Power Quality Considerations for Effective Predictive Maintenance (PdM) Integration

Power Quality Considerations for Effective Predictive Maintenance (PdM) Integration

Predictive maintenance is a powerful tool that can help reduce equipment-related downtime and increase asset life. It can also lead to improved asset-life expectancy, reduced capital outlay for asset replacement, and better control over production/operations planning.

When it comes to power quality, predictive maintenance can be applied by examining the condition of operational equipment and foreseeing its maintenance needs to attain optimum performance and avert any equipment failure. By checking the power quality at critical loads, you can see the effect of the electrical system up to the load.

There are several methods for predictive maintenance, including:

1. Infrared thermography: This technique is used to detect worn parts and components that emit more heat than normal.

2. Acoustic monitoring: Acoustic sensors are used to detect gas, liquid, or vacuum leaks in equipment.

3. Vibration analysis: Technicians analyze the vibrations of a machine by means of sensors integrated into the equipment.

These techniques are designed to help determine the condition of in-service equipment to estimate when maintenance should be performed. Predictive maintenance aims to minimize maintenance frequency, avoiding unplanned outages and unnecessary preventive maintenance costs.

Predictive maintenance can provide several benefits, including:

1. Reduced downtime: Predictive maintenance can help reduce equipment-related downtime by identifying potential issues before they become major problems.

2. Increased asset life: By identifying and addressing issues early, predictive maintenance can help extend the life of assets.

3. Improved asset-life expectancy: Predictive maintenance can help improve asset-life expectancy by ensuring that equipment is maintained and repaired as needed.

4. Reduced capital outlay for asset replacement: By extending the life of assets, predictive maintenance can help reduce the need for costly asset replacements.

5. Better control over production/operations planning: Predictive maintenance can help ensure that equipment is available when needed, which can help improve production and operations planning.

Implementing predictive maintenance in an organization requires a systematic and holistic approach to the design, development, and implementation of predictive maintenance.

Here are some steps that can help you implement predictive maintenance in your organization:

1. Identify the assets to include: Be judicious about which assets to include in your predictive maintenance program.

2. Consider the right partners: Consider partnering with a company that has a proven track record in predictive maintenance.

3. Provide sufficient time to improve models: Predictive maintenance models require time to improve and become more accurate.

4. Put people first: Ensure that your team has the necessary skills and expertise to build and maintain predictive maintenance models.

There are several resources available online that can help you implement predictive maintenance in your organization. For example, McKinsey & Company has published an article on how industry can get more value out of maintenance. Additionally, Fiix Software has published an article on what predictive maintenance is and how it can be implemented.

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