Using machine learning and deep learning algorithms for downtime minimization in manufacturing systems: an early failure detection diagnostic service

Abstract

Accurate detection of possible machine failure allows manufacturers to identify potential fault situations in processes to avoid downtimes caused by unexpected tool wear or unacceptable workpiece quality. This paper aims to report the study of more than 20 fault detection models using Machine Learning (ML), Deep Learning (DL), and Deep Hybrid Learning (DHL). Predicting how the system could fail based on certain features or system settings (input variables) can help avoid future breakdowns and minimize downtime. The effectiveness of the proposed algorithms was experimented with a synthetic predictive maintenance dataset published by the School of Engineering of the University of Applied Sciences in Berlin, Germany. The delity of these algorithms was evaluated using performance measurement values such as accuracy, precision, recall, and the F-Score. Final results demonstrated that Deep Forest and Gradient Boosting algorithms had shown very high levels of average accuracy (exceeded 90%).Additionally, the Multinominal Logistic Regression and Long Short Term Memory based algorithms have shown satisfactory average accuracy (above 80%). Further analysis of models suggests that some models outperformed others. The research concluded that, through various ML, DL, and DHL algorithms, operational data analytics, and health monitoring system, engineers could optimize maintenance and reduce reliability risks.

Artificial intelligence in lean manufacturing: digitalization with a human touch?

“…This includes decision support for production scheduling (Bouzekri et al, 2022), optimization of resource utilization (Tripathi et al, 2022b) and multi-skilled worker assignment (Xin et al, 2015). (Abusaq et al, 2023;Arana-Landín et al, 2023;Marinelli, 2022;Shahin et al, 2023aShahin et al, , 2023bShahin et al, , 2023cShahin et al, , 2023dShahin et al, 2023a;Thiede et al, 2017;Trabucco and De Giovanni, 2021;Tripathi et al, 2022a;Tseng et al, 2021) Source: Author's own work adapt to changing environments and respond quickly to market changes. Importantly, and in line with lean thinking and practice, most of the research here points towards the decentralization of production planning and control systems, increasing flexibility and resilience of operations (Küfner et al, 2021;Rossit et al, 2019;Ulhe et al, 2023).…”

“…Results Ahmed et al, 2023;Antosz et al, 2020;Herwan et al, 2023;Hosseinzadeh et al, 2023;Küfner et al, 2021a;Mjimer et al, 2023;Shahin et al, 2023c;Shakir and Iqbal, 2018) Smart production planning and control(Bouzekri et al, 2022;Castej on-Limas et al, 2022;Duhem et al, 2023;Fanti et al, 2022;Herwan et al, 2023;ITO et al, 2020; Jan et al, 2023; Javaid et al, 2022; Khadiri et al, 2022; Küfner et al, 2021b; Kutschenreiter-Praszkiewicz, 2018; Paraschos et al, 2023; Puche et al, 2019; Rossit et al, 2019; Sordan et al, 2022; Tripathi et al, 2022b, 2022c; Ulhe et al, 2023; Vickranth et al, 2019; Villalba-Díez et al, 2020; Xia et al, 2022; Xin et al, 2015) Quality control (Bhatia et al, 2023; Duc and Bilik, 2022; Kumar et al, 2021; Park et al, 2020; Perera et al, 2021; Pongboonchai-Empl et al, 2023; Shahin et al, 2023b; Yadav et al, 2020) Towards Industry 5.0: Sustainability, Resilience, Human-centricity…”

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

“…This approach departs from the CNNs that have traditionally dominated this domain. ViT demonstrates that transformers can achieve remarkable performance on image recognition tasks, challenging the supremacy of CNNs in computer vision and thus representing a novel approach to image classification [103,104]. The core idea behind ViT is to treat an image as a sequence of patches, akin to how a sentence is viewed as a sequence of words in NLP [105].…”