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How to Get Started

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STEP 1: Decide which equipment you want to monitor.

The first step in asset health management is determining which equipment you want to monitor. Start by separating your rotating equipment into two categories: critical assets and balance-of-plant equipment. The technology used and the type of data collected vary between these two categories, so this is a vital step.

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STEP 2: Define the data you want to see.

The actionable insights you’ll glean from equipment performance data depend on what software and analytics you’re using to analyze it. Algorithms are based on proprietary models, methodologies and industry experience, so the company providing the algorithms has a significant impact on what you can achieve by using them.

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STEP 3: Establish access to expertise.

The ability to repair equipment problems before they result in downtime, lost productivity and decreased revenue is asset health management’s greatest benefit. However, without access to repair and maintenance expertise, a failing asset is a ticking time bomb.

FAQs

What’s the difference between condition monitoring and predictive analytics?

Condition monitoring provides a snapshot of equipment performance and detects small changes in equipment operating conditions, which could be an early indicator of failure. Predictive analytics collects full-spectrum data to forecast when your equipment will fail, why it will fail, and what you can do to prevent it.

What specific data can I track with asset health management?

Plant operations will receive a near real-time system measurement and analysis. The analysis will include critical equipment monitoring and failure mode, remaining time to maintenance, a configurable trending report, efficiency and performance optimization curves, and customizable alerts and alarms.

What can I realistically do with the data?

Monitoring equipment performance leads to enhanced equipment efficiency, improved reliability and elevates profitability.

You can set alerts and learn immediately when an equipment’s performance deviates from a pre-defined threshold and generate trend reports to see how equipment performs over time.

With this information, you can forecast when equipment maintenance will be needed. With predictive analytics, you can go a step further to determine an asset’s remaining life, most likely failure modes, and recommended actions to mitigate failures.

On which assets should I use condition monitoring and/or predictive analytics?

Consider predictive analytics for critical operating equipment such as overhung, vertical, between bearings and high-energy pumps. Predictive analytics is best used when unplanned downtime significantly impacts productivity and creates revenue losses, equipment repairs are expensive, and replacement equipment is not available. You should consider condition monitoring for balance-of-plant equipment, and when unplanned downtime results in lower productivity, repairs are expensive but not as costly as critical equipment, and specific equipment experiences frequent failures.