Assessing the opportunity to use the infrared thermography method for predictive maintenance of hydrostatic pumps

Marinescu, Alexandru-Daniel; Cristescu, Corneliu; Popescu, Teodor; Safta, Carmen-Anca

doi:10.1109/ciem.2017.8120790

Cited by 7 publications

(3 citation statements)

References 4 publications

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“…Thermal imaging is another widely known technique for PdM [100][101][102][103][104]. The technique uses infrared cameras to perceive potential problems, such as overheating, breakdown, friction, and energy inefficiencies, on a wide range of equipment, including electrical panels, transformers, and motors, by measuring the temperature of equipment.…”

Section: State-of-the-art Techniques For Predictive Maintenancementioning

confidence: 99%

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Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

Ucar,

Karakose,

Kırımça

2024

Applied Sciences

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Predictive maintenance (PdM) is a policy applying data and analytics to predict when one of the components in a real system has been destroyed, and some anomalies appear so that maintenance can be performed before a breakdown takes place. Using cutting-edge technologies like data analytics and artificial intelligence (AI) enhances the performance and accuracy of predictive maintenance systems and increases their autonomy and adaptability in complex and dynamic working environments. This paper reviews the recent developments in AI-based PdM, focusing on key components, trustworthiness, and future trends. The state-of-the-art (SOTA) techniques, challenges, and opportunities associated with AI-based PdM are first analyzed. The integration of AI technologies into PdM in real-world applications, the human–robot interaction, the ethical issues emerging from using AI, and the testing and validation abilities of the developed policies are later discussed. This study exhibits the potential working areas for future research, such as digital twin, metaverse, generative AI, collaborative robots (cobots), blockchain technology, trustworthy AI, and Industrial Internet of Things (IIoT), utilizing a comprehensive survey of the current SOTA techniques, opportunities, and challenges allied with AI-based PdM.

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Section: State-of-the-art Techniques For Predictive Maintenancementioning

confidence: 99%

“…Refs. [101][102][103] use thermal imaging technology in railways, medical, and hydraulics industries. Ref.…”

Section: State-of-the-art Techniques For Predictive Maintenancementioning

confidence: 99%

Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

Ucar,

Karakose,

Kırımça

2024

Applied Sciences

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“…The related literature is still scarce and also very diverse. For instance, Marinescu et al [5] introduce an experimental method regarding the opportunity to apply the infrared thermography approach in order to predict the behavior of hydrostatic pumping systems. Tse and Wang [6] evaluate the impeller's performance degradation, by extracting statistical features from vibration signals obtained from on-site operating slurry pumps and then estimate the remaining useful life (RUL) of the impeller.…”

Section: Introductionmentioning

confidence: 99%

Smart Predictive Maintenance Strategy Based on Cyber-Physical Systems for Centrifugal Pumps: A Bearing Vibration Analysis

Karami

Madlener

2019

SSRN Journal

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Early detection of faults in rotary machines, particularly in centrifugal pumps, has become essential in terms of avoiding unplanned or unnecessary maintenance and enhancing system reliability at minimized costs. This paper focuses on the predictive maintenance (PdM) for centrifugal pumps in Cyber-physical Systems (CPS) and proposes a concept to monitor the bearings in order to evaluate the pump's health condition. CPS have the potential to provide technical systems with self-awareness and self-maintenance capabilities. The implementation of predictive analytics as part of the CPS framework enables the machinery to continuously track its own performance and predict potential failures. Among the different methods for monitoring the pumps, vibration monitoring is one of the most important methods to collect real-time data. Using this technique for PdM would enable maintenance prior to early failure. In the case of the bearing's vibration monitoring the residual useful life can be predicted and even increased. Consequently, the probability that a breakdown happens is minimized and a smart PdM which guarantees an optimally safe system can be accomplished. Additionally, a conceptual economic analysis which compares two different maintenance strategies is presented in the last section.

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Predictive Maintenance in Correlation with Industry 4.0 and the Circular Economy

Drumea¹,

Marinescu²

2020

Proceedings of the International Conference of Mechatronics and Cyber- MixMechatronics - 2020

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Assessing the opportunity to use the infrared thermography method for predictive maintenance of hydrostatic pumps

Cited by 7 publications

References 4 publications

Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

Smart Predictive Maintenance Strategy Based on Cyber-Physical Systems for Centrifugal Pumps: A Bearing Vibration Analysis

Predictive Maintenance in Correlation with Industry 4.0 and the Circular Economy

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