Failure Monitoring and Predictive Maintenance of Hydraulic Cylinder - State-of-the-Art Review

2021 ◽  
pp. 1-1 ◽  
Author(s):  
Vignesh Vishnudas Shanbhag ◽  
Thomas J. J. Meyer ◽  
Leo W. Caspers ◽  
Rune Schlanbusch
Keyword(s):  
State Of The Art ◽  
Hydraulic Cylinder ◽  
Predictive Maintenance ◽  
Failure Monitoring

The State of the Art of Hidden Markov Models for Predictive Maintenance of Diesel Engines

2017 ◽  
Vol 33 (8) ◽  
pp. 2765-2779 ◽  
Author(s):  
António Simões ◽  
José Manuel Viegas ◽  
José Torres Farinha ◽  
Inácio Fonseca
Keyword(s):  
Hidden Markov Models ◽  
Diesel Engines ◽  
Markov Models ◽  
State Of The Art ◽  
Hidden Markov ◽  
The State ◽  
Predictive Maintenance

scholarly journals A Comparison Study of a Novel Self-Contained Electro-Hydraulic Cylinder versus a Conventional Valve-Controlled Actuator—Part 2: Energy Efficiency

Actuators ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 78 ◽  
Author(s):  
Daniel Hagen ◽  
Damiano Padovani ◽  
Martin Choux
Keyword(s):  
Energy Efficiency ◽  
Energy Savings ◽  
State Of The Art ◽  
Hydraulic Cylinder ◽  
The Self ◽  
The Novel ◽  
Working Cycle ◽  
Load Carrying ◽  
Motion Performance ◽  
Cleaner Technology

This research paper presents the second part of a comparative analysis of a novel self-contained electro-hydraulic cylinder with passive load-holding capability against a state of the art, valve-controlled hydraulic system that is typically used in load-carrying applications. After addressing the control design and motion performance in the first part of the study, the comparison is now focused on the systems’ energy efficiency. It is experimentally shown that the self-contained solution enables 62% energy savings in a representative working cycle due to its throttleless and power-on-demand nature. In the self-contained drive, up to 77% of the energy taken from the power supply can be used effectively if the recovered energy is reused, an option that is not possible in the state of the art hydraulic architecture. In fact, more than 20% of the consumed energy may be recovered in the self-contained system during the proposed working cycle. In summary, the novel self-contained option is experimentally proven to be a valid alternative to conventional hydraulics for applications where passive load-holding is required both in terms of dynamic response and energy consumption. Introducing such self-sufficient and completely sealed devices also reduces the risk of oil spill pollution, helping fluid power to become a cleaner technology.

scholarly journals Survey on Deep Learning applied to predictive maintenance

2020 ◽  
Vol 10 (6) ◽  
pp. 5592
Author(s):  
Youssef Maher ◽  
Boujemaa Danouj
Keyword(s):  
Deep Learning ◽  
Industrial Revolution ◽  
State Of The Art ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Traditional Learning ◽  
Maintenance Policy ◽  
Manufactured Products ◽  
Useful Life ◽  
Machine Health

Prognosis Health Monitoring (PHM) plays an increasingly important role in the management of machines and manufactured products in today’s industry, and deep learning plays an important part by establishing the optimal predictive maintenance policy. However, traditional learning methods such as unsupervised and supervised learning with standard architectures face numerous problems when exploiting existing data. Therefore, in this essay, we review the significant improvements in deep learning made by researchers over the last 3 years in solving these difficulties. We note that researchers are striving to achieve optimal performance in estimating the remaining useful life (RUL) of machine health by optimizing each step from data to predictive diagnostics. Specifically, we outline the challenges at each level with the type of improvement that has been made, and we feel that this is an opportunity to try to select a state-of-the-art architecture that incorporates these changes so each researcher can compare with his or her model. In addition, post-RUL reasoning and the use of distributed computing with cloud technology is presented, which will potentially improve the classification accuracy in maintenance activities. Deep learning will undoubtedly prove to have a major impact in upgrading companies at the lowest cost in the new industrial revolution, Industry 4.0.

scholarly journals Digital Twin in Electrical Machine Control and Predictive Maintenance: State-of-the-Art and Future Prospects

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5933
Author(s):  
Georgios Falekas ◽  
Athanasios Karlis
Keyword(s):  
State Of The Art ◽  
Predictive Maintenance ◽  
Electrical Machine ◽  
Digital Twin ◽  
Industrial Manufacturing ◽  
Core Concepts ◽  
Processing Techniques ◽  
And Control ◽  
Future Work ◽  
Relevant Work

State-of-the-art Predictive Maintenance (PM) of Electrical Machines (EMs) focuses on employing Artificial Intelligence (AI) methods with well-established measurement and processing techniques while exploring new combinations, to further establish itself a profitable venture in industry. The latest trend in industrial manufacturing and monitoring is the Digital Twin (DT) which is just now being defined and explored, showing promising results in facilitating the realization of the Industry 4.0 concept. While PM efforts closely resemble suggested DT methodologies and would greatly benefit from improved data handling and availability, a lack of combination regarding the two concepts is detected in literature. In addition, the next-generation-Digital-Twin (nexDT) definition is yet ambiguous. Existing DT reviews discuss broader definitions and include citations often irrelevant to PM. This work aims to redefine the nexDT concept by reviewing latest descriptions in broader literature while establishing a specialized denotation for EM manufacturing, PM, and control, encapsulating most of the relevant work in the process, and providing a new definition specifically catered to PM, serving as a foundation for future endeavors. A brief review of both DT research and PM state-of-the-art spanning the last five years is presented, followed by the conjunction of core concepts into a definitive description. Finally, surmised benefits and future work prospects are reported, especially focused on enabling PM state-of-the-art in AI techniques.

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