Asset maintenance is the act of maximizing the life of an asset through the application of various software applications that are capable of monitoring an asset’s performance and determining when and what action should be taken to keep the asset at its optimum operating condition and minimize the probability of an unplanned failure and lost productivity. This involves such things as preventive and predictive maintenance. This approach is in contrast to reactive maintenance that relies on repairing an asset only when it fails.
asset performance management (APM) involves using such things as condition monitoring, predictive maintenance, and reliability-centered maintenance to improve the availability and reliability of physical assets, such as equipment, plants, and infrastructure. A robust APM strategy is designed to minimize business risk and maintenance costs by eliminating unplanned asset downtime. APM involves a connected and integrated enterprise-wide solution that includes tools and applications to help asset-intensive businesses achieve optimal performance at a sustainable cost.
Cloud computing is a method of delivering IT services where resources and data are retrieved from the Internet using web-based applications and tools, and not through a direct connection to a server. Cloud computing makes it possible to store files and data remotely from the workplace and reduces the need for a local storage device or computer hard drive. However, to access data in the cloud, a user must have access to the web. Cloud-computing technology enables employees in the oil and gas sector to work remotely and to analyze extensive amounts of data at a lower cost. Delfi is an example of a cutting-edge software system that utilizes cloud computing to coordinate oil well data. By analyzing how wells are designed, drilled, and configured for production, the software program can maximize output for an oilfield and dramatically cut down costs.
A data-centric outlook is a core concept in digital project execution architecture where data is viewed as the most important and perpetual asset used in support of applications to produce deliverables. Within a data-centric architecture, the data model precedes implementation of a given application and remains valid long after the application is gone. In a data-centric approach, data must drive the development of projects, designs, business decisions, and culture. The emergence of cloud computing and storage enables organizations to remotely access and analyze large databases in order to make more objective, risk-mitigating, and profitable decisions.
A digital engineering environment is the part of a digital project hub that encompasses the various software applications required for engineering tasks. Where under a traditional execution model, the work of engineering disciplines would be segregated and linear, a data-centric execution model requires near-live, cross-discipline collaboration to take place in a digital engineering environment. The environment also hosts any digital representations of the real-world assets recreated from data captured in the field.