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What Is Predictive Maintenance?
Predictive maintenance (PdM) is a maintenance strategy that uses technology such as sensors to monitor equipment performance and condition during normal operation. This information gives maintenance teams early indications when the asset experiences a problem, before failure occurs. When a maintenance team knows the condition of every asset in real time, they can take proactive maintenance measures to reduce the chances of unexpected failures and unplanned downtime.
Traditionally, most maintenance teams have used reactive or preventive maintenance (PM) strategies, where repairs happen after machines fail or teams perform maintenance regularly based on the manufacturer’s guidelines. Today, many organizations use new predictive maintenance technologies — like IoT predictive maintenance sensors — to move beyond these methods and adopt a predictive maintenance approach.
Organizations that use predictive maintenance software and tools will monitor and test specific characteristics to identify conditional changes as they happen. There are numerous testing methods that fall under the umbrella of predictive maintenance, including infrared testing, vibration analysis, oil analysis, and more.
There is not one singular best maintenance solution, and assets within the same facility may benefit from different maintenance strategies. But for assets that are critical to the organization, predictive maintenance, also known as PdM maintenance, is often the best approach.
6 Steps for Establishing a Predictive Maintenance Program
Your ideal predictive maintenance solution depends on your organization’s size and budget, as well as your equipment and crew. However, there are a few key steps that every organization needs to follow to reap the full benefits of predictive maintenance, as illustrated in the graphic above.
- Identify which assets to target for your predictive maintenance analytics program
Not every asset is a great choice for a predictive maintenance approach. Inexpensive, easy-to-replace equipment probably doesn’t require a predictive maintenance solution, for example. Instead, identify your most critical assets — the ones you rely on to keep your production or service delivery running smoothly.This shouldn’t be guesswork; your CMMS holds the data you need to conduct a detailed criticality analysis. Work order histories, maintenance records, and KPIs like Mean Time Before Failure (MTBF) can give you insights into which assets need frequent repairs, as well as the associated costs. This data will paint a picture of which assets would benefit from a structured predictive maintenance solution.
- Choose Your Predictive Maintenance Tools and Methods
Condition monitoring is a key step on the path to predictive maintenance. Many organizations are already using IoT predictive maintenance tools to monitor vibration data, temperature, oil quality, and more.If you’re not already doing so, build a network of IoT predictive maintenance tools, like wireless sensors, to collect condition monitoring data in real time. The sensors will stream data to the cloud for predictive maintenance analytics.Decide which tools and methods match your organization’s needs. Depending on your assets and infrastructure, you may get the most relevant data from vibration monitoring, infrared thermography, or a different approach altogether.
- Select and Train a Team
An effective PdM maintenance team should include data scientists capable of constructing predictive models and managing your data infrastructure — that’s in addition to maintenance and operations experts and technicians who can use IoT predictive maintenance tools.Many organizations choose to outsource some or all PdM maintenance tasks by partnering with trusted experts in the field. It’s also a good idea to include AI tools to streamline the analytic process.
- Perform System Integrations
At this stage, integrate your IIoT predictive maintenance sensors and any other condition monitoring tools with your existing data-gathering systems, like SCADA and BI. The purpose is to produce one comprehensive stream of data that can be analyzed by your predictive maintenance model.
- Coordinate Your Overall Maintenance Strategy
Most organizations employ a mix of different maintenance approaches, including preventive maintenance and elements of reactive maintenance. Your CMMS can help coordinate all these approaches. CMMS software collects and stores data from IoT predictive maintenance sensors; it also handles processes like scheduling, generating work orders, and tracking task completion, so that even the most complex maintenance strategy can flow seamlessly.
- Determine How To Share Asset Health Data
It’s a good practice to standardize data collection methods, naming conventions, and maintenance metrics across your organization. This makes asset health data more meaningful and facilitates sharing. Using a CMMS enables instant access to data, even for remote teams. Manage permissions so that each team member has access to the data they need.
