On the application of uncertainty models in copying machine maintenance

Abass, Samir A.; Elsayed, Marwa Sh.

doi:10.1080/17509653.2012.10671218

Cited by 2 publications

(1 citation statement)

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“…A comparison of the actual time date with standard values for the proposed optimal decision making of the fault identification in the industry was studied [16,17]. A fault identification of the petrol engine was described through a smart monitoring system for vibration control as well as a knowledge-based decision support system of the machine maintenance activities was presented [18][19][20]. Digital control system of maintenance and manufacturing industry securities issues and challenges was described in detail via a sample model [21].…”

Section: Introductionmentioning

confidence: 99%

IIoT Based Anomaly Detection and Maintenance Management of an Industrial Rotary System

Velmurugan

Saravanasankar

Venkumar³

et al. 2022

Curr. Appl. Sci. Technol.

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Numerous challenges are being faced by implementing the Industrial Internet of Things (IIoT), which enables anomaly detection and optimal maintenance management of industrial production systems and their critical machines. Industries have started adopting this revolutionary technology along with other allied technologies to reap the full benefits out of Industry 4.0 environment. The objectives of this research work were to use the IIoT to develop a continuous monitoring system of the behavior of bottleneck facilities in a production system, to predict and avoid all possible failures, and to improve the overall productivity of the manufacturing system. The proposed prognostic health monitoring system employs IIoT sensors to measure the current values of the operating parameters of a machine, using built-in intelligent decision support mechanism to compare with optimal ranges of values, and to message the appropriate alarming signals as per the severity of the deviation. The system developed was tested with a prototype model comprising the Internet of Things, internet communication technology, and a machine learning algorithm, MEMS with standard input, output, storage and display of components, which was developed in a laboratory but implemented in a case study in a real industrial production plant. After the successful implementation of the developed system, the performance of the critical machine was evaluated in terms of metrics such as the average number of failures, average downtime and average service time spent. It was found that after the implementation, the downtime has decreased by nearly 22% and for the performance in terms of its output, the flow rate exhibited a steady increase with the passage of time.

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Section: Introductionmentioning

confidence: 99%