may be a comparatively new trend to suit the prediction model and algorithms within the right place to support human helpful factors. This research aims to make a prediction model spot the activity incidents in chemical and gas industries. This paper describes the design and approach of building and implementing the prediction model to predict the reason behind the incident which may be used as a key index for achieving industrial safety specific to chemical and gas industries. The implementation of the grading algorithmic program including the prediction model ought to bring unbiased information to get a logical conclusion. The prediction model has been trained against incident information that has 25700 chemical industrial incidents with accident descriptions for the last decade. Inspection information and incident logs ought to be chomped high of the trained dataset to verify and validate the implementation. The result of the implementation provides insight towards the understanding of the patterns, classifications, associated conjointly contributes to an increased understanding of quantitative and qualitative analytics. Innovative cloud-based technology discloses the gate to method the continual in-streaming information, method it, and output the required end in a period. The first technology stack utilized in this design is Apache Kafka, Apache Spark, KSQL, Data frames, and AWS Lambda functions. Lambda functions are accustomed implement the grading algorithmic program and prediction algorithmic program to put in writing out the results back to AWS S3 buckets. Proof of conception implementation of the prediction model helps the industries to examine through the incidents and can layout the bottom platform for the assorted protective implementations that continuously advantage the workplace's name, growth, and have less attrition in human resources.

scholarly journals A Genetic Algorithm Optimized RNN-LSTM Model for Remaining Useful Life Prediction of Turbofan Engine

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 285
Author(s):  
Kwok Tai Chui ◽  
Brij B. Gupta ◽  
Pandian Vasant
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Prediction Model ◽  
Prediction Models ◽  
Remaining Useful Life ◽  
Prediction Algorithm ◽  
Short Term ◽  
Turbofan Engine ◽  
Term Prediction ◽  
Useful Life

Understanding the remaining useful life (RUL) of equipment is crucial for optimal predictive maintenance (PdM). This addresses the issues of equipment downtime and unnecessary maintenance checks in run-to-failure maintenance and preventive maintenance. Both feature extraction and prediction algorithm have played crucial roles on the performance of RUL prediction models. A benchmark dataset, namely Turbofan Engine Degradation Simulation Dataset, was selected for performance analysis and evaluation. The proposal of the combination of complete ensemble empirical mode decomposition and wavelet packet transform for feature extraction could reduce the average root-mean-square error (RMSE) by 5.14–27.15% compared with six approaches. When it comes to the prediction algorithm, the results of the RUL prediction model could be that the equipment needs to be repaired or replaced within a shorter or a longer period of time. Incorporating this characteristic could enhance the performance of the RUL prediction model. In this paper, we have proposed the RUL prediction algorithm in combination with recurrent neural network (RNN) and long short-term memory (LSTM). The former takes the advantages of short-term prediction whereas the latter manages better in long-term prediction. The weights to combine RNN and LSTM were designed by non-dominated sorting genetic algorithm II (NSGA-II). It achieved average RMSE of 17.2. It improved the RMSE by 6.07–14.72% compared with baseline models, stand-alone RNN, and stand-alone LSTM. Compared with existing works, the RMSE improvement by proposed work is 12.95–39.32%.

Professional Retraining in the Staffing System for the Mining Enterprises

2021 ◽  
pp. 79-84
Author(s):  
O.I. Kazanin ◽  
◽  
M.A. Marinin ◽  
A.M. Blinov ◽  
◽  
...  
Keyword(s):  
Professional Education ◽  
Work Experience ◽  
Basic Education ◽  
Integrated Approach ◽  
Legal Framework ◽  
Industrial Safety ◽  
Number Of Factors ◽  
The Right ◽  
Additional Education ◽  
Engineering Personnel

The issues are considered related to providing mining enterprises with the engineering personnel capable of managing mining and blasting operations. At present, not all the mining enterprises have a full staff of specialists and managers who are legally entitled to manage mining and blasting operations. Some employees who previously had such a right, after changes in the legal framework, ceased to meet the new requirements. The analysis is presented concerning the competencies required to perform these production functions, as well as educational programs that allow acquiring these competencies. The importance of professional retraining programs for solving these problems and the imperfection of the modern regulatory framework, which practically excludes the possibility of obtaining the right to manage mining and blasting operations, even after professional retraining for persons with a higher technical education in a non-mining profile, are shown in the article. An integrated approach is proposed for resolving the issues of the admissibility of obtaining the right to manage mining and blasting operations by these persons considering a number of factors: basic education, work experience and positions held at a mining enterprise, completed training in programs of additional education and professional retraining. Such programs should be developed and implemented by the organizations with experience in training mining engineers and having a license from Rosobrnadzor for the right to implement programs not only for additional professional education, but also for higher professional mining education. The need is substantiated in developing professional standards for managers of mining and blasting operations at the enterprises for the extraction of solid minerals. Recommendations were developed for amending the federal rules and regulations in the field of industrial safety in order to ensure the possibility of using professional retraining programs for training and final certification of the managers of mining and blasting operations.

