Anyone who has been in manufacturing has dealt with the stresses and costs associated with unplanned downtime. Because of its negative effects, businesses are always looking for ways to reduce this downtime — and one answer is predictive maintenance.

Predictive maintenance (PdM) is a type of maintenance strategy that involves using data and analytics to predict when equipment is likely to fail. This allows businesses to take proactive measures to prevent equipment failures and extend the lifespan of their critical assets.

This method has proven extremely effective, and McKinsey found that predictive maintenance can reduce machine downtime by 30% to 50% while increasing machine life by 20% to 40%. These reductions can mean huge savings as downtime can cost $10,000 to $500,000 per hour, depending on the operation and equipment.

Because of these and other benefits, many businesses have successfully implemented a predictive maintenance strategy. But how can you establish your own PdM program and experience these same benefits?

Four Steps to Building a PdM System

There are four steps necessary for implementing a successful PdM program from pilot to completion.

Step 1: Develop an Effective Pilot Program

The first step in building your in-house predictive maintenance system is to develop an effective pilot program. This will help you test and refine your PdM strategy to ensure the system meets your company’s needs, as well as identify any potential challenges or obstacles that may arise during implementation.

When developing your pilot program, it is important to define your goals and objectives clearly. These will help you measure the overall success of your program and help guide you on any necessary adjustments that need to be made before implementation.

After your goals and objectives are defined, begin identifying which pieces of equipment should be included in your PdM program. Start by selecting a small number of key equipment that are crucial to your operations and likely to benefit the most from predictive maintenance.

How can you choose the right equipment? Some helpful criteria to consider include:

• Choosing an asset that is critical to operations.
• Selecting machines with high maintenance times and costs.
• Singling out assets that have a high replacement cost.
• Considering equipment in hazardous areas or remote locations.

Finally, it is essential to gather the necessary data for your predictive maintenance program. This includes the performance and condition history of the selected assets. To effectively collect and monitor this data, it is recommended to have condition monitoring strategies in place and devices on your pilot equipment, such as sensors. PdM programs cannot hope to be successful without the assistance of condition monitoring.

During this stage, you will gather a large amount of data, and to process it all, you will need a data analytics system in place. This system can analyze this data as well as develop algorithms and models to help you predict when equipment is likely to fail.

Don’t worry about having all the answers for the pilot — you will be able to learn and adjust throughout the experience.

Step 2: Evaluate and Refine

Next, evaluate and refine your pilot system. Analyze the data collected from your pilot program to identify any patterns or trends that can help you to improve your predictive maintenance strategy.

It is important to systematically and thoroughly evaluate the data that you have collected. This will typically involve using data analytics software to identify patterns and trends in the data, as well as to test different modeling approaches and algorithms.

Once you have evaluated the data, the next step is to refine the models and algorithms to help make more accurate predictions about potential equipment failures. To help train the algorithms on which pieces of information are most important, consider implementing machine learning techniques, which help improve accuracy and reliability.

During this stage, it will be important to assess the performance of your predictive maintenance system on an ongoing basis. Monitor the accuracy of your predictions and track key metrics, such as equipment downtime and maintenance costs.

Step 3: Replicate and Monitor

Once your PdM pilot program is performing at a satisfactory level on your test equipment, take what you have learned and begin applying it across your company. This involves not only applying the program to other critical assets but also getting buy-in from all necessary team members. This is how the PdM program becomes a pillar feature of the company culture and turbocharges the benefits for the entire company.

To begin, it is important to train personnel on how to use your predictive maintenance system. You should provide training on the equipment and software that you are using, as well as on the data analytics and modeling techniques that are at the core of your predictive maintenance program.

When your personnel is prepared, you can replicate the approach and expand your predictive maintenance program to other critical assets. Again, this will involve installing sensors and other condition-monitoring devices on your equipment.

Once up and running, it will be necessary to perform regular system audits. By regularly monitoring the performance of your system, you can identify underperforming areas and make changes to improve its effectiveness. Ask yourself, “Is the system improving key metrics?” This will help focus the attention of the operation on the right areas. If key metrics aren’t improving, now is a great time to revisit the data models and PdM system parameters to ensure you are focusing on the right aspects.

Step 4: Optimize

The final step is to optimize your system by identifying opportunities to improve its performance.

To identify improvement opportunities, analyze the data that you have collected and ask yourself:

• Are you able to accurately predict issues before they occur?
• Have key performance metrics, such as equipment downtime and maintenance costs, improved?

By carefully analyzing this information, you can identify areas where your predictive maintenance system is not performing as well as it could. This can help you find where changes are needed to correct any program issues.

Utilize advanced system features to maximize the effectiveness of your predictive maintenance system. This will typically involve using advanced data analytics and modeling techniques, as well as leveraging the latest technologies and innovations in the field of predictive maintenance.

Conclusion

Building an in-house predictive maintenance system can provide numerous benefits for businesses, including reduced downtime, improved safety, and extended equipment lifespan. To successfully implement a predictive maintenance program, it is important to follow a systematic process.

This process typically involves four steps: developing an effective pilot program, evaluating and refining, replicating and monitoring, and expanding and optimizing the system.

Following these steps, businesses can effectively build and implement a predictive maintenance system that meets their needs and provides maximum benefits. With a sound predictive maintenance program in place, you can achieve true reliability.