Engineering systems asset management

Souza, Gilberto Francisco Martha de; Netto, Adherbal Caminada; Melani, Arthur Henrique de Andrade; Michalski, Miguel Ângelo de Carvalho; Silva, Renan Favarão da

doi:10.1016/b978-0-12-823521-8.00008-6

Cited by 4 publications

(10 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The literature (e.g., FEMP, 2010 [ 13 ]; NASA, 2008 [ 14 ]; Smith, 2022 [ 15 ]; Souza, 2021 [ 16 ]) indicates that a graphical representation of the failure rate of a population of components versus time would probably be in the form of a “bathtub”, as shown in Figure 1 . In this representation, the Y-axis indicates the rate of failure, whereas the X-axis corresponds to time.…”

Section: Related Workmentioning

confidence: 99%

“…In this representation, the Y-axis indicates the rate of failure, whereas the X-axis corresponds to time. Based on its shape, the curve can be divided into three distinct zones: early failures, useful life, and wear-out failures (FEMP, 2010 [ 13 ]; NASA, 2008 [ 14 ]; Smith, 2022 [ 15 ]; Souza, 2021 [ 16 ]).…”

Section: Related Workmentioning

confidence: 99%

“…According to FEMP (2010) [ 13 ], NASA (2008) [ 14 ], Smith (2022) [ 15 ], and Souza (2021) [ 16 ], the early period, i.e., the infant mortality region of the bathtub curve, is characterized by a high failure rate followed by a period of decreasing failure. Many of the faults associated with this region are related to inappropriate projects and poor manufacturing and assembly, including human error during installation and operation (FEMP, 2010 [ 13 ]; Souza, 2021 [ 16 ]). The period of infant mortality is followed by a nearly constant period known as useful life (FEMP, 2010 [ 13 ]; NASA, 2008 [ 14 ]; Smith, 2022 [ 15 ]; Souza, 2021 [ 16 ]).…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning for the Detection and Diagnosis of Anomalies in Applications Driven by Electric Motors

Ferraz Júnior,

Romero,

Hsieh

2023

Sensors

View full text Add to dashboard Cite

Manufacturing systems are becoming increasingly flexible, necessitating the adoption of new technologies that allow adaptations to a turbulent and complex modern market. Consequently, modern concepts of production systems require horizontal and vertical integration, extending across value networks and within a factory or production shop. The integration of these environments enables the acquisition of a substantial amount of data containing information pertaining to production, processes, and equipment located on the shop floor. When these data and information are processed and analyzed, they have the potential to reveal valuable insights and knowledge about the manufacturing systems, offering interpretive outcomes for strategic decision making. One of the opportunities presented in this context includes the implementation of predictive maintenance (PdM). However, industrial adoption of PdM is still relatively low. In this paper, the aim is to propose a methodology for selecting the main attributes (variables) to be considered in the instrumentation setup of rotating machines driven by electric motors to decrease the associated costs and the time spent defining them. For this, the most well-known data science and machine learning algorithms are investigated to choose the one most adequate for this task. For the experiments, different testing scenarios were proposed to detect the different possible types of anomalies, such as uncoupled, overloaded, unbalanced, misaligned, and normal. The results obtained show how these algorithms can be effective in classifying the different types of anomalies and that the two models that presented the best accuracy values were k-nearest neighbor and multi-layer perceptron.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning for the Detection and Diagnosis of Anomalies in Applications Driven by Electric Motors

Ferraz Júnior,

Romero,

Hsieh

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…All organizations own and use physical assets such as equipment, vehicles, facilities and infrastructure to realize value from them through the production of products and provision of services (de Souza et al, 2021). Since these assets allow organizations to achieve their strategic objectives and meet the needs and expectations of stakeholders, such as owners, shareholders, employees, government organizations and customers, proper asset management has become crucial for organizations to remain competitive in the global market.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Discussing the applicability of the maintenance management framework for asset management (MMFAM): a hydroelectric power plant case study

da Silva,

de Souza

2023

JQME

View full text Add to dashboard Cite

PurposeThe Maintenance Management Framework for Asset Management (MMFAM) is a recently modeled framework to ensure the alignment of maintenance management with physical asset management based on the ISO 55000 series for asset management. In this context, the purpose of this paper is to discuss the applicability of the MMFAM considering the operational context of a hydroelectric power plant.Design/methodology/approachThe paper adopted the case study method for the discussion of the applicability of the MMFAM to a real operational context. A hydroelectric power plant was chosen as the scope of the case study due to its relevance since the electricity sector is an example of an asset-intensive industry in which asset management performance is fundamental. To gain a detailed understanding of the organization, data were collected through direct requests to the plant, informal meetings with technical collaborators, a technical visit to the hydroelectric plant and on-site data collection. Then, the MMFAM processes were demonstrated based on this information and the results supported the discussion of the MMFAM applicability.FindingsThe case study provided a deeper understanding of the processes included in the MMFAM. In addition, the results suggested the applicability of the framework to other organizations besides the hydroelectric sector due to its generic approach and the possibility of choosing appropriate tools to support and implement the MMFAM processes.Practical implicationsThe case study is expected to contribute to the practical understanding of the MMFAM processes within an operational context and assist maintenance professionals and researchers in their implementation in other organizations.Originality/valueAlthough the literature provides different maintenance management frameworks, their practical discussion based on a real operational context is still a gap. Accordingly, this paper discusses the MMFAM under a case study method to expand its understanding beyond theory and contribute to practical comprehension in depth.

show abstract

Reliability and Risk Centered Maintenance: A Novel Method for Supporting Maintenance Management

da Silva,

Melani,

Michalski

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Proper maintenance planning is critical for maintenance management to contribute to increasing availability, ensuring quality requirements, and controlling the safety and environmental risks associated with physical assets. As supporting tools for developing maintenance strategies, Reliability-Centered Maintenance (RCM) and Risk-Based Maintenance (RBM) methods are currently used in several organizations. Nevertheless, these strategies are often approached separately although they are complementary. In this context, this paper proposes a novel method that effectively integrates RCM and RBM by adapting the traditional RCM method to incorporate risk management into maintenance planning decision-making to support maintenance management. The proposed Reliability and Risk Centered Maintenance (RRCM) method allows organizations to determine maintenance plans that ensure the reliability of the physical assets while considering and prioritizing the risks associated with their potential functional failures. The proposed method was demonstrated through a case study considering the operational context of a hydroelectric power plant. The results show the ability of RRCM to assist in the development and implementation of maintenance plans oriented to reliability, risk, and cost.

show abstract

Engineering systems asset management

Cited by 4 publications

References 20 publications

Machine Learning for the Detection and Diagnosis of Anomalies in Applications Driven by Electric Motors

Machine Learning for the Detection and Diagnosis of Anomalies in Applications Driven by Electric Motors

Discussing the applicability of the maintenance management framework for asset management (MMFAM): a hydroelectric power plant case study

Reliability and Risk Centered Maintenance: A Novel Method for Supporting Maintenance Management

Contact Info

Product

Resources

About