Intelligent manufacturing execution systems: A systematic review

Shojaeinasab, Ardeshir; Charter, Todd; Jalayer, Masoud; Khadivi, Maziyar; Ogunfowora, Oluwaseyi; Raiyani, Nirav; Yaghoubi, Marjan; Najjaran, Homayoun

doi:10.1016/j.jmsy.2022.01.004

Cited by 62 publications

(28 citation statements)

References 109 publications

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“…MES [78][79][80][81] Realizes the interaction of data between devices, which is helpful for the scheduling and maintenance of production tasks.…”

Section: Technology Opportunities Challengesmentioning

confidence: 99%

“…With the development of artificial intelligence technology, researchers combine it with MES for productivity estimation, quality failure detection, job scheduling, manufacturing process control, etc. Some scholars have reviewed this aspect [78] and summarized the development situation and the technology trend of MES.…”

Section: Manufacturing Execution System (Mes)mentioning

confidence: 99%

“…In the research content, it was found that most MES worked together with ERP to realize intelligent manufacturing. Aiming at the limitation that some popular MES software is mainly designed for specific industries, Shojaeinasab et al [78] developed the IMES conceptual framework shown in Figure 12. It utilized a proven cloud architecture that supports edge devices [79] to upload critical information to the MES.…”

Section: Manufacturing Execution System (Mes)mentioning

confidence: 99%

See 2 more Smart Citations

Management Control and Integration Technology of Intelligent Production Line for Multi-Variety and Complex Aerospace Ring Forgings: A Review

Huang

Cui

et al. 2022

Metals

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Large and complex ring forgings are key structural parts of the aerospace field, and their quality is closely related to the reliability of aerospace vehicles. However, high-quality production of aerospace ring forgings faces many problems, such as the long process design cycle and impoverished consistency, the difficulties of real-time detection under the severe time-varying state of the deformation process, the complexity of high-quality non-destructive testing under multitudinous defects, and the cumbersome management control of the multi-source and multi-dimensional heterogeneous data. Considering the current situation of multi-variety and multi-batch production for aerospace ring forgings, establishing an intelligent production line is a crucial means to solving the above problems and realizing the standardization and premiumization of key aerospace components. Therefore, management control and integration technology of the intelligent production line play a crucial role. An analysis, including the research progress of the intelligent computer-aided process planning (CAPP) system, the real-time detection and control system, the product quality testing system, and the intelligent management control and integration system, is systematically reviewed in this work. Through intelligently managing and controlling the integrated systems of the production line, the production efficiency of ring forgings can be effectively improved, and the production energy consumption can be remarkably reduced, which is of great significance for enhancing the manufacturing technology level of aerospace products.

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“…MES [78][79][80][81] Realizes the interaction of data between devices, which is helpful for the scheduling and maintenance of production tasks.…”

Section: Technology Opportunities Challengesmentioning

confidence: 99%

Section: Manufacturing Execution System (Mes)mentioning

confidence: 99%

Section: Manufacturing Execution System (Mes)mentioning

confidence: 99%

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Management Control and Integration Technology of Intelligent Production Line for Multi-Variety and Complex Aerospace Ring Forgings: A Review

Huang

Cui

et al. 2022

Metals

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“…Machine breakdowns can present complex challenges during day-to-day operations and significantly impact business profitability and operations productivity. Monitoring machine health conditions can prevent machine breakdowns and reduce the maintenance costs of manufacturing systems [2]. It is, hence, crucial to develop efficient diagnosis systems to analyze different health conditions of the rotating components.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery

et al. 2022

Self Cite

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Fault diagnosis plays an essential role in reducing the maintenance costs of rotating machinery manufacturing systems. In many real applications of fault detection and diagnosis, data tend to be imbalanced, meaning that the number of samples for some fault classes is much less than the normal data samples. At the same time, in an industrial condition, accelerometers encounter high levels of disruptive signals and the collected samples turn out to be heavily noisy. As a consequence, many traditional Fault Detection and Diagnosis (FDD) frameworks get poor classification performances when dealing with real-world circumstances. Three main solutions have been proposed in the literature to cope with this problem: (1) the implementation of generative algorithms to increase the amount of under-represented input samples, (2) the employment of a classifier being powerful to learn from imbalanced and noisy data, (3) the development of an efficient data preprocessing including feature extraction and data augmentation. This paper proposes a hybrid framework which uses the three aforementioned components to achieve an effective signal based FDD system for imbalanced conditions. Specifically, it first extracts the fault features, using Fourier and wavelet transforms to make full use of the signals. Then, it employs Wasserstein Generative Adversarial with Gradient Penalty Networks (WGAN-GP) to generate synthetic samples to populate the rare fault class and enrich the training set. Moreover, to achieve a higher performance a novel combination of Convolutional Long Short-term Memory (CLSTM) and Weighted Extreme Learning Machine (WELM) is also proposed. To verify the effectiveness of the developed framework, different bearing datasets settings on different imbalance severities and noise degrees were used. The comparative results demonstrate that in different scenarios GAN-CLSTM-ELM significantly outperforms the other state-of-the-art FDD frameworks.

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“…Implementing these models enable us to monitor and control the parameters of machines from a remote distance and drive insights. That is the main reason for which data-driven fault detection and diagnosis models are used in smart manufacturing systems [2].…”

Section: Introductionmentioning

confidence: 99%

Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery

Jalayer,

Kaboli,

Orsenigo

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Fault diagnosis plays an essential role in reducing the maintenance costs of rotating machinery manufacturing systems. In many real applications of fault detection and diagnosis, data tend to be imbalanced, meaning that the number of samples for some fault classes is much less than the normal data samples. At the same time, in an industrial condition, accelerometers encounter high levels of disruptive signals and the collected samples turn out to be heavily noisy. As a consequence, many traditional Fault Detection and Diagnosis (FDD) frameworks get poor classification performances when dealing with real-world circumstances. Three main solutions have been proposed in the literature to cope with this problem:(1) the implementation of generative algorithms to increase the amount of under-represented input samples, (2) the employment of a classifier being powerful to learn from imbalanced and noisy data, (3) the development of an efficient data pre-processing including feature extraction and data augmentation. This paper proposes a hybrid framework which uses the three aforementioned components to achieve an effective signal based FDD system for imbalanced conditions. Specifically, it first extracts the fault features, using Fourier and wavelet transforms to make full use of the signals. Then, it employs Wasserstein Generative Adversarial Networks (WGAN) to generate synthetic samples to populate the rare fault class and enhance the training set. Moreover, to achieve a higher performance a novel combination of Convolutional Long Short-term Memory (CLSTM) and Weighted Extreme Learning Machine (WELM) is also proposed. To verify the effectiveness of the developed framework, different bearing datasets settings on different imbalance severities and noise degrees were used. The comparative results demonstrate that in different scenarios GAN-CLSTM-ELM significantly outperforms the other state-of-the-art FDD frameworks.

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Intelligent manufacturing execution systems: A systematic review

Cited by 62 publications

References 109 publications

Management Control and Integration Technology of Intelligent Production Line for Multi-Variety and Complex Aerospace Ring Forgings: A Review

Management Control and Integration Technology of Intelligent Production Line for Multi-Variety and Complex Aerospace Ring Forgings: A Review

Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery

Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery

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