At the AVEVA World Conference in Orlando this week, AVEVA has booths devoted to predictive and prescriptive maintenance capabilities, artificial intelligence (AI), and the application of asset performance management (APM). While much can be said about each of these technologies on their own, it’s important to understand that they’re also inextricably connected—as manufacturers increasingly use AI-driven predictive and prescriptive analytics to better understand and improve the performance of their assets.
AVEVA预见性维护是一个成熟的工艺ogy, having been in use now for about 14 years, says Jim Chappell, vice president of information solutions for AVEVA. The prescriptive analytics technologies AVEVA is showcasing at the event this year, however, are relatively new additions, coming largely from the company’s acquisition of MaxGrip last spring. Although AVEVA had some prescriptive-related offerings, MaxGrip “bulleted us forward,” Chappell says, with its 20 or so years of experience in fault diagnostics. “We’re merging all that together with our predictive analytics portfolio.”
The bulk of AVEVA’s work with predictive maintenance began with customers in the power industry, who were well positioned with their historian data to take advantage of analytics, Chappell says. This type of use has spread in the past five or six years to the oil and gas industry, and more recently to food and beverage, water/wastewater, mining, and others.
“We’re in all these industries with predictive analytics now,” Chappell points out. “We’re infusing AI across all of our business units, from both SCADA and MES perspectives.”
To highlight how these technologies are being applied, Chappell shares an example of a customer’s steam turbine that was showing unusual vibration on one of its blades. Using AVEVA analytics to drill down into the turbine’s data, the customer discovered that the major contributor to this anomaly was a hairline crack in a blade that was almost undetectable at first glance with the human eye. “It could’ve caused major damage,” Chappell says. “They calculated the avoidance of potential damage—had the cracked blade gone unnoticed—to more than $34 million.”
查佩尔说，对于使用分析的食品和饮料行业，制造商通常希望改善维护并对运营做出更好的反应，但他们不一定知道他们需要寻找哪些问题。
作为食品和饮料行业如何开始使用分析的一个例子，Chappell分享了AVEVA客户的故事，其烤箱氧化剂正经历大于预期的压力差异。利用AVEVA分析提供的见解，制造商发现，当外部温度降至零以下时，水分出血管冻结。这导致线备份，从而导致整个催化剂的压力增量。
查佩尔评论说：“这不是他们想要的东西。”但是，一旦他们找到了它，他们就可以回去解决其他植物的问题。
查佩尔（Chappell）在解释预测性维护和规范性维护之间的关键区别时说，预测性通过异常检测提供了预警，使用机器学习来创建资产或流程的数字双胞胎来寻找异常。它与绩效分析齐头并进，该绩效分析利用第一原则来模拟资产或过程的行为，首先是通过自动进行仿真，然后使用模拟来寻找与基线的偏差。
查佩尔说，规定性维护远远超出了异常检测，以查看哪些特定标签是对检测异常的贡献者。他说，使用规定，您不仅知道您有一个需要解决的问题，而且还可以更好地了解可能的根本原因以及如何修复它。
“预测性告诉您您有问题，情况越来越糟，现在该解决它了。您可以安排下一次计划中断的维护，或者至少采取受控措施。”查佩尔说。“有了规定，它告诉您您需要做些什么才能解决它。”
