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。

In this brief video, Antonio Pietri discusses the concept of VUCA and what that means for Industry 4.0 and digitalization in the smart enterprise.

Adoption of ACCE can easily follow a known process that leverages organizational alignment, implementation of the software and a change in workflow processes that maximizes benefits.

This compelling interview with Technip and two other customers, provides a strong testimonial about has Aspen Economics improves work processes and business results, from process engineering to conceptual estimating to detailed estimating.

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.

特殊化学品メーカーの主なバリュードライバーにしっかりと整合化された実証済みの業界ベストプラクティスを導入した、製造パフォーマンスを変革するためのロードマップを示しています。また、製品品質を改善し、顧客への対応力を強化し、コスト効率を高める革新的な製造プラクティスを実現するために、どのような技術が活用できるのかについて説明しています。

Learn about AspenTech's 3 keys to a successful customer relationship: trust, effective communication and customer focus. We're shaping the strategies for our solutions around the value we deliver and the problems that we help our customers solve.

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。

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:

Learn how a major Middle East Refinery used AspenTech solutions to address operational issues with their crude distillation unit (CDU). This video highlights how the detailed hydraulic analysis capability in Aspen HYSYS, along with its seamless integration with the heat exchanger design & rating software Aspen EDR, helped the refinery troubleshoot a challenging operational issue with their CDU.