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Zeitschriftenartikel zum Thema "Marchant Calculating Machine Company"
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Alda, Tania, Andika Sukma Ompusunggu, Ahmad Shalihin, Chindy Elsanna Revadi, Fadylla Ramadhani Putri Nasution und Naomi Cevania Purba. „Analisis Pengendalian Kualitas Produk Tiang Pancang di PT. X dengan Menggunakan Metode Failure Mode and Effect Analysis (FMEA)“. Jurnal Manajemen Rekayasa dan Inovasi Bisnis 2, Nr. 2 (21.02.2024): 61–69. http://dx.doi.org/10.62375/jmrib.v2i2.284.
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This research was conducted at a company engaged in the precast concrete industry. The company produces and distributes piling products both domestically and abroad. The quality of pile products made by the company is one of the considerations of consumers. For this reason, the company must provide the best quality by utilizing all production factors as efficiently as possible. This is done to maintain consumer confidence and reduce the number of products that must meet company standards. This research aims to find the leading cause of defects in pile products. The method used is failure mode and effect analysis (FMEA). The FMEA method will look at the factors that cause defects based on the highest risk priority number (RPN). From the research results obtained, four factors cause defects in pile products, namely human factors, machine factors, material factors and method factors. Based on calculating the priority risk value in the FMEA method, the RPN value results are Machine Factors of 192, Material Factors of 180, Human Factors of 120, and Method Factors of 75. The factor causing the highest pile product defects is the machine factor, with an RPN value of 192. Based on this, it is known that the spinning machine used is too old and many machine components need to be replaced, so the proposed improvements given are to carry out regular and scheduled machine checks and maintenance and immediately repair or replace damaged machine components.
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Muhammad Ferry Nasichul Achab und Hery Murnawan. „Perhitungan Kelayakan Investasi Mesin Amplas Pada UD. Surya Sejati Dengan Pendekatan NPV Dan IIR“. Jurnal Ilmiah Teknik Informatika dan Komunikasi 3, Nr. 2 (23.06.2023): 159–67. http://dx.doi.org/10.55606/juitik.v3i2.511.
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Surya Sejati is a company engaged in manufacturing backrests, seats and table bases, in the production process of table mats there are problems of wasting time in the process of refining table mats, this study aims to analyze the activities of the production process and provide suggestions in the form of a sanding machine , and the results obtained are reduced production time, and work efficiency, the feasibility of the sanding machine will be tested by calculating the depreciation, machine costs, and production capacity time on the sanding machine, in calculating the feasibility of the sanding machine investment using the NPV (net present value) approach ) and IIR (internal rate of return) in order to determine whether the investment is feasible or not in business management at UD. Surya Sejati, in the calculation of investment feasibility the NPV value is more than 0, which is Rp.478,626,819. and the IIR (internal rate of return) value is 30% and the MAAR (Minimum Attractive Rate of Return) value is 12%, with a value (IIR > MAAR)
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Rachman, Taufiqur, Darmiolla Natasia Watunglawar, M. Derajat Amperajaya, Septian Rahmat Adnan und Iphov Kumala Sriwana. „Penentuan Interval Waktu Penggantian dan Perbaikan Komponen Kritis Mesin Bubut Type SS-850 di PT. Hamdan Jaya Makmur Dengan Metode Age Replacement“. Jurnal METRIS 23, Nr. 01 (06.08.2022): 52–61. http://dx.doi.org/10.25170/metris.v23i01.3547.
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PT. Hamdan Jaya Makmur is a company that runs in the field of Machining, Engineering, Fabrication, Stamping, and Trading Company, which produces products for small, medium and modern industries. Less than optimal maintenance system with corrective maintenance causes PT. Hamdan Jaya Makmur often experiences machine failure. Based on historical data, it can be seen that the lathe machine type SS-850 experienced very high downtime. Therefore, it is necessary to plan optimal and preventive maintenance of the machine in order to produce according to the target and increase the reliability of the machine. The purpose of this research is to determine the time interval and cost of preventive replacement on components of the lathe machine type SS-850 at PT. Hamdan Jaya Makmur. The method used in this research is the Age Replacement method, namely by determining critical components, determining the distribution pattern of damage, determining distribution parameters, and calculating MTTF and MTTR, where the results obtained are preventive replacement time intervals, and calculating preventive replacement costs. The results obtained from this research are that there are four critical components on the lathe machine type SS-850, namely electric components with a preventive replacement time interval of 11 days and a preventive replacement cost of Rp.3,080,679,253, bearing components have a preventive replacement time interval of 14 days and a preventive replacement cost of Rp.4,931,989,307, the gearbox component has a preventive replacement time interval of 33 days and a preventive replacement cost of Rp.11,621,532,829, and bolt and nut components have a preventive replacement time interval of 11 days and a preventive replacement cost of Rp.1,615,296,412.