Steps to Implementing Predictive Maintenance
What Are the Benefits of Predictive Maintenance?
The benefits of predictive maintenance go beyond the production floor. Not only does implementing a PdM maintenance solution make the workplace safer and production more efficient, but it also benefits the end users of the product and your organization’s bottom line.
Here are the major benefits of using predictive maintenance tools:
- Reduces unplanned downtime: When predictive maintenance software identifies a potential problem, teams can schedule maintenance during planned downtime. That way, the asset can continue to run as scheduled during normal hours.
- Safer work environment: Because planned maintenance is inherently less risky than reactive maintenance, predictive maintenance analytics creates a safer work environment. Catching failures early reduces the chance of injuries caused by unexpected machine malfunctions.
- Reduces the frequency of maintenance tasks: While preventive maintenance is a preferred strategy for many organizations, in some cases, it can lead to over-maintenance as teams perform unnecessary maintenance based on the manufacturer’s directions. With predictive maintenance, assets only receive maintenance when they need it, reducing costs and saving technicians time.
- Extends asset lifespans: Organizations invest substantially in their assets. So, increasing the availability and lifespan of those assets through predictive maintenance can drive maintenance KPIs and give organizations the best return on their investment.
- Lowers maintenance costs: It’s easier to correct smaller problems than to correct major failures. Predictive maintenance helps catch developing problems before they cause a full-blown shutdown or damage other parts of the equipment.
- Improves production quality: When machines aren’t running optimally, finished products are less likely to meet quality standards. Spotting and fixing issues early can reduce wasted materials, energy, and time.
- Supports data-driven maintenance decisions: If data gathered by sensors is stored in a cloud-based computerized maintenance management system (CMMS), teams can work together from wherever they are, consult with specialists, and make data-driven maintenance decisions based on predictive maintenance analytics.
- Improved work environment: With predictive maintenance, technicians can plan their work time to make the best use of their hours. Instead of rushing to fix assets after a breakdown they can plan maintenance as needed, lowering stress levels and minimizing unplanned downtime.
Effective asset management is crucial for organizations in today’s competitive environment, and predictive maintenance gives organizations the tools to do this successfully. The biggest benefit of predictive maintenance is that it makes the best possible use of maintenance resources.
What’s the Difference? Predictive Maintenance vs Preventive Maintenance
Preventive maintenance and PdM maintenance are both effective maintenance strategies, but there are key differences between the two. Understanding the differences between preventive and predictive maintenance can help your team select the best type of maintenance for your organization. In the same way, understanding the benefits of predictive maintenance and preventive maintenance can help you choose the right strategy. Many of the best maintenance programs use a combination of both strategies.
Preventive maintenance uses the expected life cycle of an asset to determine when to perform maintenance tasks. One common preventive maintenance example is changing a car’s oil every three months or every 3,000 miles.
A preventive maintenance schedule is straightforward and sufficient for some assets. Preventive maintenance on assets may be performed based on the calendar, a certain number of hours of use, or some other usage-based metric. It could include tasks like changing filters, performing lubrication, or replacing worn parts.
Of course, preventive maintenance presents some challenges. When the calendar dictates maintenance actions, some components are replaced before they need to be. There is also some risk incurred every time a machine is worked on. Preventive maintenance can be simpler to plan, but it uses more time, money, and parts.
Predictive maintenance uses the actual operating condition of an asset to determine what steps to take and when to take them. Instead of basing maintenance on a schedule, maintenance occurs when predictive maintenance analytics identify an irregularity in the asset’s performance. While similar steps, such as lubrication or parts replacement, may be taken, the difference is that predictive maintenance actions occur exactly at the time they are needed.
A predictive maintenance strategy can save both time and money, but it poses challenges, too: chiefly, the complexity of PdM maintenance implementation. Fortunately, with the right tools, you can overcome this. While equipment is operating normally, it can be monitored by predictive maintenance technologies and condition monitoring devices, like remote sensors. They can take measurements at regular intervals or continuously.
