This white paper considers the challenges power generation operators face today and the forces that are driving demand for more proactive maintenance strategies. Gain insight into predictive maintenance that is being used to create a more comprehensive, correlated, efficient and effective environment for the collection, management,analysis and presentation of data. Discover the critical differences that make predictive maintenance a much more efficient and effective alternative to planned and condition based maintenance methodologies.

All chemical facilities have opportunities to reduce manufacturing costs, including newer, state-of-the art plants, older plants and those in between. Leading global producers have demonstrated reduced energy consumption, increased yield and higher reliability by simply improving operational practices through the application of advanced technology.

Mining companies invest heavily in equipment for all stages of mining, mineral processing, refining and distribution. By monitoring asset condition and behavior and developing profiles of normal operations, anomalies and failures, predictive maintenance tools can notify staff of equipment problems prior to failure. This paper outlines how predictive maintenance provides mining organizations the intelligence needed to:

Las compañías mineras invierten fuertemente en activos para todas las etapas del proceso minero: extracción, procesamiento, refinería y distribución del mineral. Al monitorear la condición y el comportamiento de los activos y desarrollar perfiles de operaciones normales, anomalías y fallas, las herramientas de mantenimiento predictivo pueden notificar al personal sobre los problemas del equipo antes de que ocurra falla. Este documento describe cómo el mantenimiento predictivo proporciona a las organizaciones mineras la inteligencia necesaria para:

この記事では AspenTech AIoT platform のIoTアナリティックスが、センサや稼働中デバイス の時系列データを使用し、設備資産のAsset Health スコアをどのように算出しているかを解説します。Asset Health スコアによって、サービスチームはリアルタイムで設備資産の健全性を把握で き、サービス業務やメンテナンスにかかる費用の削減につなげることができます。

Prescriptive maintenance technology is transforming how companies increase asset availability. With more advance warning of equipment failures comes more opportunity to mitigate the negative impact of those events.

With downstream margins globally close to a 10-year low, according to S&P Global Platts Analytics and ExxonMobil, companies need to find new areas for improvement. Current digital solutions offer ways plants can move beyond mechanical and equipment investments to drive greater profits. Download this white paper that outlines high impact operational improvements that deliver immediate results.

在全球范围内，各领先组织正在接纳并实施先进的数字化技术。数字化转型之旅将改变资产密集型行业（尤其是能源和化工企业）的性质。在这种情况下，数字孪生（实物资产及其操作行为的虚拟副本）将发挥关键作用。对于今天我们创建的数字孪生，一个关键概念是人工智能在提供虚拟数据相关见解和建议方面的作用。

Enhance mine-to-mill-to-metal operations with cutting-edge technologies that drive production increases, cost savings and increased safety for employees, the community and the environment. Learn how machine learning in AspenTech’s asset performance management solutions helps ensure reliable operations, enhance process efficiencies and increase mineral recovery.

Traditional preventive maintenance alone cannot solve the problems of unexpected breakdowns. With asset performance management powered by low-touch machine learning, it’s now possible to extract value from decades of process, asset and maintenance data to optimize asset performance. This white paper describes five best practices for driving state-of-the-art reliability management to predict breakdowns months in advance—increasing production and profitability.