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Syarifah Alda Azlika, Kurnia Diana, Mardian Adma Gumilang, Erik Mario Sihotang, Indrayani, Muammar Khaddafi und Damsar. „THE IMPORTANCE OF CAPITAL BUDGETING IN LONG TERM INVESTMENT DECISION MAKING“. Journal of Accounting Research, Utility Finance and Digital Assets 1, Nr. 4 (30.04.2023): 602–6. http://dx.doi.org/10.54443/jaruda.v1i4.89.
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Lack of significant planning in investing by a company. This because in planning an investment project of course requires substantial funds, so if not budgeted and calculated properly, it can result in investment failure projects that can cause a company to experience large losses. This study discusses capital budgeting of a project in CV. ABC will buy a new machine. In the This study discussed how to calculate the initial investment, estimate the income that the company will get during the project, how long is the capital issued by the company for investment projects will be returned, and at most what is important is whether it is feasible or not is the investment project planning. Method used in capital budgeting calculations is the payback period, discounted payback period, Net Present Value (NPV), and Internal rate of Return (IRR). In the The results showed that CV ABC accepted the plan to purchase a corn drying machine by calculating the payback period for 5 years, the NPV and IRR are considered feasible.
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Ahmadzai, Nazak, Hameedullah Mohammadi und Naqibullah Mangal. „Data Mining Techniques in Telecommunication Company“. Journal for Research in Applied Sciences and Biotechnology 2, Nr. 1 (09.02.2023): 96–98. http://dx.doi.org/10.55544/jrasb.2.1.12.
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Due to emerging of amalgam amount of data from variety sources, the data mining has become a hot trend in field of Computer Science. Data mining extracts useful pattern and information from huge amount of existing data with the help of machine learning algorithms that can be helpful in solving many sophisticated problems.
Telecommunication companies also generates big amount of data from providing services to their customers, besides that telecommunication companies suffers from many problems like fraud, Customer churn and …etc.
The generated amount of data from these companies can help them to address the solution for their problems such as Customer Churn. Customer churn indicates to the event when a customer stops using the service of a company and starts to use the service of another company.
Churning of a Customer plays a vital role in having a sustainable business development for a telecommunication company since attracting new customers do not profit a company without retaining the old ones.
Data mining can address the problem by predicting the occurrence of customer churn in Telecom Company, which helps the company to be proactive in this event and can have the chance to retain them before the churn occurs.
In this study, I have chosen two open Telecom Churn data sets and have applied Support Vector Machine, Logistic Regression and Decision Tree Machine Learning Algorithms on each data sets independently, which conclude my work to six experiments.
I have used k-fold cross validation as validation technique during my experiments and confusion matrix for calculating the accuracy of each algorithm, the result of experiments will provide the accuracy of each algorithm in churn prediction for each data set.
At the end we will have a general comparison table from all six experiments which will show the algorithms performance summary and will indicate which algorithm will outperform the others.
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Septian Effendy, Septian Effendy, Rio Rahmat Ramadhan und Langga Pratama. „Analisis Penentuan Biaya Produksi Dengan Menggunakan Metode Full Costing Untuk Menentukan Harga Jual Pada PT Sumber Rezeki Internasional“. Jurnal Ilmu Ekonomi, Manajemen dan Bisnis 2, Nr. 1 (31.01.2024): 34–40. http://dx.doi.org/10.30787/jiembi.v2i1.1406.
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A company that focuses on generating profits will definitely make every effort to maintain the survival of its company. One of the ways to increase profits is by raising the selling price of its products. In carrying out the production process to create a product with market value, manufacturing companies incur various costs. These costs are classified as production elements, such as raw material costs, direct labor costs, and factory overhead costs. The purpose of this study is to apply the full costing method to determine the selling price based on production costs. The analytical method used in this study is qualitative with a descriptive approach. The study's results indicate that the company does calculate its production costs when setting the selling price. However, when calculating factory overhead costs, the company does not include expenses such as machine and equipment depreciation costs, component material costs, and insurance costs.
