Predictive maintenance entails using data from instrumented machinery to identify when servicing or overhaul is needed. In principle, this reduces maintenance work and expense while improving equipment availability.
In practice though, implementation often faces resistance. Running a predictive maintenance pilot program is a way of gathering data to win over naysayers and demonstrate a return on the effort and investment. It also helps fine-tune the roll-out to larger areas.
This article explains how to implement predictive maintenance, starting with a small-scale demonstration. Going through the steps involved and discussing the benefits to expect, will guide the development of a predictive maintenance strategy.
Steps to Implement a Predictive Maintenance Pilot
A pilot program has two primary goals: to gather the data needed to justify a broader implementation and to identify when changes in approach are needed to maximize benefits. Beyond this, it will also help win over those who might resist the idea of predictive maintenance, on the grounds of cost or risk, or because it’s seen as a threat.
The following 10 steps describe the key elements of a predictive maintenance pilot plan.
#1: Define Objectives and Criteria
Decide what you want to achieve and how to measure progress. Also, establish a budget as part of setting objectives, since this will determine the number and range of sensors to deploy.
Objectives are usually:
- Reduce downtime (or increase availability)
- Extend equipment life
- Reduce maintenance expenses
We usually measure in terms of mean time before failure (MTBF), availability (as part of an OEE calculation), or dollars, but we can agree on other measures as part of the pilot.
#2: Choose Assets to Test
It’s vital to select an area or group of machines that will show off the benefits of predictive maintenance. This means choosing assets of high importance to manufacturing and of a type for which sensor technology is available.
Rotating machinery like pumps, motors, and fans can be a good pilot application because several sensing and diagnostic technologies are available.
#3: Develop Data Collection and Analysis Plans
Establish baseline performance data, then capture and analyze data from sensors installed on the pilot equipment.
Will it rely on someone making periodic visits to the machinery and gathering sample data, or will Industry 4.0 technology be used to capture and analyze in real-time? Is there an opportunity to make this a predictive maintenance machine learning pilot project?
#4: Select Proper Condition Monitoring Equipment
Having identified the machines or area for the pilot, determine which parameters should be monitored. Where maintenance records exist, review these to determine the primary failure modes.
Running a root cause analysis can help ensure you select the correct characteristics for monitoring. Once you establish these characteristics, search for sensors with appropriate features, such as high sampling rates, communications interfaces, and possibly edge computing capabilities.
#5: Set Expectations
A pilot program is an opportunity to learn what works and what does not. Predictive maintenance will not eliminate all breakdowns, at least not initially. It takes time to build up the history that enables early detection of inconsistencies in operation.
When you observe inconsistencies, inspect how machine condition and likelihood of breakdown correlate with the data. This could result in more downtime than was the case before implementing the pilot. Stakeholders should be prepared for this before the program gets underway.
#6: Implement Pilot Program
Install sensors and begin collecting data. Implement and test data algorithmic models to detect anomalous conditions that may signal an approaching breakdown. Perform machinery inspections to ascertain the reasons for any abnormal signals detected. Keep stakeholders fully informed of progress.
#7: Pursue Continuous Feedback and Modeling Improvement
The pilot program should be updated as lessons are learned and new information becomes available. A core element is to use of predictive maintenance modeling to guide refinement of the algorithms used.
Improvement may include adding more sensors or sensors of a different type, changing sampling frequencies, and revising action thresholds.
#8: Analyze Sucesses and Failures
If machinery breaks down during the pilot study, understand why signs were not detected or acted on in a timely manner. Conversely, if you decide to perform maintenance based on the captured data, review the replaced parts and machine condition to estimate how close the machine was to failure.
Remember to ensure stakeholders understand that breakdowns are opportunities for improvement. Not all maintenance work is a success if it fails to prevent a breakdown.
#9: Consider Scalability
The ultimate goal of a predictive maintenance pilot study is to gather the data to both justify and guide the roll-out of a bigger program. This might be across a single line in a plant, throughout an enterprise, or somewhere in between.
#10: Wider Implementation
Considerations for a wider implementation include:
- Variety of sensor technologies and number of vendors: In general, fewer is better as standardization reduces learning curves and simplifies spare requirements, providing the right characteristics are being monitored.
- Communications technologies: Wired Ethernet may work in a small area close to the maintenance administration offices but on a plant spanning many acres, or when the goal is to implement predictive maintenance across multiple facilities, other technologies such as cellular may be more appropriate.
- Data analysis and interpretation responsibilities: Manual methods may be appropriate for the pilot study, but automation will become essential as the volume of data rises. Decide whether machine learning or other analysis tools will be helpful.
Benefits of Predictive Maintenance Programs
Industrial maintenance aims to ensure machinery is available to run when needed, at the required rate and quality. Many manufacturing plants use planned maintenance strategies to achieve this, but as resources are scarce and machines time limited, it’s important to focus on priority aspects.
Predictive maintenance uses machine monitoring and machine history data to determine when work is needed. The benefits of this are:
- Eliminates unnecessary maintenance work: This avoids taking machinery out of service and saves on both maintenance hours and materials.
- Better maintenance scheduling: By estimating the time remaining before failure from when an inconsistent or abnormal parameter is detected, (best done with the aid of a detailed machine history), it’s possible to schedule repair work for a time that won’t disrupt production.
- Reduced spare parts inventory: Rather than maintaining large stocks of parts such as couplings, belts, and filters in case of breakdown, inventories can be reduced because spares need only be purchased when a decision is taken to schedule repair or replacement work.
- Higher machine availability: Optimizing maintenance schedules and the type of work done will reduce breakdowns and the need to interrupt production for inspection and servicing. This supports higher OEE and output.
- Increased maintenance technician engagement: No one enjoys performing unnecessary work, which is sometimes the case when time-based preventive maintenance strategies are in place. With a predictive maintenance strategy, as technicians learn that the system is helping them prevent failures, they will develop more trust in what it tells them and will see the benefits in fewer breakdowns.
Get Support Deploying Predictive Maintenance
Predictive maintenance promises to reduce maintenance work, and so lower costs, while also improving machine availability. However, proposals to adopt this kind of maintenance strategy often face resistance, particularly regarding implementation costs and subsequent benefits.
In addition, it’s seldom realistic or practical to identify all the important characteristics to monitor before starting such a program. Instead, it’s important to keep revising the approach based on the lessons learned and data acquired.
A predictive maintenance pilot program is a low-risk way to address these concerns while gathering data, learning lessons, and getting stakeholders on board with the objectives. Nonetheless, such a program needs careful planning and implementation if it is not to undermine the broader goals.
This blog outlines ten steps for creating and implementing a predictive maintenance pilot, providing a foundation for those ready to take on the challenge independently. However, it’s important to recognize that partnering with an experienced predictive maintenance services provider can offer significant advantages. These experts bring valuable insights and proven strategies to the table, helping to navigate potential pitfalls and ensuring a smooth, effective implementation.
