Risk-based optimization of pipe inspections in large underground networks with imprecise information

Mancuso, Alessandro; Compare, Michele; Salo, Ahti; Zio, Enrico; Laakso, T.I.

doi:10.1016/j.ress.2016.03.011

Cited by 43 publications

(19 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to engineering practice, the second inspection time can be set as 02 02 tt  , so the first inspection time is 01 02 02 / 2 / 2 t t t , and then the product can be respectively inspected at 01 t and 02 t to obtain new degradation data 01 x and 02 x . ( 1) , ( 0, 0)…”

Section: Determination Of the Initial Inspection Time T 01 And T 02 Fmentioning

confidence: 99%

“…x exceeds the failure threshold w during the inspection, this component will be replaced. Then, all the degradation data ( 1)…”

Section: Determination Of the Inspection Timementioning

confidence: 99%

“…Although there are several definitions for risk, in maintenance engineering it has always been viewed as a combination of two attributes: likelihood (i.e., a description, even rough, of the uncertainty in the occurrence of the failure event) and severity (i.e., a quantification of the impact of the failure on properties, environment, safety, production, etc.) [1]. To quantify the severity of personnel injury and environmental pollution, a failure modes and effects analysis method is developed using subjective information derived from domain experts by Wang [2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Sequential Inspection Model based on Risk Quantitative Constraint and Component Importance

Bai¹,

Zeng²,

Zhao³

et al. 2018

IJPE

View full text Add to dashboard Cite

Due to aerospace equipment's need to maintain a certain degree of safety and reduce the system risk of operation, relevant maintenance and inspection strategies should be developed to meet the requirements of risk quantitative indicators. The inertial navigation system commonly used in aerospace products is taken as an example, and a sequential inspection and maintenance model based on quantitative risk constraints and component importance is proposed in this paper. Firstly, based on the quantitative constraints of the system risk and the importance of the components, the reliability constraints of the components in the inertial navigation system are determined by the fault tree analysis method. Secondly, the Wiener process is used to establish a performance degradation model for a key component of the inertial navigation system, and the expression of real-time reliability distribution is obtained with close form by use of the first-hitting time theory. The adaptive estimation method is used to estimate the unknown parameters of the model. Once the new degradation information is available, the parameters should be updated with a Bayesian equation. Thirdly, a sequential inspection model is discussed to determine the optimal intervals to satisfy the requirements for the real-time reliability at a certain time. Finally, an example of the drift data of the gyroscopes in the inertial navigation system is given to illustrate the validity of the proposed method.

show abstract

Section: Determination Of the Initial Inspection Time T 01 And T 02 Fmentioning

confidence: 99%

“…x exceeds the failure threshold w during the inspection, this component will be replaced. Then, all the degradation data ( 1)…”

Section: Determination Of the Inspection Timementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Sequential Inspection Model based on Risk Quantitative Constraint and Component Importance

Bai¹,

Zeng²,

Zhao³

et al. 2018

IJPE

View full text Add to dashboard Cite

show abstract

“…Risk is often defined as a combination of the Likelihood of Failure (LoF) with the Consequence of Failure (CoF) (see e.g. Anbari, Tabesh, & Roozbahani, 2017;Arthur, Crow, Pedezert, & Karikas, 2008;Scott Arthur & Crow, 2007;Baah, Dubey, Harvey, & McBean, 2015;Laakso, Ahopelto, Lampola, Kokkonen, & Vahala, 2018;Lukas & Merrill, 2006;Mancuso, Compare, Salo, Zio, & Laakso, 2016;McDonald & Zhao, 2001;Pienaar, 2013;Ward & Savic, 2012). Approaches used to express the consequences of failure can be divided into three categories:…”

Section: Introductionmentioning

confidence: 99%

Identifying critical elements in drinking water distribution networks using graph theory

Meijer

Post²,

Hoek

et al. 2020

Structure and Infrastructure Engineering

View full text Add to dashboard Cite

Drinking water distribution networks (WDNs) are a crucial infrastructure for life in cities. Deterioration of this ageing, and partly hidden from view, infrastructure can result in losses due to leakage and an increased contamination risk. To counteract this, maintenance strategies are required to maintain the service level. Information on the most critical elements of a WDN, with respect to the functioning of the system as a whole, is essential for prioritising maintenance or rehabilitation activities. In this study a Graph theory based method is developed and applied for efficiently identifying the most critical elements. The main advantage of this method is that it avoids the need to perform elaborate hydrodynamic model calculations. Instead, the structure of the network is the main starting point. The results show that the structure of the network is more decisive than the hydraulics with respect to the criticality of the system's performance as a whole. Results depict that the suggested approach is applicable not only to the main (primary) network, but also to the capillaries which are normally beyond the scope of the traditional methods applied so-far because of the complexity of the networks and the required calculation time.

show abstract

“…Studies have been carried out to develop methods for optimizing the locations and frequencies of visual inspections. A generally used optimization concept is the combination of the likelihood of failure (LoF) with the consequence of failure (CoF) (see e.g., [4][5][6][7][8][9][10][11]). The likelihood of failure is often related to the soil type, the load on the system and the material type where the consequence of failure is often related to the conduit characteristics (e.g., conduit size, conduit depth) and the location of the conduit in the urban area.…”

Section: Introductionmentioning

confidence: 99%

Identifying Critical Elements in Sewer Networks Using Graph-Theory

Meijer

Bijnen

Langeveld

et al. 2018

Water

View full text Add to dashboard Cite

Underground water infrastructure is essential for life in cities. The aging of these infrastructures requires maintenance strategies to maintain a minimum service level. Not all elements are equally important for the functioning of the infrastructure as a whole. Identifying the most critical elements in a network is crucial for formulating asset management strategies. The graph theory is presented as a means to identify the most critical elements in a network with respect to malfunctioning of the system as a whole. As opposed to conventional methods, the proposed method does not rely on iterative hydraulic calculations; instead, the structure of the network is taken as a starting point. In contrast to methods applied in practise, the results are independent on the chosen test-load. Because of the limited calculation effort, the method allows the analysis of large networks that are now, for practical reasons, beyond the scope of methods applied so-far.

show abstract

Risk-based optimization of pipe inspections in large underground networks with imprecise information

Cited by 43 publications

References 34 publications

A Sequential Inspection Model based on Risk Quantitative Constraint and Component Importance

A Sequential Inspection Model based on Risk Quantitative Constraint and Component Importance

Identifying critical elements in drinking water distribution networks using graph theory

Identifying Critical Elements in Sewer Networks Using Graph-Theory

Contact Info

Product

Resources

About