Leading asset-intensive companies are using prescriptive maintenance—powered by AI and machine learning—to unlock the value and productivity lying uncaptured in assets. Using a scalable, easy to implement prescriptive maintenance solution, companies can improve the accuracy of failure detection, increase the advance notification period of asset downtime events and reduce maintenance spend.

领先的资产密集型公司正在使用人工智能和机器学习支持的规范性维护，以释放资产中未被捕获的价值和生产力。使用可扩展、易于实施的规定性维护解决方案，公司可以提高故障检测的准确性，延长资产停机事件的提前通知期，并减少维护开支。

Today’s industries face mounting challenges in assuring the best operational performance of assets, both mechanical and process. Managing asset performance is the key element of controlling the operational risk that varying situations can present.

Regardless of what our plans were in January 2020, nothing is the same in 2021 or the future. A digital strategy is no longer limited to a subset of impacted functions, but touches everyone’s goals organization-wide in some way, shape or form.

Achieve savings in both engineering hours and CAPEX with modular design through the use of stanadardized designs in re-useable templates.

When delivering an estimate on time and on budget, learn why it is important to focus on the components that matter most.

ACCE is central to your strategy to lower CAPEX for your bids or estimates by being one common bidding and estimating platform across the bidding cycle.

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。

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.