When paired with predictive maintenance software, these sensors can alert maintenance teams when any asset’s condition changes. Automatically generated work orders via a CMMS enable teams to act quickly, preventing equipment failures.
Maintenance teams can track and analyze asset condition data to help spot patterns and make more informed decisions for future maintenance. Ultimately, the goal of PdM maintenance is to maximize asset availability and minimize the time and cost spent repairing each asset.
How To Overcome Predictive Maintenance Challenges
Predictive maintenance comes with some built-in challenges. The program has a relatively high upfront cost, it requires managers to oversee complex operations, and it usually calls for training maintenance teams to use new technology. You can overcome these barriers if you implement your PdM maintenance program carefully.
It’s a good idea to start out with a pilot program, instead of trying to convert your whole organization to a predictive maintenance approach. Piloting the system lets you keep costs low, minimizes training, and limits the operation’s administrative requirements. It’s much more affordable to buy predictive maintenance technologies in small quantities, for example — and you’ll find that they quickly pay for themselves.
A successful pilot program will deliver a significant return on investment (ROI) that can then be invested in a larger PdM program. The pilot will also help drive understanding of predictive maintenance; maintenance crews will likely get on board with the new approach when they see results.
As we have seen, the right tools can also help you to overcome challenges. Using IoT sensors and high-quality data analytics allows you to meet the challenges of this highly complex maintenance approach.
What Are the 3 Types of Predictive Maintenance?
There are a number of different types of predictive maintenance. The most widely used types of predictive maintenance include vibration analysis, infrared thermography, and acoustic monitoring.
Vibration Analysis
Every rotating asset vibrates while in use. However, changes to an asset’s baseline vibration pattern usually indicate a new fault. Vibration analysis monitors an asset’s vibration levels in real-time, looking for anomalies.
Changes in vibration level can indicate premature wear and corrosion; they can also point to looseness, misalignment, and bearing faults.
Today, vibration analysis is highly sophisticated. Done right, the technique lets you spot machine faults months before they grow serious enough to cause a breakdown.
Acoustic Monitoring
Acoustic monitoring lets you — or rather, your condition monitoring tools — “hear” the early indicators of friction or wear and tear. Rotating equipment emits characteristic sounds as it deteriorates. Sometimes, those sounds are loud enough to hear with your naked ear, but acoustic monitoring catches much fainter sounds you can’t pick up, making it an excellent predictive tool.
Acoustic monitoring is widely used as a leak prevention tool, especially in systems with extensive pipelines for gas, oil, or liquids.
Infrared Cameras
Infrared cameras can detect subtle changes in temperature that may point to emerging machine faults.
Increases in temperature often result from high levels of friction, premature wear, or deterioration. Faulty wiring or other electrical issues are another possible root cause. Infrared thermography can also assist with locating gas or liquid leaks; it can spot changes in temperature caused by moisture or gas.
Of course, there are many other approaches to predictive maintenance. If you use a CMMS to anchor your predictive maintenance program, you’ll be able to integrate all of these different types of insights into one highly effective PdM model.
Predictive Maintenance Techniques
There are many ways to implement a predictive maintenance strategy, and many available predictive maintenance technologies. The following predictive maintenance tools and techniques give each organization the power to gather as much or as little information as they need to implement and maintain their predictive maintenance program.
- Vibration monitoring: Sensors installed on equipment can monitor in-depth vibration readings. Once the baseline for the asset is established, these sensors can be continuously monitored to detect deviations that could indicate faults like imbalances, misalignments, or bearing faults.
- Temperature monitoring: Similar to vibration monitoring, sensors can detect when temperatures rise above the asset’s normal temperatures. When a temperature increase is detected, technicians can find and address the root cause before failure occurs.
- Condition monitoring: Using a cloud-based CMMS stores sensor data in the cloud, where it can be monitored and analyzed from anywhere. Even if equipment is in a remote location or monitoring needs to occur off-site, users can access current or historical data and use it to make decisions about maintenance and replacement.
