Data driven predictive maintenance applications for industrial systems with temporal convolutional networks

Sharma, Deepak Kumar; Brahmachari, Shikha; Singhal, Kartik; Gupta, Deepak

doi:10.1016/j.cie.2022.108213

Cited by 11 publications

(1 citation statement)

References 33 publications

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“…The advent of machine learning in cyber-physical systems enables reliable predictions, significantly reducing downtime and operational losses. This data-driven approach to maintenance is indispensable for enhancing the operational prowess of industrial equipment [4] [8].…”

Section: Machine Learning In Industrial Roboticsmentioning

confidence: 99%

The convergence of artificial intelligence and blockchain in industrial robotics

2024

ACE

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In an era marked by rapid technological advances, understanding the intersection of artificial intelligence (AI), Blockchain, and industrial robotics is both timely and imperative. The research is motivated by the pressing need for a unified framework that stakeholders can refer to as they navigate the complexities of integrating AI and Blockchain into their robotic systems. Through this paper, the aim is to bridge the existing knowledge gap, highlight the synergistic benefits of combining these technologies, and discuss the challenges and opportunities that lie ahead, thereby providing a well-rounded viewpoint crucial for future research and practical applications. This paper serves as a synthesis of existing literature. As AI and Blockchain individually revolutionise various facets of industrial operations, their combined capabilities could bring about unprecedented levels of efficiency, security, and autonomy. The prospect of integration is full of enormous potential, but it also faces some challenges that require sufficient attention. Technological progress, favorable regulatory frameworks, and firm industry adoption are crucial for achieving the expected benefits of such integration.

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Section: Machine Learning In Industrial Roboticsmentioning

confidence: 99%

The convergence of artificial intelligence and blockchain in industrial robotics

2024

ACE

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Deep learning-based cutting force prediction for machining process using monitoring data

Lee

Kim

et al. 2023

Pattern Anal Applic

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Machine learning tool for the prediction of electrode wear effect on the quality of resistance spot welds

Panza,

Bruno,

Antal

et al. 2024

Int J Interact Des Manuf

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The quality of resistance spot welding (RSW) joints is strongly affected by the condition of the electrodes. This work develops a machine learning-based tool to automatically assess the influence of electrode wear on the quality of RSW welds. Two different experimental campaigns were performed to evaluate the effect of electrode wear on the mechanical strength of spot welds. The resulting failure load of the joints has been used to define the weld quality classes of the machine learning tool, while data from electrode displacement and electrode force sensors, embedded in the welding machine, have been processed to identify the predictors of the tool. Some machine learning algorithms have been tested. The most performing algorithm, i.e., the neural network, achieved an accuracy of 90%. This work provides important theoretical and practical contributions. First, the decreasing thermal expansion of the weld nugget as the electrode degradation advances results in a strong correlation between the difference of the maximum displacement value and the last value recorded during the welding and the relative failure load. Then, this work offers a practical decision support tool for manufacturers. In fact, the automatic detection of low-quality welds allows to reduce or eliminate unnecessary redundant welds, which are performed to compensate for the uncertainty of electrode wear. This leads to savings in time, energy, and resources for manufacturers. Finally, general recommendations for the timing of redressing or replacing the electrode are provided in the manuscript based on the company willingness to accept some non-compliant welds or not.

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Data driven predictive maintenance applications for industrial systems with temporal convolutional networks

Cited by 11 publications

References 33 publications

The convergence of artificial intelligence and blockchain in industrial robotics

The convergence of artificial intelligence and blockchain in industrial robotics

Deep learning-based cutting force prediction for machining process using monitoring data

Machine learning tool for the prediction of electrode wear effect on the quality of resistance spot welds

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