Health index assessment of aged oil-filled ring main units

Amir, Mohd Dzaki Mohd; Muttalib, Esmet Sidqie A.

doi:10.1109/peoco.2014.6814452

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…A paper published by Amir et al has researched a condition-based health index concept where overall health index was calculated based on the individual indicators [1] and used a 10-grade scale differentiating a system condition from good to bad and enabling to categorize the particular system units. In power transformer application a health index ranging from 0 to 1 have been presented by Lata et al [2] and incorporated a various input relevant to that particular system to establish the resulting index value.…”

Section: Target Variable In Researchesmentioning

confidence: 99%

Predictive modelling of turbofan engine components condition using machine and deep learning methods

Matuszczak¹,

Żbikowski²,

Teodorczyk³

2021

Eksploatacja i Niezawodność – Maintenance and Reliability

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The article proposes an approach based on deep and machine learning models to predict a component failure as an enhancement of condition based maintenance scheme of a turbofan engine and reviews currently used prognostics approaches in the aviation industry. Component degradation scale representing its life consumption is proposed and such collected condition data are combined with engines sensors and environmental data. With use of data manipulation techniques, a framework for models training is created and models' hyperparameters obtained through Bayesian optimization. Models predict the continuous variable representing condition based on the input. Best performed model is identified by detemining its score on the holdout set. Deep learning models achieved 0.71 MSE score (ensemble meta-model of neural networks) and outperformed significantly machine learning models with their best score at 1.75. The deep learning models shown their feasibility to predict the component condition within less than 1 unit of the error in the rank scale.

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Section: Target Variable In Researchesmentioning

confidence: 99%

Predictive modelling of turbofan engine components condition using machine and deep learning methods

Matuszczak¹,

Żbikowski²,

Teodorczyk³

2021

Eksploatacja i Niezawodność – Maintenance and Reliability

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“…The descriptions of these three conditions are provided in Table 2. Additionally, as depicted in this table, three colour codes [2,7] are utilized to visually indicate the corresponding potential danger level of the three conditions.…”

Section: Problem Formulationmentioning

confidence: 99%

Real-time equipment condition assessment for a class-imbalanced dataset based on heterogeneous ensemble learning

Chen¹,

Zhang²,

Zhang³

2019

Eksploatacja i Niezawodność – Maintenance and Reliability

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Article citation info: Real-time equipment condition assessment foR a class-imbalanced dataset based on heteRogeneous ensemble leaRning ocena stanu spRzętu w czasie Rzeczywistym dla zbioRów danych o niezRównoważonym Rozkładzie w klasach. metoda opaRta na uczeniu zespołowym This study proposes an ensemble learning model for the purpose of performing a real-time equipment condition assessment. This model makes it possible to plan desired preventive maintenance activities before an unexpected failure takes place. This study focuses on the class-imbalanced problem in equipment condition assessment research. In reality, equipment will experience multiple conditions(states), most of the time remaining in the normal condition and relatively rarely being in the critical condition, which means that, from the perspective of data modelling, the distribution of samples is highly imbalanced among different classes(conditions). The majority of samples belong to the normal condition, while the minority belong to the critical condition, which poses a great challenge to the classification performance. To address this problem, a genetic algorithm-based ensemble learning model is presented. Furthermore, a self-updating learning strategy is presented for online monitoring, contributing to adaptability and reliability enhancement along with time. Many previous studies have attempted feature extraction and to set thresholds for equipment health indicators. This study has an advantage of omitting these steps, as it can directly assess the equipment condition through the proposed ensemble learning model. Numerical experiments, including two types of comparison studies, have been conducted. The results show the greater effectiveness of our proposed model over that of previous research in terms of the stability and accuracy of its classification performance.

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“…" As depicted in Table 3, three colour codes are used to indicate potential courses of action. This colour coding methodology has so far used as an equipment condition acceptance criteria for some industries such as electric power, offshore oil and gas and petrochemical industries (Amir and Muttalib, 2014;Hameed and Khan, 2014;Carvalho et al, 2015).…”

Section: Condition Assessmentmentioning

confidence: 99%

Condition assessment, remaining useful life prediction and life extension decision making for offshore oil and gas assets

Animah

Shafiee

2018

Journal of Loss Prevention in the Process Industries

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Offshore oil and gas assets are highly complex structures comprising of several components, designed to have a lifecycle of 20-25 years of working under harsh operational and environmental conditions. These assets, during their operational lifetime, are subjected to various degradation mechanisms such as corrosion, erosion, wear, creep and fatigue cracks. In order to improve economic viability and increase profitability, many operators are looking at extending the lifespan of their assets beyond the original design life, thereby making life extension (LE) an increasingly critical and highly-discussed topic in the offshore oil and gas industry. In order to manage asset aging and meet the LE requirements, offshore oil and gas operators have adopted various approaches such as following maintenance procedures as advised by the original equipment manufacturer (OEM), or using the experience and expertise of engineers and inspectors. However, performing these activities often provides very limited value addition to operators during the LE period of operation. This paper aims to propose a systematic framework to help operators meet LE requirements while optimizing their cost structure. This framework establishes an integration between three individual life assessment modules, namely: condition assessment, remaining useful life (RUL) prediction and LE decision-making. The benefits of the proposed framework are illustrated through a case study involving a three-phase separator system on a platform which was constructed in the mid-1970s in West Africa. The results of this study affirm the effectiveness of this framework in minimizing catastrophic failures during the LE phase of operations, whilst ensuring compliance to regulatory requirements.

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Health index assessment of aged oil-filled ring main units

Cited by 4 publications

References 4 publications

Predictive modelling of turbofan engine components condition using machine and deep learning methods

Predictive modelling of turbofan engine components condition using machine and deep learning methods

Real-time equipment condition assessment for a class-imbalanced dataset based on heterogeneous ensemble learning

Condition assessment, remaining useful life prediction and life extension decision making for offshore oil and gas assets

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