Risk-based and predictive maintenance planning of engineering infrastructure: Existing quantitative techniques and future directions

Arzaghi, Ehsan; Yazdi, Mohammad; Aryai, Vahid; Garaniya, Vikram; Rahnamayiezekavat, Payam

doi:10.1016/j.psep.2022.07.046

Cited by 37 publications

(18 citation statements)

References 122 publications

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“…Planning for road maintenance is hampered by practical concerns, mostly. These limitations mostly relate to the maintenance budget, contract conditions, and actual maintenance arrangements in practice [14]. In addition, the worldwide optimal asset management of road may require to be foregone in both public and private contexts, and a local finest need to be established within the parameters of the yearly budget restrictions.…”

Section: Literature Reviewmentioning

confidence: 99%

Empirical Models Investigation of Pavement Management for Advancing the Road's Planning Using Predictive Maintenance

Aldabbas¹

2023

cea

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Section: Literature Reviewmentioning

confidence: 99%

Empirical Models Investigation of Pavement Management for Advancing the Road's Planning Using Predictive Maintenance

Aldabbas¹

2023

cea

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“…The modernization and automation of the manufacturing industry involves the use of increasingly complex, interdependent (often interconnected), precise and fast mechanical equipment. The onset of a defect [ 14 ], if not properly identified [ 15 ], can cause cascading component failures [ 16 ], causing economic damage [ 17 ], financial losses [ 18 ] and industry downtime [ 19 ]. However, for the evolved ML and DL algorithms to provide reliable and trustworthy information on the monitored process, data provided by interconnected sensors must be truly accurate and traceable.…”

Section: Introductionmentioning

confidence: 99%

Highly Reliable Multicomponent MEMS Sensor for Predictive Maintenance Management of Rolling Bearings

et al. 2023

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In the field of vibration monitoring and control, the use of low-cost multicomponent MEMS-based accelerometer sensors is nowadays increasingly widespread. Such sensors allow implementing lightweight monitoring systems with low management costs, low power consumption and a small size. However, for the monitoring systems to provide trustworthy and meaningful data, the high accuracy and reliability of sensors are essential requirements. Consequently, a metrological approach to the calibration of multi-component accelerometer sensors, including appropriate uncertainty evaluations, are necessary to guarantee traceability and reliability in the frequency domain of data provided, which nowadays is not fully available. In addition, recently developed metrological characterizations at the microscale level allow to provide detailed and accurate quantification of the enhanced technical performance and the responsiveness of these sensors. In this paper, a dynamic calibration procedure is applied to provide the sensitivity parameters of a low-cost, multicomponent MEMS sensor accelerometer prototype (MDUT), designed, developed and realized at the University of Siena, conceived for rolling bearings vibration monitoring in a broad frequency domain (from 10 Hz up to 25 kHz). The calibration and the metrological characterization of the MDUT are carried out by comparison to a reference standard transducer, at the Primary Vibration Laboratory of the National Institute of Metrological Research (INRiM).

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“…The maintenance of engineering facilities includes a wide range of repairs and services to enable the systems and equipment to perform their intended functions. This helps achieve the desired level of reliability and operational safety and improves the availability of the equipment, which will enhance the capability and productivity of the facilities [6]. The leakage of hazardous substances in process industries has long been a serious threat to workers and those who live near these industries; it has also caused substantial environmental damage [7].…”

Section: Introductionmentioning

confidence: 99%

Reliability assessment on natural gas pressure reduction stations using Monte Carlo simulation (MCS)

Karimi¹,

Zarei²,

Hokmabadi³

2022

IJRRS

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Gas pressure reduction stations play a key role in the timely and safe supply of natural gas (NG) to residential, commercial, and industrial customers. Accordingly, system reliability analysis should be performed to prevent potential failures and establish resilient operations. This research proposed a reliability assessment approach to natural gas pressure-reducing stations using historical data, statistical analysis, and Monte Carlo simulation (MCS). Historical data are employed to establish the probability distributions of the system and subsystems in gas stations. Then the Kolmogorov-Smirnov test is conducted to assess the goodness-of-fit for the developed distributions. Bayesian network (BN) is utilized to develop a system failure causality model. Finally, we performed MCS to precisely predict the failure rate and reliability of stations and all subsystems, such as the regulator, separator and dry gas filters, shut-off valves, and regulator. This research provided numerical findings on the reliability indicators of pressure reduction stations which can be used to improve system performance and, subsequently, the resilience of NG pipelines.

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Risk-based and predictive maintenance planning of engineering infrastructure: Existing quantitative techniques and future directions

Cited by 37 publications

References 122 publications

Empirical Models Investigation of Pavement Management for Advancing the Road's Planning Using Predictive Maintenance

Empirical Models Investigation of Pavement Management for Advancing the Road's Planning Using Predictive Maintenance

Highly Reliable Multicomponent MEMS Sensor for Predictive Maintenance Management of Rolling Bearings

Reliability assessment on natural gas pressure reduction stations using Monte Carlo simulation (MCS)

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