Incorporating Sustainability and Maintenance for Performance Assessment of Offshore Oil and Gas Platforms: A Perspective

The existence of external two-fold pressure regarding competitiveness and sustainable development in a capital-intensive industry supports the need for sustainable performance. However, endeavors to create a sustainable framework to measure the performance of the oil and gas (O&G) industry are mostly devoted to the production and supply chain of petrochemical products and rarely focus on a maintenance perspective. Motivated by such scarcity, the goal of this research was to discuss and articulate the performance assessment framework by integrating concepts of maintenance and sustainability in the O&G industry. This study proposed the use of a range of performance measures for assessing sustainability on offshore production and drilling platforms. The conceptual framework consists of four aspects of sustainability categorized into technical, environmental, social, and economic dimensions. Each measure was assigned according to its relevance at the strategic, tactical, and functional levels of maintenance decision making. The conceptual framework resulted in hierarchical clusters of twelve strategic indicators. These indicators consist of conventional measures as well as new ones relating to the safety and reliability on offshore platforms. The potential contribution of the present study is found in its intention to empower a better understanding of sustainable maintenance and encourage those making decisions about practical implementation within the O&G industry. This paper culminates with directions for future studies.

Simulation Analysis of Arc Interruption Characteristics in Disconnector

Wind and solar energy are examples of clean energy that are widely developed and utilized in order to achieve the goal of carbon neutrality. Higher requirements for the safety and reliability of the power grid are put forward after they are connected to it. In the case of disconnectors, as the power system’s protection equipment, their arc interruption characteristics are closely tied to the safety and reliability of the power system. In addition, a disconnector is required to be able to break the DC arc in the photovoltaic power generation system. Therefore, this paper focuses on the arc evolution characteristics in disconnectors. A magnetohydrodynamics (MHD) model of disconnectors was built. In this model, not only are the coupling of the electromagnetic field and the airflow field considered, but also the characteristics of the external circuit. Therefore, not only can arc evolution characteristics be obtained through this simulation model, but the breaking performance will also be directly obtained. The temperature, pressure and velocity distribution are obtained to analyze the evolution process. The curve of current versus time is calculated to analyze the breaking performance. The evolution characteristics of AC and DC arcs in the disconnector are analyzed by calculation and comparison. This provides theoretical guidance for the optimal design of DC disconnectors through simulation analysis.

A Novel Approach of Label Construction for Predicting Remaining Useful Life of Machinery

Rolling bearings are key components of rotating machinery, and predicting the remaining useful life (RUL) is of great significance in practical industrial scenarios and is being increasingly studied. A precise and reliable remaining useful life prediction result provides valuable information for decision-makers, which is essential to ensure the safety and reliability of mechanical systems. Generally, the RUL label is considered to be an ideal life curve, which is the benchmark for RUL prediction. However, the existing label construction methods make more use of expert experience and seldom mine knowledge from data and combine experience to assist in constructing a health index (HI). In this paper, a novel and simple approach of label construction is proposed for predicting the RUL accurately. More specifically, the degradation index of the multiscale frequency domain is first extracted. Furthermore, the fuzzy C-means (FCM) algorithm is innovatively used to divide the degradation data into several stages to obtain the turning point of degradation. Then, a nonlinear degradation index, the RUL label with the turning point, was constructed based on principal component analysis (PCA). Finally, the recurrent neural network (RNN) is used for prediction and verification. In order to verify the effectiveness of the proposed approach, two different bearing lifecycle datasets are gathered and analyzed. The analysis result confirms that the proposed method is able to achieve a better performance, which outperforms some existing methods.