What to Consider Before Implementing a Predictive Maintenance Program

Now more than ever, organizations are searching for ways to increase efficiency and decrease asset failures, while reducing costs. 

While preventative maintenance is surely better than reactive maintenance, asset management teams need to transition to a more predictive state with their maintenance to maintain a competitive edge. 

Through our experience helping countless asset-intensive organizations implement predictive maintenance programs, we have identified the best practices you need to follow to start transitioning to a predictive maintenance state. 

In this article, we will cover what you should know before implementing a predictive maintenance program at your site, like requirements for predictive maintenance, how to successfully implement a PdM program, and what to avoid. 

What is a Predictive Maintenance Program?

The goal of a predictive maintenance program is to predict potential equipment failures before they occur. A traditional predictive maintenance program involves four key components: sensors, a data historian, an asset performance management solution, and an ERP. 

Most APM solutions are built one asset or condition at a time, rather than providing a holistic view of processes. This prevents organizations from achieving true predictive maintenance. Additionally, some APM solutions are so manual they end up becoming more time-consuming than helpful. This means that organizations may feel like they are doing predictive maintenance work but not getting the results they expect – leading to distrust in predictive maintenance solutions. 

To truly achieve predictive maintenance, organizations need the ability to quickly analyze data from sensors to predict potential failures before they occur. 

There are two primary ways this is done concerning predictive maintenance programs. 

First, we need to address how condition monitoring and data analysis are fundamental to predictive maintenance. By consistently collecting data on asset conditions, organizations can build predictive models based on historical and real-time sensor data, which can identify patterns and predict when maintenance is required. 

When speaking about data-driven decisions, predictive maintenance leverages sophisticated algorithms and AI to determine the right time to service assets before an issue arises. This approach not only reduces maintenance costs but prevents downtime by addressing problems proactively. 

Predictive maintenance leverages data and analytics to monitor the condition of assets and make predictions. Organizations can address issues proactively by analyzing trends and anomalies in real-time data, versus reacting to unexpected breakdowns as they occur. 

Predictive maintenance can require monitoring asset conditions like temperature, vibration, pressure, speed, torque, voltage, current, power consumption, lubricant condition, oil levels, flow rate, load, etc. The conditions you monitor will differ depending on the types of assets you are monitoring. Some examples of condition monitoring include: 

• Vibration Analysis: Identifies issues such as imbalance, misalignment, or bearing failures.
• Thermography: Detects overheating electrical components or other assets at risk of failure.
• Oil Analysis: Helps detect contamination or degradation in lubrication systems.
• Ultrasound: Used to detect leaks, electrical issues, or machinery faults. 

How Does PdM Work?

A predictive maintenance program typically follows a structured workflow to ensure assets are continuously monitored and maintained at the proper time. The process usually begins with installing sensors on critical assets to measure various performance indicators. These sensors send data to a central repository, where it’s analyzed using advanced algorithms. 

Once the system identifies a potential failure or anomaly, maintenance teams are alerted with sufficient lead time to intervene before the failure happens. For example, if a vibration sensor detects an abnormality in a motor, the system can predict when the motor will likely fail, giving technicians time to replace the motor before a complete breakdown occurs. 

Example Predictive Maintenance Workflow: 

1. Sensor data collection: Sensors collect real-time data on temperature, vibration, and other asset health indicators.
2. Data analysis: The data is sent to a central system, where AI algorithms analyze trends and patterns.
3. Predictive model generation: Based on historical data, the system predicts potential failures.
4. Alert generation: Maintenance teams are alerted to upcoming failures with enough lead time for action.
5. Preventive maintenance action: Teams carry out maintenance activities, such as replacing parts or adjusting settings, before the issue leads to a failure. 

Preventative Maintenance vs. Predictive Maintenance

While both preventative maintenance (PdM) and predictive maintenance aim to prevent downtime and reduce maintenance costs, they do so in fundamentally different ways. 

• Preventative Maintenance: Involves performing routine maintenance at fixed intervals, regardless of whether an issue is present. For example, replacing parts after a certain number of hours of operation.
• Predictive Maintenance: Uses real-time data and predictive analytics to determine the optimal time for maintenance based on the actual condition of the asset. This means you only perform maintenance when it's necessary, extending asset life and reducing unnecessary work. 

