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.

Prescriptive maintenance technology is transforming how companies manage asset downtime. 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.

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.

When hyper compressors fail, the cost of production losses can range from tens of thousands to millions of dollars per occurrence. In this white paper, learn how companies are using Aspen Mtell to recognize the early indications of hyper compressor failure, reducing unplanned downtime and catching problems earlier—allowing more lead time to take appropriate action.

Understand the full capabilities of ACCE. Calibration will improve bid accuracy by adjusting costs to those mimicing the users. For those that have just started using ACCE or those that want to do Class 3 estimates for both Owner Operator and E&C.

世界中で、主要な組織が高度なデジタル技術を採用および実装しています。デジタルトランスフォーメーションの旅は、資産集約型産業、特にエネルギーおよび化学薬品ビジネスの性質を変えるでしょう。こうした状況下では、デジタルツイン（物理的な資産の仮想化されたコピーとその運用上の動作）が重要な役割を果たします。今日アスペンテックが描くデジタルツインの重要なコンセプトは、仮想データに対して洞察とアドバイスを提供するAIの力です。本ホワイトペーパーでは、これからのデジタルツイン戦略で重要になる鍵をご覧いただけます。

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.

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.

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。