Why APC should be implemented on secondary units. Includes sections on environmental emissions control, fuel gas networks control and optimization, sulphur complex control and steam networks control.

Ron Beck speaks to the power of a model based estimating system providing insight to its attributes like reusable templates and saleable features. How does ACCE differ from my proprietary Excel-based spreadsheets?

在本白皮书中，了解预测分析的九个早期使用者如何减少计划外停工时间，提高资产可靠性。借助全新的资产绩效管理方法，来自石油和天然气、化工、采矿、运输和其他资本密集型行业的组织即可识别出可准确预测设备故障的故障特征，并执行预防性维护。下载本白皮书，查看真实的成功案例，了解仅需两个半星期即可创造的底线效益。

Are you wondering how your site stacks up against those of your peers? Do you know what your Advanced Process Control (APC) Score is? Gain key insights into your APC program and find out how best to measure it against others by reading this white paper. Determining where you stand compared to your competitors is a key step toward closing your APC GAPs and improving your site’s profitability. Download this white paper and learn how you can also participate in a benchmarking survey and be a part of an exciting study!

It is critical for combined cycle power plants to control costs and increase efficiency. In order to accomplish these goals, these plants must make every effort to avoid costly shutdowns, including implementing the newest innovative technologies. This white paper details how combined cycle power plants can leverage the lessons learned from other industries to capture more value from capital equipment, thereby improving operations overall.

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.

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アセット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、アセット（ここで言うアセットとは主に装置などのハードウェアを指す）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。アセットパフォーマンスの最適化を行えるようになりました。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.

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。

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.