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Candrianto, Candrianto, Rizaldi Sardani, Rizki Fadhillah Lubis und M. Zakaria. „Analisis Penyebab Kegagalan Mesin Wrapping Menggunakan Failure Mode And Effect Analysis di PT. X“. INVENTORY: Industrial Vocational E-Journal On Agroindustry 2, Nr. 1 (30.06.2021): 33. http://dx.doi.org/10.52759/inventory.v2i1.58.
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PT. X is an industry that produces green tea and black tea. In this study, there are often problems with the wrapping machine due to the age of use and the tight schedule of usage in the wrapping machine. If the machine operates in an unstable condition, it will greatly affect the results of the production. Damaged components such as cutting blades, heating heaters, thermo control and bearings. By using the Failure Mode and Effect Analysis (FMEA) method and also calculating the Risk Priority Number (RPN) value, the highest RPN value to the lowest is obtained, namely Cutting Knife (RPN = 100), Heater (RPN = 90), Thermo Control (RPN = 72) and Bearings (RPN = 36). It can be seen that the cause of damage to the wrapping machine and also the highest RPN calculation is found in the damage to the cutting knife whose RPN value is 100. From the analysis of the damage to the cutting knife, the author provides a suggested repair plan for the company so that it can be implemented in the company which can later improve the quality of the machine to be operated. for the future which cannot be separated from the supervisory role of the operators in charge of their cooperation to improve quality and the quality control team that always monitors quality.
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Baron, Petr, Jozef Dobránsky, Martin Pollák, Tomáš Cmorej und Marek Kočiško. „Proposal of the Knowledge Application Environment of Calculating Operational Parameters for Conventional Machining Technology“. Key Engineering Materials 669 (Oktober 2015): 95–102. http://dx.doi.org/10.4028/www.scientific.net/kem.669.95.
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Calculation of the machining parameters is one of the basic routine operations carried out before initializing the work and technological operations on a machine tool. Basis for calculating these parameters is obtained from the technical documentation in the form of rules, tables, catalogues and company literature. It can be concluded that this is a routine activity which represents a significant proportion of the activities performed before actual production of the product. It is exactly the routine activities that are possible to underpin and process well with the help of computers and appropriate application equipment. This contribution describes the design and implementation of a knowledge based system of calculation of the processing and operational machining parameters with the possibility of parameter correction with regard to specific technological equipment. The result is the obtaining of an optimal machine setting for machined range of products with a consideration for the production efficiency, durability of the cutting tool and the possibility of reusing the archived operational parameters.
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Irfandi Ambia, Mahmuddin Marbun und Zuhrahmi DE. „Analysis of Screw Press Machine Performance Using the Overall Equipment Effectiveness Method at PT Agro Synergy Nusantara“. Jurnal Inotera 9, Nr. 1 (13.03.2024): 88–97. http://dx.doi.org/10.31572/inotera.vol9.iss1.2024.id301.
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PT Agro Sinergi Nusantara PKS Aceh Seujahtera is a company engaged in the plantation and processing of oil palm fruit, where the production is in the form of crude palm oil (CPO), palm kernel and fiber. This factory has 2 horizontal type sterilizers with a capacity of 40 tons each. One machine that has a vital role in the production process is the screw press. Based on the results of observations and interviews with the company, it was found that the screw press machine was often damaged in the production process, as a result the production process was stopped and production results were not optimal, besides that the company would also experience losses. The purpose of this research is to measure the performance of the Screw Press Machine at PT Agro Sinergi Nusantara by calculating OEE through 3 main factors, namely Availability Ratio, Performance Effeciency Ratio, and Quality Ratio and determining corrective actions that can improve the performance of the Screw Press machine. The results of the performance analysis on the screw press machine obtained an Availability Ratio value of 84.52%, Performance Effeciency Ratio of 86.68%, Quality Ratio of 100%, and OEE of 73.37%. Based on a comparison with the Word Class Ideal Standard, only the Quality Ratio value meets the standard while the Availability Ratio, Performance Effeciency Ratio, and OEE have not met, meaning that the overall performance of the Screw Press Machine has decreased and has not met the Word Class Ideal Standard and it is necessary to improve the performance of the machine. Recommendations for improvements that can be made to improve the performance of the screw press machine again are to carry out routine maintenance on machine components so that damage can be prevented before severe damage occurs and cause longer downtime, conduct training for workers so that they can develop themselves and know more about the machine and the responsibilities that must be carried out, reinforce the implementation of routine and scheduled maintenance (preventive maintenance), and increase supervision during the FFB sorting process.