scholarly journals Heterogeneous data-processing optimization with CLARA’s adaptive workflow orchestrator

2020 ◽  
Vol 245 ◽  
pp. 05020
Author(s):  
Vardan Gyurjyan ◽  
Sebastian Mancilla
Keyword(s):  
Data Processing ◽  
Heterogeneous Systems ◽  
Optimal Solution ◽  
Heterogeneous Data ◽  
Streaming Data ◽  
Time Data ◽  
Maximum Performance ◽  
Adaptive Workflow ◽  
Main Challenge ◽  
The Right

The hardware landscape used in HEP and NP is changing from homogeneous multi-core systems towards heterogeneous systems with many different computing units, each with their own characteristics. To achieve maximum performance with data processing, the main challenge is to place the right computing on the right hardware. In this paper, we discuss CLAS12 charge particle tracking workflow orchestration that allows us to utilize both CPU and GPU to improve the performance. The tracking application algorithm was decomposed into micro-services that are deployed on CPU and GPU processing units, where the best features of both are intelligently combined to achieve maximum performance. In this heterogeneous environment, CLARA aims to match the requirements of each micro-service to the strength of a CPU or a GPU architecture. A predefined execution of a micro-service on a CPU or a GPU may not be the most optimal solution due to the streaming data-quantum size and the data-quantum transfer latency between CPU and GPU. So, the CLARA workflow orchestrator is designed to dynamically assign micro-service execution to a CPU or a GPU, based on the online benchmark results analyzed for a period of real-time data-processing.

scholarly journals Identifying Children at Readmission Risk: At-Admission Versus Traditional At-Discharge Readmission Prediction Model

Healthcare ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1334
Author(s):  
Hasan Symum ◽  
José Zayas-Castro
Keyword(s):  
Prediction Model ◽  
Information Exchange ◽  
High Risk Patient ◽  
Machine Learning Algorithms ◽  
Polynomial Kernel ◽  
Gradient Boosting ◽  
Support Vector ◽  
Hospital Discharges ◽  
Discharge Model ◽  
Readmission Risk

The timing of 30-day pediatric readmissions is skewed with approximately 40% of the incidents occurring within the first week of hospital discharges. The skewed readmission time distribution coupled with delay in health information exchange among healthcare providers might offer a limited time to devise a comprehensive intervention plan. However, pediatric readmission studies are thus far limited to the development of the prediction model after hospital discharges. In this study, we proposed a novel pediatric readmission prediction model at the time of hospital admission which can improve the high-risk patient selection process. We also compared proposed models with the standard at-discharge readmission prediction model. Using the Hospital Cost and Utilization Project database, this prognostic study included pediatric hospital discharges in Florida from January 2016 through September 2017. Four machine learning algorithms—logistic regression with backward stepwise selection, decision tree, Support Vector machines (SVM) with the polynomial kernel, and Gradient Boosting—were developed for at-admission and at-discharge models using a recursive feature elimination technique with a repeated cross-validation process. The performance of the at-admission and at-discharge model was measured by the area under the curve. The performance of the at-admission model was comparable with the at-discharge model for all four algorithms. SVM with Polynomial Kernel algorithms outperformed all other algorithms for at-admission and at-discharge models. Important features associated with increased readmission risk varied widely across the type of prediction model and were mostly related to patients’ demographics, social determinates, clinical factors, and hospital characteristics. Proposed at-admission readmission risk decision support model could help hospitals and providers with additional time for intervention planning, particularly for those targeting social determinants of children’s overall health.

scholarly journals Predicting Student’s Performance Using Machine Learning Algorithm

2021 ◽  
pp. 53-58
Author(s):  
Sheela Rani P ◽  
Dhivya S ◽  
Dharshini Priya M ◽  
Dharmila Chowdary A
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbors

Machine learning is a new analysis discipline that uses knowledge to boost learning, optimizing the training method and developing the atmosphere within which learning happens. There square measure 2 sorts of machine learning approaches like supervised and unsupervised approach that square measure accustomed extract the knowledge that helps the decision-makers in future to require correct intervention. This paper introduces an issue that influences students' tutorial performance prediction model that uses a supervised variety of machine learning algorithms like support vector machine , KNN(k-nearest neighbors), Naïve Bayes and supplying regression and logistic regression. The results supported by various algorithms are compared and it is shown that the support vector machine and Naïve Bayes performs well by achieving improved accuracy as compared to other algorithms. The final prediction model during this paper may have fairly high prediction accuracy .The objective is not just to predict future performance of students but also provide the best technique for finding the most impactful features that influence student’s while studying.

scholarly journals A Model Trend and Application of Niger Delta Ultrasonic Field Data to Manage Erosion Corrosion in Pipelines and Flowlines