- Artificial intelligence (AI) analysis and recommendations: Learning how to read the signatures provided by vibration sensors takes years of education and experience. Now, even if your organization doesn’t have an expert on-site, advanced AI-powered analytics can assess machine vibration patterns and identify changes. It can even recognize different patterns of common issues, giving your team the insight to find and fix the problem even faster.
- Alarms: When vibration levels indicate faults, predictive maintenance software can send alerts to the appropriate personnel so they can take immediate action.
- Automated work orders: If the vibration monitoring software is integrated with a computerized maintenance management system, the CMMS can automatically trigger a work order when a fault is detected, saving time and reducing the amount of human intervention needed to fix the problem.
Predictive Maintenance Examples
Predictive maintenance tools and strategies can benefit assets in almost any industry. Here are just a few predictive maintenance examples from different industries.
Predictive Maintenance Examples in Automotive
Predictive maintenance tools can identify impending failures, such as a slowing conveyor belt or abnormalities in vibrations from stamping or press machines. It can also be used on other assets, like forklifts and painting equipment.
Predictive Maintenance Examples in Food and Beverage
In the food and beverage industry, predictive maintenance technologies can play a role in not only ensuring maximum uptime, but also ensuring all products are created in compliance with strict food regulations. Predictive maintenance can be used on equipment like mixers and blenders, dust collection systems, extrusion equipment, pumps, and conveyor belts.
Predictive Maintenance Examples in Manufacturing
Manufacturers of all types can use predictive maintenance technology to improve the consistency and quality of their product output, reduce labor costs, and prolong the lifespan of assets. Predictive maintenance in manufacturing can help predict and reduce failures for assets like fans, pumps, and motors.
Predictive Maintenance Examples in Life Sciences
Many manufacturers in the life sciences industry are subject to audits from local, state, and federal authorities. Predictive maintenance technologies can ensure equipment stays running within required parameters and can provide organizations with audit-proof records of asset history. And in cases where products need to be refrigerated or frozen, sensors help ensure that the equipment used to keep them at the proper temperature is always working as intended.
Predictive Maintenance Examples in Oil and Gas
Reliability is incredibly important in the oil and gas industry, where equipment failures could have environmental consequences and pose safety threats to employees. Predictive maintenance on assets like pumps, boilers, and compressors can help reduce the risks of unplanned failure and its consequences.
How To Create a PdM Maintenance Program
Making the switch from reactive to predictive maintenance doesn’t happen overnight. But advances in predictive maintenance technologies, such as CMMS software and wireless vibration sensors, have made predictive maintenance a more attainable strategy than ever before. There are a few questions to keep in mind for each asset when considering creating a predictive maintenance plan:
- If this asset fails, how does it impact production?
- How much does it cost to repair this asset?
- How much does it cost to replace this asset?
Answering these questions for each piece of equipment can help teams narrow down which assets to maintain on a predictive basis.
Predictive maintenance is not necessarily the most effective strategy for every asset. Some assets can be run to failure with little to no impact on production or the bottom line. Others benefit from simple and straightforward preventive maintenance. But for some assets, predictive maintenance is the best strategy.
Even if you plan to use predictive maintenance tools on just a handful of assets, it helps to plan ahead and build a program that your maintenance team can stick to. Here are six key steps for setting up your predictive maintenance program:
- Identify which assets should be targeted for predictive maintenance
- Choose the predictive maintenance tools and methods you will use to monitor asset condition (such as sensors and a CMMS)
- Select and train an implementation team to learn and carry out predictive maintenance technologies
- Perform system integrations to get a complete picture of asset health
- Coordinate your overall maintenance strategy, identifying which approach will be used where
- Determine how asset health data will be shared among team members, stakeholders, and auditors
Ultimately, implementing a successful predictive maintenance program requires taking a long-term view of your organization’s goals and needs. No two predictive maintenance plans will look the same.
