Hang Cheng¹, Jiawen Fei¹, Jianfeng Wen^1,*, Shan-Tung Tu^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.2, pp. 1-1, 2023, DOI:10.32604/icces.2023.09663

Abstract Alkaline water electrolysis system for hydrogen production has the characteristics of complex structure, fault coupling and state nonlinearity, coupled with the restriction by many factors such as data acquisition methods and analysis methods. The operation status cannot be fully characterized through current monitoring information. In order to solve the problems in health status assessment in the operation of alkaline water electrolysis system, a digital twin-driven predictive maintenance method is put forward to achieve the real-time monitoring of operation status and prediction of remaining useful life. In the study, a multi-disciplinary simulation model of the alkaline electrolysis system and a physical… More >

Majida Kazmi^1,*, Maria Tabasum Shoaib^1,2, Arshad Aziz³, Hashim Raza Khan^1,2, Saad Ahmed Qazi^1,2

Computer Systems Science and Engineering, Vol.47, No.1, pp. 255-272, 2023, DOI:10.32604/csse.2023.038464

Abstract Predictive maintenance is a vital aspect of the industrial sector, and the use of Industrial Internet of Things (IIoT) sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor conditions. An integrated approach for acquiring, processing, and wirelessly transmitting a large amount of data in predictive maintenance applications remains a significant challenge. This study presents an IIoT-based sensor node for industrial motors. The sensor node is designed to acquire vibration data on the radial and axial axes of the motor and utilizes a hybrid approach for efficient data processing via edge and cloud platforms. The initial step… More >

Dongyan Shi^1,*, Hui Ma¹, Chunlong Ma^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1899-1923, 2023, DOI:10.32604/cmes.2022.022444

Abstract In multi-component systems, the components are dependent, rather than degenerating independently, leading to changes in maintenance schedules. In this situation, this study proposes a grouping dynamic maintenance strategy. Considering the structure of multi-component systems, the maintenance strategy is determined according to the importance of the components. The strategy can minimize the expected depreciation cost of the system and divide the system into optimal groups that meet economic requirements. First, multi-component models are grouped. Then, a failure probability model of multi-component systems is established. The maintenance parameters in each maintenance cycle are updated according to the failure probability of the components.… More > Graphic Abstract

Pelin Yildirim Taser^*

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

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 taking into the natural… 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

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 computers for control operations and… More >

Qihang Wang, Gang Wu^*

Computer Systems Science and Engineering, Vol.36, No.1, pp. 225-237, 2021, DOI:10.32604/csse.2021.014100

Abstract AI approaches have been introduced to predict the remaining useful life (RUL) of a machine in modern industrial areas. To apply them well, challenges regarding the high dimension of the data space and noisy data should be met to improve model efficiency and accuracy. In this study, we propose an end-to-end model, termed ACB, for RUL predictions; it combines an autoencoder, convolutional neural network (CNN), and bidirectional long short-term memory. A new penalized root mean square error loss function is included to avoid an overestimation of the RUL. With the CNN-based autoencoder, a high-dimensional data space can be mapped into… More >

Marius Becherer^∗, Michael Zipperle^†, Achim Karduck^‡

Computer Systems Science and Engineering, Vol.35, No.2, pp. 81-89, 2020, DOI:10.32604/csse.2020.35.081

Abstract Machines are serviced too often or only when they fail. This can result in high costs for maintenance and machine failure. The trend of Industry 4.0 and the networking of machines opens up new possibilities for maintenance. Intelligent machines provide data that can be used to predict the ideal time of maintenance. There are different approaches to create a forecast. Depending on the method used, appropriate conditions must be created to improve the forecast. In this paper, results are compiled to give a state of the art of predictive maintenance. First, the different types of maintenance and economic relationships are… More >