Reliability modelling and assessment of electric motor driven systems in hydrocarbon industries

Badawi, Fahad S. Al; Al-Muhaini, Mohammad

doi:10.1049/iet-epa.2015.0089

Cited by 17 publications

(6 citation statements)

References 12 publications

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“…To do this, the damage behavior was evaluated first using the criteria from Table 4. It should be noted that the electric motor is composed of several entities with different damage behavior, including ball bearings, rotor and coils (Al Badawi and AlMuhaini, 2015). This leads to lifetime heterogeneity and makes an evaluation more difficult.…”

Section: Application Of the Procedures To An Electric Powertrainmentioning

confidence: 99%

Selection of Model Approaches and Modelling Methods for Lifetime Prognosis

Bauer

Inkermann

2023

Proc. Des. Soc.

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Lifetime prognoses are fundamentally important to improve products regarding safety, costs, availability and sustainability. To modelling the lifetime of a system or its components and subsystems different methods and model approaches are available, which are not compatible in any case. Depending on the system, use case and available data, the existing model approaches and modelling methods are differently suitable for a precise lifetime prediction. In this contribution a procedure was developed to help in the selection of suitable approach-method combinations. For this purpose, the compatibility of method types with the different model approaches was assessed and criteria for the pre-selection of suitable approaches and methods for lifetime modelling were defined. The selection procedure was applied to the example of entities for electric powertrains of aircraft in early design stages. Finally, the results were summarized and evaluated. The insights gained in this paper can help to enhance lifetime models of products in early design phases.

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Section: Application Of the Procedures To An Electric Powertrainmentioning

confidence: 99%

Selection of Model Approaches and Modelling Methods for Lifetime Prognosis

Bauer

Inkermann

2023

Proc. Des. Soc.

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“…However, over the past few years and recent year, there have been records of drives failure which have cause extended plant downtime and production loss. Understanding the root cause of drive failure and records of failure modes will assist the plant operators in establishing a better maintenance plan and conduct a complete diagnostic and preventive maintenance (Al Badawi et al 2015), (ABB 2011).…”

Section: Pain Pointsmentioning

confidence: 99%

Electric Drive Health Assessment

Lai Tiing,

Rosli,

Abdul Rahman

et al. 2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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Electric drives are widely used to regulate the flow of power supply between source to motor either due to Process or Operation requirement and as means to lower motor's inrush current. The use of AC electric drives is expected to increase due to pressing need to reduce operational cost, increase productivity and energy saving in line with company's efforts towards electrification. There have been records of failure of electric drives in the past which have caused major production loss within Petroleum Nasional Berhad (PETRONAS) operations. Common drives' failure mechanisms are converter malfunction or electronic component failures, voltage dip susceptibility, insulation degradation and so on which could be avoided through effective preventive maintenance and condition-based monitoring via data analytics. This paper describes a methodology on Electric Drive Health Assessment (EDHA) and as a web-based data analytical tool to assess and monitor electric drives' key components and parameters that contribute to a drive health index. Based on the drive health index and corresponding risk, operators are able to closely monitor the condition of electric drives in their facility which will prompt them to carry out necessary intervention. EDHA was developed based on PETRONAS installation, operation and maintenance practices, maintenance data and lessons learnt gathered from assets which have been operating for more than 30 years.

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“…e current failure rate prediction methods include traditional reliability predictions, statistical predictions, and data-driven predictions. Traditional reliability prediction mainly includes the prediction method based on fault tree analysis [4] and the prediction method based on life distribution [5]. Al Badawi et al designed a fault tree model to identify the failure mechanism in the EMD system.…”

Section: Introductionmentioning

confidence: 99%

Method for Predicting Failure Rate of Airborne Equipment Based on Optimal Combination Model

Hou

Sun

2021

Mathematical Problems in Engineering

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Accurate prediction of airborne equipment failure rate can provide correct repair and maintenance decisions and effectively establish a health management mechanism. This plays an important role in ensuring the safe use of the aircraft and flight safety. This paper proposes an optimal combination forecasting model, which mixes five single models (Multiple Linear Regression model (MLR), Gray model GM (1, N), Partial Least Squares model (PLS), Artificial Neural Network model (BP), and Support Vector Machine model (SVM)). The combined model and its single model are compared with the other three algorithms. Seven classic comparison functions are used for predictive performance evaluation indicators. The research results show that the combined model is superior to other models in terms of prediction accuracy. This paper provides a practical and effective method for predicting the airborne equipment failure rate.

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Reliability modelling and assessment of electric motor driven systems in hydrocarbon industries

Cited by 17 publications

References 12 publications

Selection of Model Approaches and Modelling Methods for Lifetime Prognosis

Selection of Model Approaches and Modelling Methods for Lifetime Prognosis

Electric Drive Health Assessment

Method for Predicting Failure Rate of Airborne Equipment Based on Optimal Combination Model

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