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Agus, Agus suparna, und Mukhlis Ali. „Penggunan Safety Stock Order Untuk Mengatasi Delay Waktu Pada Proses Produksi Paracetamol Infus disalah Satu PT. Farmasi Di Sukabumi“. Jurnal Permadi : Perancangan, Manufaktur, Material dan Energi 5, Nr. 1 (31.01.2023): 1–9. http://dx.doi.org/10.52005/permadi.v5i1.104.
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In a company, of course there are lots of problems that often occur even though the system is running properly. Such as delays in goods, machine errors, transportation equipment, and others. With supply chain management, it is one of the most important factors that will be used to support and regulate operational activities in it. This research relates to the availability of raw material stocks which aims to avoid stockouts and meet demand levels. If done right, this system will increase the efficiency of supply chain management and reduce inventory costs. The main purpose of establishing the term safety stock related to inventory is to get a profit that is more in line with expectations in the company concerned. Profit will indeed be the most important factor that must be considered when building a company. The method applied is safety stock, which is an inventory prepared by a company to prevent inventory shortages when market demand is unstable, collecting data before delays occur, and calculating the required safety stock.
Dissertationen zum Thema "Marchant Calculating Machine Company"
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Hung-Chien, Chang, und 張宏謙. „Calculating and managing liquid material in an automated machine environment– A case study on X Company“. Thesis, 2018. http://ndltd.ncl.edu.tw/handle/292e55.
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碩士中原大學
工業與系統工程研究所
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In recent years, most of the electronic goods product life cycle is getting shorter and shorter, leading to an increasing demand in wafer related products. To avoid shortages or delayed shipments of the parts, a smooth supply chain material inventory management becomes critical. In order to be competitive, it is indispensable for companies to manage and control their materials efficiently. In the wafer industry, in order to reduce waste, the management of high cost liquid material is critical. Enterprises often rely on the sensors of the automated machine to monitor the usage of liquid materials. The company has no way to know or calculate the liquid material usage, Unable to set standard, nor the use of standard to predict the material requirements. Therefore, this study discusses the liquid measurement model of an automated machine tool and uses liquid measurement model to explore the standard usage of liquid materials so that the X Company can use them for instant material monitoring. Further, the liquid material standard can be used to estimate the inventory levels to avoid unnecessary accumulation. This study uses a real case study to analyze the accuracy of inventory forecasting under three different situations: non-standard dosages, standard dosages, and incorrect standard dosages. These analyses are performed to show the interactions between the actual liquid material usage and the practicability of this research model. The results show that the model we developed can indeed achieve the effect of real-time material monitoring and reduce the overall inventory.
Buchteile zum Thema "Marchant Calculating Machine Company"
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Ainatzoglou, Konstantina K., Georgios K. Tairidis, Georgios E. Stavroulakis und Constantin K. Zopounidis. „Application of Adaptive Neurofuzzy Control in the Field of Credit Insurance“. In Machine Learning Applications for Accounting Disclosure and Fraud Detection, 201–22. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4805-9.ch014.
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Credit insurance is of vital importance for the trade sector and almost every related business. Moreover, every policy in credit insurance is tailor-made in order to suit in the best available way the unique needs and demands of the insured business. Thus, pricing of such service can be tricky for an insurance company. In the present chapter, this pricing problem in the field of credit insurance will be addressed through the use of intelligent control mechanisms. More specifically, a way of calculating the price of insurance policies that has to be paid by a prospective client of an insurance company will be suggested. The model will be created and implemented with the use of fuzzy logic, and more specifically, through the implementation of an adaptive neurofuzzy inference system. The training data that will be used for the tuning of the system will be derived from real anonymous insurance policies of the Greek insurance market.
