Pelin Yildirim Taser^*

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1499-1516, 2023, DOI:10.32604/csse.2023.028083 - 15 June 2022

Abstract Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed. Although machine learning techniques have been frequently implemented in this area, the existing studies disregard to the natural order between the target attribute values of the historical sensor data. Thus, these methods cause losing the inherent order of the data that positively affects the prediction performances. To deal with this problem, a novel approach, named Ordinal Multi-dimensional Classification (OMDC), is proposed for estimating the conditions of a hydraulic system's four components by… More >

Rao Fu^1,*, Liming Wang², Xuesong Huo², Pei Pei², Haitao Jiang³, Zhongxing Fu⁴

Energy Engineering, Vol.119, No.6, pp. 2621-2636, 2022, DOI:10.32604/ee.2022.020873 - 14 September 2022

Abstract The power infrastructure of the power system is massive in size and dispersed throughout the system. Therefore, how to protect the information security in the operation and maintenance of power equipment is a difficult problem. This paper proposes an improved time-stamped blockchain technology biometric fuzzy feature for electrical equipment maintenance. Compared with previous blockchain transactions, the time-stamped fuzzy biometric signature proposed in this paper overcomes the difficulty that the key is easy to be stolen by hackers and can protect the security of information during operation and maintenance. Finally, the effectiveness of the proposed method More >

Atif Ikram^1,2,*, Masita Abdul Jalil¹, Amir Bin Ngah¹, Nadeem Iqbal², Nazri Kama⁴, Azri Azmi⁴, Ahmad Salman Khan³, Yasir Mahmood^3,4, Assad Alzayed⁵

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3827-3845, 2022, DOI:10.32604/cmc.2022.029609 - 16 June 2022

Abstract Properly created and securely communicated, non-disclosure agreement (NDA) can resolve most of the common disputes related to outsourcing of offshore software maintenance (OSMO). Occasionally, these NDAs are in the form of images. Since the work is done offshore, these agreements or images must be shared through the Internet or stored over the cloud. The breach of privacy, on the other hand, is a potential threat for the image owners as both the Internet and cloud servers are not void of danger. This article proposes a novel algorithm for securing the NDAs in the form of… More >

Rishi Dewan¹, Adarsh Kumar², Mohammad Khalid Imam Rahmani³, Surbhi Bhatia⁴, Md Ezaz Ahmed^3,*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5761-5776, 2022, DOI:10.32604/cmc.2022.024762 - 21 April 2022

Abstract Water injection has shown to be one of the most successful, efficient, and cost-effective reservoir management strategies. By re-injecting treated and filtered water into reservoirs, this approach can help maintain reservoir pressure, increase hydrocarbon output, and reduce the environmental effect. The goal of this project is to create a water injection model utilizing Eclipse reservoir simulation software to better understand water injection methods for reservoir pressure maintenance. A basic reservoir model is utilized in this investigation. For simulation designs, the reservoir length, breadth, and thickness may be changed to different levels. The water-oil contact was More >

Md. Mottahir Alam^1,*, Ahteshamul Haque², Mohammed Ali Khan³, Nebras M. Sobahi¹, Ibrahim Mustafa Mehedi^1,4, Asif Irshad Khan⁵

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3999-4017, 2022, DOI:10.32604/cmc.2022.026353 - 29 March 2022

Abstract The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of action. But with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow, monitoring and maintenance are a few of the prime concerns. These problems were addressed widely in the literature, but most of the research has drawbacks due to long detection time, and high misclassification error. Hence to overcome these drawbacks, and to develop an accurate monitoring approach, this paper is motivated toward the understanding of primary failure… More >

Mehdi Zandiyehvakili¹, Babak Aminnejad^2,*, Alireza Lork³

Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 499-512, 2022, DOI:10.32604/iasc.2022.021461 - 26 October 2021

Abstract This article presents a methodology to optimize the maintenance planning model and minimize the total maintenance costs of a typical school building. It makes an effort to provide a maintenance schedule, focusing on maintenance costs. In the allocation of operations to the school equipment, the parameter of its age was also taken into account. A mathematical optimization model to minimize the school maintenance cost in a three-year period was provided in the GAMS software with CPLEX solver. Finally, the optimum architecture of the Perceptron multi-layer neural network was used to predict the schedule of equipment More >

Ahmed Ali Ajmi^1,2, Noor Shakir Mahmood^1,2, Khairur Rijal Jamaludin^1,*, Hayati Habibah Abdul Talib¹, Shamsul Sarip¹, Hazilah Mad Kaidi¹

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5783-5801, 2022, DOI:10.32604/cmc.2022.021885 - 11 October 2021

Abstract Industry 4.0 is expected to play a crucial role in improving energy management and personnel performance in power plants. Poor performance problem in maintaining power plants is the result of both human errors, human factors and the poor implementation of automation in energy management. This problem can potentially be solved using artificial intelligence (AI) and an integrated management system (IMS). This article investigates the current challenges to improving personnel and energy management performance in power plants, identifies the critical success factors (CSFs) for an integrated intelligent framework, and develops an intelligent framework that enables power… More >

Sunwoo Hwang¹, Joouk Kim¹, Hagseoung Kim¹, Hyungchul Kim², Youngmin Kim^3,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5453-5466, 2022, DOI:10.32604/cmc.2022.021057 - 11 October 2021

Abstract This paper is on the suggestion of maintenance items for electric railway facility systems. With the recent increase in the use of electric locomotives, the utilization and importance of railroad electrical facility systems are also increasing, but the railroad electrical facility system in Korea is rapidly aging. To solve this problem, various methodologies are applied to ensure operational reliability and stability for railroad electrical facility systems, but there is a lack of detailed evaluation criteria for railroad electrical facility system maintenance. Also, maintenance items must be selected in a scientific and systematic method. Therefore, railroad More >

S. Gopalakrishnan^1,*, M. Senthil Kumaran²

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1435-1449, 2022, DOI:10.32604/iasc.2022.020660 - 09 October 2021

Abstract In the automotive industry, multiple predictive maintenance units run behind the scenes in every production process to support significant product development, particularly among Accessories Manufacturers (AMs). As a result, they wish to maintain a positive relationship with vehicle manufacturers by providing 100 percent quality assurances for accessories. This is only achievable if they implement an effective anticipatory strategy that prioritizes quality control before and after product development. To do this, many sensors devices are interconnected in the production area to collect operational data (humanity, viscosity, and force) continuously received from machines and sent to backend… More >

Juyoung Park^1,*, Jung Hee Lee¹, Junseong Bang^2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.3, pp. 965-976, 2021, DOI:10.32604/cmes.2021.014669 - 24 May 2021

Abstract

We propose a mobile system, called PotholeEye⁺, for automatically monitoring the surface of a roadway and detecting the pavement distress in real-time through analysis of a video. PotholeEye⁺ pre-processes the images, extracts features, and classifies the distress into a variety of types, while the road manager is driving. Every day for a year, we have tested PotholeEye⁺ on real highway involving real settings, a camera, a mini computer, a GPS receiver, and so on. Consequently, PotholeEye⁺ detected the pavement distress with accuracy of 92%, precision of 87% and recall 74% averagely during driving at an average speed of

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