What are the Benefits of a Predictive Maintenance Program?

A well-executed predictive maintenance program offers numerous benefits for organizations, such as: 

• Reduced Downtime: By predicting failures in advance, you can schedule repairs during planned downtimes, reducing the likelihood of unplanned disruptions.
• Cost Savings: Predictive maintenance reduces unnecessary part replacements and lowers maintenance costs by focusing only on assets that need attention.
• Increased Equipment Lifespan: By addressing issues before they escalate, you can extend the life of your assets and avoid costly replacements.
• Improved Safety: Proactively addressing equipment failures can help reduce accidents and ensure a safer working environment.
• Optimized Resource Allocation: Focus maintenance efforts on critical assets, allowing maintenance teams to be more productive and reducing overall resource waste. 

What are the Requirements for a Predictive Maintenance Program?

To successfully implement a predictive maintenance program, organizations must meet several key requirements: 

• Sensors: Necessary to collect real-time data from assets. This may include vibration sensors, temperature sensors, pressure transducers, and more.
• Data Historian: A central repository where all sensor data is stored and analyzed.
• Integrated Asset Performance Management (APM) Solution: A comprehensive solution that can process and analyze the data to provide actionable insights.
• ERP Integration: The predictive maintenance system should integrate with existing enterprise resource planning (ERP) systems to ensure seamless operation and coordination across teams.
• Skilled Personnel: A trained team is required to understand the system and interpret the data correctly.
• Continuous Monitoring and Analysis: Ongoing data collection and analysis are essential to maintain the effectiveness of the program. 

How to Implement a Predictive Maintenance Program

Implementing a predictive maintenance program involves several key steps: 

1. Assessment of Current Maintenance Practices: Begin by evaluating existing maintenance processes and identifying areas for improvement. This assessment provides a baseline for measuring the impact of the predictive maintenance program.
2. Goal Setting: Define clear objectives for the predictive maintenance program. Goals should be specific, measurable, achievable, relevant, and time-bound (SMART).
3. Pilot Program: Start with a pilot program to test the predictive maintenance approach on a small scale. This allows for adjustments and fine-tuning before full-scale implementation.
4. Technology Selection: Choose the appropriate tools and technologies for data collection and analysis.
5. Training and Development: Ensure that all relevant personnel are trained to use the predictive maintenance tools and interpret the data effectively.
6. Data Integration: Integrate the predictive maintenance tools with existing systems, such as ERP and other asset management solutions.
7. Continuous Improvement: Regularly review the program’s performance and make necessary adjustments based on feedback and data. Continuous improvement is key to the long-term success of a predictive maintenance program. 

What to Avoid When Setting Up Your Predictive Maintenance Program

• Isolated Asset Monitoring: Focusing only on individual assets instead of a holistic approach can result in missed opportunities for optimization.
• Manual Data Analysis: Relying on manual methods for data analysis can lead to delays and human error. It’s important to leverage automation.
• Ignoring System Integration: Without integration with existing systems like ERP or CMMS, your predictive maintenance solution can become fragmented and inefficient.
• Lack of Stakeholder Buy-In: Ensure all stakeholders are on board with the program. Resistance to change can hamper the success of the implementation. 

Takeaway for Businesses 

Predictive maintenance programs offer significant benefits, including increased equipment lifespan, reduced downtime, improved safety, and better resource allocation. Businesses should take proactive steps to implement these programs, leveraging integrated APM tools to facilitate the process. 

Implementing a predictive maintenance program is a strategic move that can yield substantial long-term benefits for businesses. By integrating real-time data analytics, fostering cross-functional collaboration, and continuously improving processes, companies can ensure the reliability and efficiency of their assets. 

Are you looking into implementing a predictive maintenance program? Prometheus APM is a comprehensive solution that integrates with your ERP, CMMS, or EAM, and analyzes large amounts of sensor data to provide you with high-level alerts on critical issues. Contact us today to learn more about how Prometheus APM can help you achieve predictive maintenance.