Konferenzberichte zum Thema "Marchant Calculating Machine Company"
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Biesinger, Thomas, Maximilian Kölzer, Alexander Schukmann, Harald Roclawski, Marc Kainz, Philippe Godin, Juan Carlos Morales und Laith Zori. „Application of the Harmonic Balance Method for Large Spread Multiple Frequency Scales“. In ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/gt2022-79393.
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Abstract This paper presents the application of the harmonic balance method to periodic turbomachinery flow problems containing multiple fundamental frequencies. It is well known that the solution convergence of a transient turbomachinery flow using classical integration methods is computationally intensive and requires the integration for multiple periods to achieve a converged periodic solution. The computational effort is even higher when multiple fundamental frequencies (vastly different time scales) are modeled since the time stepping required is limited by the higher frequency in the flow. Unlike the classical time integration methods, the multifrequency harmonic balance method is very well suited for these applications since it allows for much faster calculations than the standard time marching algorithms. In addition, to resolve all the fundamental frequencies and higher harmonics, the current implementation of the harmonic balance method allows for solutions on unequal time interval distributions of the time planes. Given a user-defined list of frequencies that govern the flow problem, this method utilizes an optimization strategy to compute the time planes. There are two simulation cases of interest: aerodynamic performance and forced response analyses, both with different accuracy requirements. The number of required frequencies is dependent on the goal of the simulation. For aerodynamic performance analysis, global quantities such efficiency, pressure ratio, etc. can be predicted with fewer fundamental frequencies than forced response analysis where accurate local flow details demand a higher number of fundamental frequencies. The advantages of the multifrequency harmonic balance are illustrated by modeling two radial turbine configurations subjected to an inlet pulse from a reciprocating engine. Not only the expected trends such as higher-modes modeling granularity and time transient accuracy are shown, but also the calculations agree well with experimental data. The computational effort can be up to tenfold lower than the standard time-marching simulations. Machine aerodynamic performance predictions from the harmonic balance method are compared to accurate time-marching solutions and experimental data measurements. The efficiency of the computation is also discussed. The second example will compare the predicted surface excitation from the harmonic balance method vs. the time-marching solution.
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Siahaan, Edwin, Irwan Mamat, Senna Sun Laksana, Agung Setyowibowo, Aulia Ahmad Naufal, Octy Edriana Wulandari, Sabrina Metra et al. „Well Portfolio Optimisation: Accelerating Generation of Well Intervention Candidates with Automated Analytics and Machine Learning - A Case Study from Attaka Field, Indonesia“. In SPE Asia Pacific Oil & Gas Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/210614-ms.
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Abstract Advancements in technology, complemented with the abundance of static and historical data brought AI and digital automation adapted very well into the oil and gas industry. Specially to solve the challenges by the engineers in selecting well intervention candidates. In Attaka Field, a multi-layered offshore field in Indonesia, workover and well service (WOWS) have been one of the strategies to reduce production decline. With traditional workflows that absorb data from multiple unconsolidated sources and data format and resource limitation, reviewing 400+ wells that penetrates more than 200 reservoirs may take 2-3 months process with a reduced scope of review. As an addition, not all data and values are justified for the prioritization process. An intelligent automated solution termed as WEPON was developed to improve decision speed and quality in Attaka Field WOWS candidate screening. WEPON was built on top of a data science platform to ease the development, production and maintenance of the analytics engine and its data pipeline. More than 15 data sources, ranging from reservoir properties, allocated production data, up to well schematics were consumed and aggregated in this solution's flow. The main components for WEPON includes: 1. Technical analysis with analytics and ML plus multi-criteria decision-making process to identify high potential completions, both produced and virgin ones 2. Adopting from the field's old workflow, feasibility checks to surface and subsurface constraints for the proposed completions 3. Diagnosing the wells and determine the right workover/ intervention opportunities 4. Calculating each well's subsurface and surface risks, and historical success rate to be integrated with the well's NPV to produce its expected value (EV) 5. Running on-demand economic analysis accessible from the solution's UI, the engine is tied into the operator's economic analysis tool that contains the currently used calculation and scheme 6. A presentation of the results on a web-based application. As the main process is triggered to be run on a weekly basis, the automation of WEPON helps to increase Attaka Field review size to the whole fields, as well as reducing 89.7% of time from 3 days to review a well to hours of run to review the whole field, enabling engineers to spend more time on high-cognitive components of the existing workflows. Moreover, it has shifted the approach to a more data-driven one leading up to smarter decisions. The implementation of this WEPON is the pilot in the Indonesian National Oil Company, PERTAMINA. This is also the first time the solution developed on a data science platform, allowing the tool to be evergreen and extensible process. This implementation is also the first one to integrate an economic analysis tool through its API.