2019 ◽  
Vol 4 (11) ◽  
pp. 46-51
Author(s):  
Azubuike Hope Amadi ◽  
Boniface A. Oriji
Keyword(s):  
Petroleum Industry ◽  
Ultrasonic Field ◽  
Machine Learning Algorithms ◽  
Industrial Facilities ◽  
Software Application ◽  
Management Procedure ◽  
Erosion Corrosion ◽  
Inspection Data ◽  
Python Programming ◽  
Major Decision

Corrosion of pipelines and other industrial facilities has led to several hazards and catastrophic events in the petroleum industry. The corrosion of pipelines can occur in different ways, as materials tend to go back to original state. This work focused on the erosion corrosion of pipelines due to sand production. Several analysis following best practices introduced in this work where carried out on field inspection data on pipelines and flowlines in which a trend was identified. This trend generated models such as erosion trend angle (ETA) and erosion trend factor (ETF) which will be used for reviews, judgment and sensitivity analysis during corrosion management. The criterion for criticality of ETA was 30 degrees for the purpose of this work and recommended that users could vary to fit work objectives. Five test points where used to establish ETA and ETF values for major decision of criticality for erosion corrosion as shown in Table 2. To enhance the use of these models and known corrosion tools, a software application known as HAZUTREND was developed using python programming and machine learning algorithms for linear regression. An erosion corrosion management procedure was also introduced as a result of analysis made, to optimize decision making.

scholarly journals Significant Impact of Improved Machine Learning Algorithm in The Processes of Large Data Sets

2020 ◽  
pp. 458-467
Author(s):  
Virendra Tiwari ◽  
Balendra Garg ◽  
Uday Prakash Sharma
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Dynamic Environment ◽  
Large Data ◽  
Machine Learning Algorithms ◽  
Streaming Data ◽  
Machine Learning Techniques ◽  
Machine Learning Algorithm ◽  
Learning Mechanisms

The machine learning algorithms are capable of managing multi-dimensional data under the dynamic environment. Despite its so many vital features, there are some challenges to overcome. The machine learning algorithms still requires some additional mechanisms or procedures for predicting a large number of new classes with managing privacy. The deficiencies show the reliable use of a machine learning algorithm relies on human experts because raw data may complicate the learning process which may generate inaccurate results. So the interpretation of outcomes with expertise in machine learning mechanisms is a significant challenge in the machine learning algorithm. The machine learning technique suffers from the issue of high dimensionality, adaptability, distributed computing, scalability, the streaming data, and the duplicity. The main issue of the machine learning algorithm is found its vulnerability to manage errors. Furthermore, machine learning techniques are also found to lack variability. This paper studies how can be reduced the computational complexity of machine learning algorithms by finding how to make predictions using an improved algorithm.

scholarly journals Determination of Significant Features for Building an Efficient Heart Disease Prediction System

2019 ◽  
Vol 8 (2) ◽  
pp. 4499-4504
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Model ◽  
Prediction Models ◽  
Heart Diseases ◽  
Medical Diagnostics ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
Prediction System ◽  
Early Stages

Heart diseases are responsible for the greatest number of deaths all over the world. These diseases are usually not detected in early stages as the cost of medical diagnostics is not affordable by a majority of the people. Research has shown that machine learning methods have a great capability to extract valuable information from the medical data. This information is used to build the prediction models which provide cost effective technological aid for a medical practitioner to detect the heart disease in early stages. However, the presence of some irrelevant and redundant features in medical data deteriorates the competence of the prediction system. This research was aimed to improve the accuracy of the existing methods by removing such features. In this study, brute force-based algorithm of feature selection was used to determine relevant significant features. After experimenting rigorously with 7528 possible combinations of features and 5 machine learning algorithms, 8 important features were identified. A prediction model was developed using these significant features. Accuracy of this model is experimentally calculated to be 86.4%which is higher than the results of existing studies. The prediction model proposed in this study shall help in predicting heart disease efficiently.

Research on Method of Metro Monitoring Model Based on Grey Prediction Algorithm

2014 ◽  
Vol 548-549 ◽  
pp. 641-645
Author(s):  
Mao Hua Liu ◽  
Xiu Bo Sun
Keyword(s):  
Prediction Model ◽  
Safety Monitoring ◽  
Prediction Algorithm ◽  
Grey Prediction ◽  
Monitoring Point ◽  
Value Analysis ◽  
Grey Prediction Model ◽  
Monitoring Model ◽  
Surface Monitoring ◽  
Set Up

Grey prediction model is a model to predict the trend maturely, its application in the subway safety monitoring is of great significance. Set up by MATLAB software to complete the grey prediction model, and take the surface monitoring point for example, Comparing the prediction value with the actual measured value, analysis by the accuracy, obtain the trend of surface change around the subway station.

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