Fuzzy early warning systems for condition based maintenance

Vafaei, Nazanin; Ribeiro, Rita A.; Camarinha-Matos, Luís M.

doi:10.1016/j.cie.2018.12.056

Cited by 35 publications

(12 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Xia et al (2018) provide a concise summary of predictive techniques in use for maintenance practice in a range of digital manufacturing activities; these authors note that innovative manufacturing techniques, such as 3D printing, bring new challenges to the way maintenance is performed, demanding new research into how supply chains support products manufactured in this way. Vafaei et al (2019) have investigated CBM from the perspective of providing an approach for an early warning system. In this work Vafaei et al (2019) utilise a fuzzy inferencing approach to enable a system capable of developing what-if scenarios based on generated rulesets regarding the potential for break downs in monitored production lines.…”

Section: Condition Based and Predictive Maintenancementioning

confidence: 99%

“…Vafaei et al (2019) have investigated CBM from the perspective of providing an approach for an early warning system. In this work Vafaei et al (2019) utilise a fuzzy inferencing approach to enable a system capable of developing what-if scenarios based on generated rulesets regarding the potential for break downs in monitored production lines.…”

Section: Condition Based and Predictive Maintenancementioning

confidence: 99%

See 1 more Smart Citation

Intelligent decision support for maintenance: an overview and future trends

Turner

Emmanouilidis

Tomiyama

et al. 2019

International Journal of Computer Integrated Manufacturing

View full text Add to dashboard Cite

The changing nature of manufacturing, in recent years, is evident in industry's willingness to adopt network connected intelligent machines in their factory development plans. A number of joint corporate/government initiatives also describe and encourage the adoption of Artificial Intelligence (AI) in the operation and management of production lines. Machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision making systems. While e-maintenance practice provides a framework for internet connected operation of maintenance practice the advent of IoT has changed the scale of internetworking and new architectures and tools are needed. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by IoT create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of a comprehensive framework for its processing, analysis and use should be a valuable contribution in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data and the provision of comprehensive framework for its processing analysis and use, allowing future systems to enable 'Human in the loop' interactions.

show abstract

Section: Condition Based and Predictive Maintenancementioning

confidence: 99%

Section: Condition Based and Predictive Maintenancementioning

confidence: 99%

Intelligent decision support for maintenance: an overview and future trends

Turner

Emmanouilidis

Tomiyama

et al. 2019

International Journal of Computer Integrated Manufacturing

View full text Add to dashboard Cite

show abstract

“…Fuzzy logic has been employed in fault diagnosis of different systems such as rotating machines [41]- [43], printing machines [44], railway wheels [45], pumps [46] or gearboxes [47]. It has also been employed to predict fault severity in helical gearboxes [48] or to develop an early warning system for improving decision-making in conditionbased maintenance [49].…”

Section: Maintenance Decision-making Support For Textile Machines: a Fuzzy Logic Approachmentioning

confidence: 99%

Maintenance Decision-Making Support for Textile Machines: A Knowledge-Based Approach Using Fuzzy Logic and Vibration Monitoring

2019

View full text Add to dashboard Cite

A condition-based maintenance approach may be used for planning the maintenance activities of textile machines with a satisfactory performance by developing maintenance decision-making support based on fuzzy logic and vibration monitoring. Since textile machines are systems with moving parts operating at relatively high-speed, vibration monitoring was used to indicate their failure development. At the same time, the characterization of the degradation phenomenon of textile machines involves some degree of uncertainty and vagueness. Within this context, a knowledge-based approach that employed fuzzy logic and vibration monitoring was developed. Deterioration symptoms do announce future failures of industrial machines, therefore building a maintenance decision-making support for scheduling maintenance actions of textile machines based on the estimation of their condition becomes a resourceful way to prevent their further deterioration.

show abstract

“…In reference [5], a proportional risk model is used to describe the combined effects of aging and cumulative damage, and a CBM strategy is developed for systems susceptible to aging and cumulative damage. Nazanin et al proposed a fuzzy early warning CBM strategy for automobile production line to provide early warning for potential production line failure or other dangerous situations, so as to make a more intelligent decision on maintenance strategy [6]. Phuc Do research group has established a binary system CBM model with stochastic and economic dependence.…”

Section: Introductionmentioning

confidence: 99%

Research on Fault Prediction for Marine Diesel Engines

Qi¹,

Qi²,

Hu³

2020

JCC

View full text Add to dashboard Cite

Condition-based maintenance based on fault prediction has been widely concerned by the industry. Most of the contributions on fault prediction are based on various sensor data and mathematical models of the equipment. The complexity of the model and data signal is the key factor affecting the practicability of the model. In addition, even for the same type and batch of equipment, the manufacturing process, operation environment and other factors also affect the model parameters. In this paper, a series event model is conducted to predict the fault of marine diesel engines. Numerical example illustrates that the proposed event model is feasible.

show abstract

Fuzzy early warning systems for condition based maintenance

Cited by 35 publications

References 27 publications

Intelligent decision support for maintenance: an overview and future trends

Intelligent decision support for maintenance: an overview and future trends

Maintenance Decision-Making Support for Textile Machines: A Knowledge-Based Approach Using Fuzzy Logic and Vibration Monitoring

Research on Fault Prediction for Marine Diesel Engines

Contact Info

Product

Resources

About