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Yakoot, Mostafa Sa'eed, Adel Mohamed Salem Ragab und Omar Mahmoud. „Multi-Class Taxonomy of Well Integrity Anomalies Applying Inductive Learning Algorithms: Analytical Approach for Artificial-Lift Wells“. In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/206129-ms.
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Abstract Well integrity has become a crucial field with increased focus and being published intensively in industry researches. It is important to maintain the integrity of the individual well to ensure that wells operate as expected for their designated life (or higher) with all risks kept as low as reasonably practicable, or as specified. Machine learning (ML) and artificial intelligence (AI) models are used intensively in oil and gas industry nowadays. ML concept is based on powerful algorithms and robust database. Developing an efficient classification model for well integrity (WI) anomalies is now feasible because of having enormous number of well failures and well barrier integrity tests, and analyses in the database. Circa 9000 dataset points were collected from WI tests performed for 800 wells in Gulf of Suez, Egypt for almost 10 years. Moreover, those data have been quality-controlled and quality-assured by experienced engineers. The data contain different forms of WI failures. The contributing parameter set includes a total of 23 barrier elements. Data were structured and fed into 11 different ML algorithms to build an automated systematic tool for calculating imposed risk category of any well. Comparison analysis for the deployed models was performed to infer the best predictive model that can be relied on. 11 models include both supervised and ensemble learning algorithms such as random forest, support vector machine (SVM), decision tree and scalable boosting techniques. Out of 11 models, the results showed that extreme gradient boosting (XGB), categorical boosting (CatBoost), and decision tree are the most reliable algorithms. Moreover, novel evaluation metrics for confusion matrix of each model have been introduced to overcome the problem of existing metrics which don't consider domain knowledge during model evaluation. The innovated model will help to utilize company resources efficiently and dedicate personnel efforts to wells with the high-risk. As a result, progressive improvements on business, safety, environment, and performance of the business. This paper would be a milestone in the design and creation of the Well Integrity Database Management Program through the combination of integrity and ML.
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Chatar, Crispin, Suhas Suresha, Laetitia Shao, Soumya Gupta und Indranil Roychoudhury. „Determining Rig State from Computer Vision Analytics“. In SPE/IADC International Drilling Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/204086-ms.
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Abstract For years, many companies involved with drilling have searched for the ideal method to calculate the state of a drilling rig. While companies cannot agree on a standard definition of "rig state," they can agree that as we move forward in drilling optimization and with further use of remote operations and automation, that rig state calculation is mandatory in one form or the other. Internally in the service company, many methods exist for calculating rig state, but one new technology area holds promise to deliver a more efficient and cost-effective option with higher accuracy. This technology involves vision analytics. Currently, detection algorithms rely heavily on data collected by sensors installed on the rig. However, relying exclusively on sensor data is problematic because sensors are prone to failure and are expensive to maintain and install. By proposing a machine learning model that relies exclusively on videos collected on the rig floor to infer rig states, it is possible to move away from the existing methods as the industry moves to a future of high-tech rigs. Videos, in contrast to sensor data, are relatively easy to collect from small inexpensive cameras installed at strategic locations. Consequently, this paper presents machine learning pipeline that is implemented to perform rig state determination from videos captured on the rig floor of an operating rig. The pipeline can be described in two parts. Firstly, the annotation pipeline matches each frame of the video dataset to a rig state. A convolutional neural network (CNN) is used to match the time of the video with corresponding sensor data. Secondly, additional CNNs are trained, capturing both spatial and temporal information, to extract an estimation of rig state from videos. The models are trained on a dataset of 3 million frames on a cloud platform using graphics processing units (GPU). Some of the models used include a pretrained visual geometry group (VGG) network, a convolutional three-dimensional (C3D) model that used three-dimensional (3D) convolutions, and a two-stream model that uses optical flow to capture temporal information. The initial results demonstrate this pipeline to be effective in detecting rig states using computer vision analytics.