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:

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。

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.

Making big capital project management decisions shouldn’t be left to subjective perceptions or over-simplified analysis. Decision-makers need quantifiable, trustworthy answers to make the most profitable decisions possible. Aspen Fidelis Reliability is a robust RAM analysis tool that can handle the real-world challenges of today's process industries. In this paper, learn how Fidelis enables you to maximize the economics of business decisions and accurately predict future asset performance of the whole system.

单独的传统预防型维护无法解决非预期停机问题。凭借低接触式机器学习所驱动的资产绩效管理，现在可能会从数十种程序、资产和维护数据中抽取相关数值，从而优化资产绩效。在本白皮书中，将学习这种插断性技术如何部署精确性故障模式识别，其具有较高的准确性，可以提前预测设备停机月数，并就约定的维护提供相关建议。本白皮书亦列述了驱动先进可靠性管理的五个最佳实践，以期增产提盈。

AspenTech conducted a survey of 240 downstream customers to uncover thoughts and opinions on digital optimization and industry trends for 2018 and beyond. This white paper details the results of the survey and provides the reader with insights on the focus of increased reliability, a top priority for many refinery organizations.

従来の対処的メンテナンスだけでは、不測の事態に対応できません。手軽な機械学習による資産パフォーマンス管理(APM) により、今や製造工場のスタッフが、何十年にもわたって蓄積してきた設計や運用データから容易に価値を引き出し、資産（主に装置などのハードウェア）のパフォーマンスをより適切に管理して最適化することが可能になりました。本書では、常識を覆すような画期的なテクノロジーがどのように精密な故障パターン認識を使い、高精度に装置の故障を数ヶ月も前に予測し、処方的なメンテナンスをガイダンスするかを説明しています。また、５つのベストプラクティス（運用方法）をご紹介し、最先端の信頼性管理による、さらなる生産効率および利益率の改善手法について述べています。

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. In this white paper, learn how this disruptive technology deploys precise failure pattern recognition with very high accuracy to predict equipment breakdowns months in advance and advise on prescriptive maintenance. The paper also outlines five best practices for driving state-of-the-art reliability management to increase production and profitability.