A Fault Diagnosis and Predictive Maintenance Algorithm for Mechanical Systems Based on Deep Learning
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Implementing a predictive maintenance model does not have to be a five year, million dollar project. There are relatively simple steps a maintenance team can take to see results within the first year. Because overall cost of predictive maintenance is up to four time less expensive than preventative maintenance, as many assets as possible should be covered by predictive maintenance. Facilitating the transition to predictive maintenance is made easier with a Computerized Maintenance Management System. A good CMMS is user-friendly, automatically produces preventative maintenance work orders, and tracks all work done on each piece of machinery. IR scans and vibration analysis are two predictive maintenance techniques that can increase uptime. IR scans are an effective way to find loose or dirty electrical connections before they cause machine down time. Vibration analyses show bearing faults before the bearing locks up and destroys the journal.
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Current trend of digitization offers a whole new approach to providing maintenance services. From sensors to cloud, production data is a unique opportunity to declare a real added value for maintenance work. Predictive maintenance is one of the key elements of the Industry 4.0 concept associated with the emerging digitalization of industry. The purpose of predictive maintenance is to predict the status of production equipment and detect potential failure. Practical implementations of predictive maintenance are already found in a number of industrial companies, either in the partial form of maintenance according to technical condition, or in diagnostic maintenance, and in the form of actual equipment wear prediction and planned maintenance. Predictive maintenance extends routine health monitoring to look into the future of machines, offering options to increase efficiency and reduce overall operating costs. First part of this article presents the term of maintenance, its definition and role in the company. Also, various types of machinery maintenance types are presented. Next part of the article focuses on predictive maintenance.
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This paper presents applicative studies related to the implementation of the supervisor system and predictive maintenance in the paper industry. After analyzing the predictive maintenance concept are presented using a case study the advantages of the predictive maintenance concept. The paper present the application of the predictive maintenance concept, at S.C. SOMES Dej Company for the black lye pump of regeneration boiler used in the paper industry. By analyzing the possible defect causes of the equipment it was elaborated a repair procedure, which follows the predictive maintenance methodology. In order to find out what are the damages of the equipment a vibration detector was used in order to collect the data. Analyzing the collected data some solutions has been proposed in order to avoid the possible damages. Key words: predictive maintenance, paper industry
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article Free Access Share on Remarks on Algorithm 332: Jacobi polynomials: Algorithm 344: student's t-distribution: Algorithm 351: modified Romberg quadrature: Algorithm 359: factoral analysis of variance Author: Arthur H. J. Sale Univ. of Sydney, Sydney, Australia Univ. of Sydney, Sydney, AustraliaView Profile Authors Info & Claims Communications of the ACMVolume 13Issue 7July 1970 https://doi.org/10.1145/362686.362700Published:01 July 1970Publication History 0citation275DownloadsMetricsTotal Citations0Total Downloads275Last 12 Months10Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
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The common practice in most of the oil and gas organizations is to use periodic and preventive (time-based) maintenance techniques. These time-based maintenance techniques apply checking on equipment at regular intervals to avoid failures. However, they can lead to high maintenance cost with either over maintenance or unscheduled downtime. This issue can be encountered by using predictive maintenance which predicts future equipment failures ahead of time. This method performs maintenance based on actual operating condition of equipment which reduces maintenance cost and eliminates the need for periodic maintenance. The purpose of this work is to develop a user-friendly Graphical User Interface (GUI) application based predictive maintenance data analytic interface with the implementation of Multiple Linear Regression predictive maintenance technique. Multiple data sets of parameters of Booster Compressor (BC) are used on the proposed GUI to determine the accuracy of future prediction through implementation of Multiple Linear Regression technique.
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Abstract: Deep fake is a rapidly growing concern in society, and it has become a significant challenge to detect such manipulated media. Deep fake detection involves identifying whether a media file is authentic or generated using deep learning algorithms. In this project, we propose a deep learning-based approach for detecting deep fakes in videos. We use the Deep fake Detection Challenge dataset, which consists of real and Deep fake videos, to train and evaluate our deep learning model. We employ a Convolutional Neural Network (CNN) architecture for our implementation, which has shown great potential in previous studies. We pre-process the dataset using several techniques such as resizing, normalization, and data augmentation to enhance the quality of the input data. Our proposed model achieves high detection accuracy of 97.5% on the Deep fake Detection Challenge dataset, demonstrating the effectiveness of the proposed approach for deep fake detection. Our approach has the potential to be used in real-world scenarios to detect deep fakes, helping to mitigate the risks posed by deep fakes to individuals and society. The proposed methodology can also be extended to detect in other types of media, such as images and audio, providing a comprehensive solution for deep fake detection.
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On our planet, skin cancer is among the most dangerous diseases. It is, however, difficult to diagnose skin cancer correctly. A variety of tasks have recently been shown to be excelled by machine learning and deep learning algorithms. In the case of skin diseases, these algorithms are very useful. In this article, we examine various machine learning and deep learning techniques and their use in diagnosing skin diseases. In this paper, we discuss common skin diseases and the method of acquiring images from dermatology, and we present several freely available datasets. Our focus shifts to exploring popular machine learning and deep learning architectures and popular frameworks for implementing machine and deep learning algorithms once we have introduced machine learning and deep learning concepts. Following that, performance evaluation metrics are presented. Here we are going to review the literature on machine and deep learning and how these technologies can be used to detect skin diseases. Furthermore, we discuss potential research directions and the challenges in the area. In this paper, the principal goal is to describe contemporary machine learning and deep learning methods for skin disease diagnosis
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article Free AccessRemarks on algorithms 372: Algorithm 401: An algorithm to produce complex primes, csieve: an improved algorithm to produce complex primes Author: Paul Bratley Univ. de Montréal, Quebec, Canada Univ. de Montréal, Quebec, CanadaView Profile Authors Info & Claims Communications of the ACMVolume 13Issue 1101 November 1970https://doi.org/10.1145/362790.362805Published:01 November 1970Publication History 0citation192DownloadsMetricsTotal Citations0Total Downloads192Last 12 Months9Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
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