For reliability engineers, Failure Modes and Effects Analysis (FMEA) is a step-by-step process for identifying and tracking the various ways an asset or equipment can fail. But despite its merits, FMEA is labor intensive, typically used only for critical equipment and does not sufficiently use data to improve its capability.

This on-demand webinar focuses on how the Aspen ProMV™ operational analytics suite provided a time-efficient and cost-effective solution for ADM in solving their most critical manufacturing process challenges by quickly learning from their process data.

请您观看资产绩效管理网络专题研讨会回放。了解AI，机器学习技术以及可靠性分析在流程工业中的应用，以及大数据技术可以为流程工业带来什么样的影响，从案例中获得了解如何利用最新的技术实现本企业的效益提升的机会。 最近数年中，新型的数字化技术和产品琳琅满目，对于不同技术之间的区别和其能够带来的效果，企业往往会陷入选择的难题。在本次研讨会中，您将更多的了解不同技术的应用范围，以为您的决策做好基础。

A recent study by industry analyst firm Verdantix found that 56% of respondents believe that asset performance management (APM) is essential to business success, but few have executed on APM strategies and documented the business value. While APM has traditionally been the domain of maintenance and reliability engineers, its place within process-intensive industries is evolving.

As it relates to asset reliability, Failure Modes and Effect Analysis (FMEA) is a process to identify potential failures in a system as well as their causes and effects. By combining predictive maintenance technology with FMEA tools, companies can associate causation with known failures and their suggested FMEA remedies – leading to earlier intervention, minimized equipment damage, fewer process interruptions and improved production yields.

Volatility in today’s markets is driving innovation across the Asset Performance Management (APM) space, demanding new levels of operational excellence and more dynamic approaches to achieve company goals. A holistic solution looks beyond just optimizing maintenance processes to deliver Financially Optimized Asset Performance. AspenTech refers to this as Autonomous Reliability.

Warnings and alerts are not new in the industrial world as some predictive alerts can provide months of warning of catastrophic events. The challenge is how to prioritize and respond to these predictive alerts in a more systematic manner that benefits all areas of an organization.

In this webinar, the second in our series of Asset Performance Management (APM) webinars for data scientists, learn how APM applications help data science teams rapidly design and scale new solutions. See how complex and time-consuming tasks such as feature engineering, state classification and best model selection are greatly streamlined—without building customized tools. AspenTech expert Ashok Rao focuses on solving complex reactor problems in the chemicals industry. Learn how Aspen Maestro™ for Mtell and ProMV® enables you to:

La ingeniería de datos (ingeniería de características), clasificación de estados y selección de modelos pueden ser tareas complejas y consumen gran parte del tiempo de los equipos de ciencia de datos. Si bien la creación de herramientas particulares suele ser el primer instinto, hay que tomar en cuenta que las aplicaciones actuales de Gestión del Desempeño de Activos (APM) que cuentan con experiencia industrial, pueden ayudar a los científicos de datos a acelerar estos procesos para diseñar y escalar nuevas soluciones rápidamente.

Engenharia de recursos, classificação de estados e seleção do melhor modelo podem ser tarefas complexas e demoradas para as equipes de ciência de dados. Embora a construção de ferramentas personalizadas seja muitas vezes o primeiro instinto, as aplicações de hoje de Asset Performance Management (APM) embutidas com expertise industrial podem ajudar os cientistas de dados a acelerar o processo de projetar e dimensionar novas soluções mais rapidamente.