On the use of machine learning methods to predict component reliability from data-driven industrial case studies

Alsina, Emanuel Federico; Chica, Manuel; Trawiński, Krzysztof; Regattieri, Alberto

doi:10.1007/s00170-017-1039-x

Cited by 59 publications

(27 citation statements)

References 46 publications

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“…A poor quality production process can degrade reliability built in during the design phase. In (Alsina et al, 2018), it is studied how machine learning models can fit the reliability estimation function in comparison with traditional approaches (e.g., Weibull distribution), having in mind that the reliability estimation of engineered components is fundamental for many optimization policies in a production process. Four diverse machine learning approaches are implemented: artificial neural networks, support vector machines, random forest, and soft computing methods.…”

Section: Ai In Reliability and Maintenancementioning

confidence: 99%

Current state of the application of artificial intelligence in reliability and maintenance

Pokorni¹

2021

Vojnotehnički glasnik

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Introduction/purpose: At the end of 2019, the Government of the Republic of Serbia adopted the Strategy for the Development of Artificial Intelligence in the Republic of Serbia for the 2020-2025 period. This was a motivation for the author of this paper to try to give an overview of the current artificial intelligence (AI) applications in the field of reliability and maintenance, as well as its future applications. Methods: The overview is done mainly using available literature, mostly from the Science Direct database, using abstracts generally, and in some cases whole papers. Results: The result of this research is an overview of the artificial intelligence applications in the field of reliability and maintenance in the past thirty years. It also showed that AI systems can also be unreliable and need maintenance. Conclusion: Artificial intelligence is and can be applied in reliability and maintenance. The research of available literature showed that AI is more applied in maintenance than in reliability. The progress in AI is inevitable, so it is important to understand its potentials for application in reliability and maintenance as well as its possible drawbacks.

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Section: Ai In Reliability and Maintenancementioning

confidence: 99%

Current state of the application of artificial intelligence in reliability and maintenance

Pokorni¹

2021

Vojnotehnički glasnik

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“…Automation and application of artificial intelligence (AI) are a rife topic in production, transportation, and logistics-from autonomous cars and trucks to ergonomic enhancements of workers in production process handling and picking for example [16][17][18][19][20]. Manufacturing and production management changed over the last decades, e.g., with the advent of cheap sensors and actuators communicating within the Internet, enabling the real-time connection between systems, materials, machines, tools, workers, customers, and products as the IoT [3,21,22].…”

Section: State-of-the-art and Current Developmentsmentioning

confidence: 99%

Production logistics and human-computer interaction—state-of-the-art, challenges and requirements for the future

Klumpp¹,

Hesenius

Meyer

et al. 2019

Int J Adv Manuf Technol

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Current concept, development, and testing applications in production concerning Cyber-Physical Systems (CPS), Industry 4.0 (I40), and Internet of Things (IoT) are mainly addressing fully autonomous systems, fostered by an increase in available technologies regarding distributed decision-making, sensors, and actuators for robotics systems. This is applied also to production logistics settings with a multitude of transport tasks, e.g., between warehousing or material supply stations and production locations within larger production sites as for example in the automotive industry. In most cases, mixed environments where automated systems and humans collaborate (e.g., cobots) are not in the center of analysis and development endeavors although the worker's adoption and acceptance of new technologies are of crucial relevance. From an interdisciplinary research perspective, this constitutes an important research gap, as the future challenges for successful automated systems will rely mainly on human-computer interaction (HCI) in connection with an efficient collaboration between motivated workers, automated robotics, and transportation systems. We develop a HCI efficiency description in production logistics based on an interdisciplinary analysis consisting of three interdependent parts: (i) a production logistics literature review and process study, (ii) a computer science literature review and simulation study for an existing autonomous traffic control algorithm applicable to production logistics Purpose This paper addresses three approaches determined to analyze the crucial role of human interaction in automated environments in production logistics and Industry 4.0 settings. The methods stem from different disciplines as successful automation concepts also have to consider computer science, economics and work science perspectives. Contribution We answer the question of human intuition and its development within a digitalized production logistics setting as well as automated algorithm reaction to human actions from an interdisciplinary perspective. So far, existing research contributions are mainly focusing on technical aspects and automation concepts as solely computer science optimization aspects. However, feasible and sustainable concepts for automated production, e.g., within production transport will only work out if the human factor is included as for a long time to come production environments will be mixed settings of robotics and human workers. Thus, we develop a HCI efficiency description in production logistics for future research and business applications.

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“…3) Predictive Maintenance: The survey shows that the predictive maintenance process has the goal of providing an accurate estimate of the RUL, but also it should asses the provided estimate, as argued in [31], [32], and [33]. Time-series analysis is used to anticipate anomalies and malfunctions in equipment and processes maintenance procedures.…”

Section: A Analysis Of Maintenance Strategies 1)mentioning

confidence: 99%

On the Use of Predictive Models for Improving the Quality of Industrial Maintenance: An Analytical Literature Review of Maintenance Strategies

Merkt

2019

Annals of Computer Science and Information Systems

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Due to advances in machine learning techniques and sensor technology, the data driven perspective is nowadays the preferred approach for improving the quality of maintenance for machines and processes in industrial environments. Our study reviews existing maintenance works by highlighting the main challenges and benefits and consequently, it shares recommendations and good practices for the appropriate usage of data analysis tools and techniques. Moreover, we argue that in any industrial setup, the quality of maintenance improves when the applied data driven techniques and technologies: (i) have economical justifications; and (ii) take into consideration the conformity with the industry standards. In order to classify the existing maintenance strategies, we explore the entire data driven model development life cycle: data acquisition and analysis, model development and model evaluation. Based on the surveyed literature we introduce taxonomies that cover relevant predictive models and their corresponding data driven maintenance techniques.

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On the use of machine learning methods to predict component reliability from data-driven industrial case studies

Cited by 59 publications

References 46 publications

Current state of the application of artificial intelligence in reliability and maintenance

Current state of the application of artificial intelligence in reliability and maintenance

Production logistics and human-computer interaction—state-of-the-art, challenges and requirements for the future

On the Use of Predictive Models for Improving the Quality of Industrial Maintenance: An Analytical Literature Review of Maintenance Strategies

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