Failure intensity of offshore power plants under varying maintenance policies

This paper proposes a time‐varying failure mode and effect analysis (FMEA) method based on interval‐valued spherical fuzzy theory, which not only improves the limitations in evaluating, weighting, and ranking but also considers the effect of time change. The process of distinguishing time changes enables the FMEA to have dynamic recognition capability, enabling it to identify critical failure modes more accurately. The interval‐valued spherical fuzzy theory is used to deal with the uncertainty of intuitionistic linguistic evaluations. The advantages of two traditional approaches are combined to improve the weight determined method. Risk factors are divided into subjective and objective types. In the subjective risk factors, which are severity (S) and detection (D), the consistency of judgment is used as the acceptance standard. In the objective risk factors, which are occurrence (O), the time‐varying characteristics are considered. The occurrence in a certain period is expressed as the integral of failure intensity in the time period. Interval‐valued spherical fuzzy exponential risk priority number is proposed as the criterion for measuring the priority of failure modes. The effectiveness of the proposed method is verified using an example of spindle.

show abstract

“…If failure data obey the Weibull process, 32 then the failure intensity function can be expressed as follows:

h (t) = λ β t^{β - 1}, t \geq 0, λ > 0, β > 0,

where

λ

is the strength parameter and

β

is the shape parameter. The cumulative failure intensity in time period [ t 1 , t 2 ] can be expressed as follows:

H (t_{1}, t_{2}) = \int_{t_{1}}^{t_{2}} λ β t^{β - 1} d t = λ ({t_{2}}^{β} - {t_{1}}^{β}) .

…”

Section: Time‐varying Risk Factormentioning

confidence: 99%

“…If failure data obey the Weibull process, 32 then the failure intensity function can be expressed as follows:…”

Section: Time-varying Risk Factormentioning

confidence: 99%

Time‐varying FMEA method based on interval‐valued spherical fuzzy theory

Zhou

Chen

Tian

et al. 2021

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…The time-varying data structure can be used to quantify vessel operational and navigation conditions and ship system health conditions. The same information can be used for vessel energy efficiency [12] and system reliability applications [13,14], were the respective decisions supporting features should be developed (see Figure 1). That has been done by the decision supporting layer of the data handling framework supported by the same data driven models.…”

Section: Figure 1 Data Handling Frameworkmentioning

confidence: 99%

Ship performance and navigation information under high-dimensional digital models

Perera

2019

J Mar Sci Technol

Self Cite

View full text Add to dashboard Cite

Future vessels will be facilitated by modern internet of things (IoT) to collect various ship performance and navigation information. Such information is collected as large-scale data sets, so called Big Data and that should be utilized towards digitalization of the shipping industry. However, various data handling challenges are encountered by the shipping industry during the phase of digitalization, onboard as well as onshore. Data driven models, so called digital models, to support data handling frameworks of the shipping industry are proposed by this study. Such models can overcome the respective data handling challenges in shipping, where conventional mathematical models may fail to facilitate. These models can be derived from ship performance and navigation data sets by considering the high dimensional parameter space. Such high dimensional models consist of several data clusters and each data cluster may consist of a possible unique data structure. These data clusters often relate to sub-operational conditions of vessels and ship systems. The identification of the distribution of data clusters and the structure of each data cluster in relation to ship performance and navigation conditions have been done under this study for a selected vessel as the main contribution. Furthermore, the domain knowledge in shipping (i.e. vessel operational and navigation conditions) is also considered during this analysis to interpret the meaning in such digital models.Index Terms-Digitalization, Shipping industry, big data, digital models, data analytics, machine leaning and artificial intelligence.

show abstract

Tilting-Pad Journal Bearing with Active Pads: A Way of Attenuating Rotor Lateral Vibrations

Silva

Nicoletti

2023

Lecture Notes in Mechanical Engineering

View full text Add to dashboard Cite

Failure intensity of offshore power plants under varying maintenance policies

Cited by 4 publications

References 23 publications

Time‐varying FMEA method based on interval‐valued spherical fuzzy theory

Time‐varying FMEA method based on interval‐valued spherical fuzzy theory

Ship performance and navigation information under high-dimensional digital models

Tilting-Pad Journal Bearing with Active Pads: A Way of Attenuating Rotor Lateral Vibrations

Contact Info

Product

Resources

About