Data-Based Estimation of the Dynamic Reliability and Performance Indicator of an Industrial Manufacturing System
Ondo Boniface1, Nasso Toumba Richard2, Ombété Tsimi Giscard3, Kombé Timothée4, Elé Pierre5
1Ondo Boniface, Laboratory of Technology and Applied Sciences, University of Douala, Cameroon.
2Nasso Toumba Richard, Laboratory of Technology and Applied Sciences, University of Douala, Cameroon.
3Ombété Tsimi Giscard, Laboratory of Technology and Applied Sciences, University of Douala, Cameroon.
4Kombé Timothée, Laboratory of Technology and Applied Sciences, University of Douala, Cameroon.
5Elé Pierre, Laboratory of Technology and Applied Sciences, University of Douala, Cameroon.
Manuscript received on 26 February 2023 | Revised Manuscript received on 07 March 2023 | Manuscript Accepted on 15 April 2023 | Manuscript published on 30 April 2023 | PP: 31-38 | Volume-12 Issue-4, April 2023 | Retrieval Number: 100.1/ijeat.D40530412423 | DOI: 10.35940/ijeat.D4053.0412423
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The aim is to develop a more straightforward and more effective method for assessing the performance and dynamic reliability of complex industrial systems. By utilising the operating data of the industrial system, characterised by strong desynchronization, and applying artificial intelligence prediction algorithms to the time series, the model will have learned from the behaviour of the complex manufacturing system, allowing the operator or decision-maker better to orient maintenance, production, and quality policies. Furthermore, we propose this approach to avoid tedious mathematical methods related to dynamic reliability calculations and performance evaluation, thereby enabling forecasts of the company’s operation over a long period by identifying future bottlenecks in the system’s behaviour. The low-performance indicators and unreliable reliability presented by many third-generation industries are due to the lack of efficient and simple tools for reliability assessment that take into account the dynamic aspects of the different elements of the production chain, including the maintenance department, production department, and quality department. We propose developing a model that abstracts from conventional, complex, and inefficient mathematical methods for systems subject to combinatorial explosion problems in the manufacturing industry.
Keywords: Dynamic Reliability, Performance Indicators, Complex Industrial System, Long Short-Term Memory (LSTM) Architecture.
Scope of the Article: Manufacturing Processes