Asset Monitoring / Predictive Maintenance / Prescriptive Maintenance

[Case history] Overcoming your information management challenges

Predictive maintenance: Pedal to the metal

By Sheila Kennedy, CMRP, contributing editor

Run-to-failure is increasingly reserved for rare and unique circumstances. This trend started when increasingly capable condition inspection and monitoring tools shifted the asset management focus from “fix what’s broken” to “keep it from breaking down.” Today, unprecedented opportunities afforded by the industrial internet of things (IIoT) have further changed the playing field, and there are potential benefits yet to be realized.

For example, predictive maintenance (PdM), originally based on selected asset condition data, has grown to accommodate online, real-time streams of multiple types of condition data received via sensors and even drones. Some companies are applying machine learning (ML) to further refine their predictive analytics and prognostics.

The newest opportunity, prescriptive maintenance (RxM), is a multivariate approach that merges asset condition data with any combination of operating, environmental, process safety, engineering, supplier, or other related data to better diagnose conditions and prescribe specific options for corrective action. The advanced analytics, pattern recognition, modeling, ML, and artificial intelligence (AI) that empower RxM may help companies finally greatly curtail, if not eliminate, the need for reactive maintenance on critical equipment.

Acquisitions and renewable adoption by AGL Energy created a surge in generation capacity for the Australian energy company but also an information management challenge. “We had a group of separate companies that were under AGL but no capability to access real-time data across the entire group,” explains David Bartolo, head of operational systems and technology at AGL Energy. “We were completely data-blind.”

To remedy this, AGL launched an Operations Diagnostic Centre (ODC) in 2015 to integrate the data and enable centralized, real-time monitoring of assets and performance. Key components of ODC include an open data infrastructure solution, visualization tools, and PdM software. Together, they enable advanced analysis, predictive modeling, PdM, and optimization of critical assets.

Already, more than 2,700 employee-built digital models are monitoring about 45,000 critical data points every five minutes, and new applications and areas for optimization have been identified. Benefits include reduced downtime, increased productivity, energy savings, avoided costs, and regulatory compliance.

AGL estimates that A$21 million (US$15.2 million) was saved in the first three years of the ODC and that it realized a return on its initial investment in approximately nine months. “The ROI is so ridiculously high that we struggled with our finance people” who believed it was a calculation error, says Bartolo.

In addition, early detection of a serious thermal failure mode within a hydrogen-cooled generator stator allowed AGL to perform repairs rather than suffer catastrophic failure of the generator, which it estimates could have cost more than A$50 million (US$37 million).