How Can You Control Predictive Maintenance?
Predictive maintenance, by definition, involves collecting and analyzing a lot of data. The best way to control predictive maintenance is by using a computerized maintenance management system (CMMS) to connect and manage data coming in from work orders, real-time predictive maintenance analytics, and maintenance history, making it accessible to the appropriate personnel no matter where or when they’re working.
Without a CMMS, maintenance teams are often left guessing about the historical maintenance of an asset. Work orders are often on paper, and paper work orders take time to find, complete, and file away. Paper work orders also make it difficult to track what’s completed or still outstanding. It’s nearly impossible to compare the full range of requests, in-progress tasks, and priority jobs when they’re all on separate sheets of paper.
A CMMS makes work orders so much easier to schedule, assign, and complete. Work orders can also be prioritized based on asset criticality, ensuring the most important tasks get assigned to the right technicians. Managers can see which tasks are outstanding and assign jobs to staff already working on a specific asset or those with the expertise needed for the task.
Technicians and decision-makers will also have access to historical maintenance records. When an asset has a history of multiple failures in a short time frame, experts can use the data and predictive maintenance analytics to get to the root cause of the issue or decide if it’s time to replace the asset.
Key Features in eMaint’s Predictive Maintenance Software
eMaint CMMS gives organizations a full suite of predictive maintenance tools. With it, organizations can:
- Define monitoring classes for each asset
- Monitor noise, vibration, temperature, lubricants, wear, corrosion, pressure and flow independently
- Enter manually or import meter readings
- Define upper and lower boundaries of acceptable operation for each asset
- Display readings as a report with color-coded exceptions
- Auto-trigger emails when a boundary is exceeded
- Auto-generate work orders when a reading falls outside of predefined boundaries
- Perform data analysis to identify failures early, prevent breakdowns, and optimize maintenance resources
- View condition monitoring diagram
Case Study: Using eMaint CMMS Condition Monitoring for Predictive Maintenance
Cleveland Tubing, Inc. is a manufacturer of flexible, collapsible tubing products, including FLEX-Drain and PumpFlex. The company set up eMaint so that meter readings on key indicators (temperature, pressure, fluid levels, suction) are imported and used to trigger priority work orders when work or inspection is needed based on predefined ranges.
Gary Payne, maintenance manager for Cleveland Tubing, noted that eMaint has become their maintenance decision support system, informing them of the tasks that need to be performed each day, based on elapsed time, equipment utilization and condition-based indicators. They also experienced:
- Automated reports for replenishing inventory on stocked and non-stocked parts
- Streamlined time tracking of labor for department of five maintenance employees
- Improved ROI calculations with better allocation of labor and material costs to assets
- The ability to evolve from reactive maintenance to planned maintenance to predictive maintenance via condition monitoring and automated alerts of potential problems on critical equipment
- Easily measure and track KPIs against world-class standards (90% planned maintenance)
Predictive Maintenance FAQs
Which Industries use Predictive Maintenance?
Predictive maintenance is a useful strategy for a wide range of industries. It leverages technologies and tools—from sensors to CMMS software to statistical analysis—to reduce unplanned downtime and wasted resources.
Any organization seeking to extend the lifespan of its assets and optimize maintenance spending can use predictive maintenance.
eMaint predictive maintenance software serves clients in industries such as:
- Manufacturing
- Food & beverage
- Government
- Healthcare (including pharmaceuticals, medical devices, and more)
- Energy (including oil & gas, wind, and more)
- Education
- Warehousing & distribution
- Transportation & fleet
- Facilities
What are the Benefits of Predictive Maintenance?
Predictive maintenance is a cost-effective maintenance strategy with numerous benefits. Among them:
- Avoiding unplanned downtime
- Improving productivity
- Extending asset life and maximizing time between purchases
- Reducing the amount of materials and spare parts needed
- Creating a safer work environment
- Benefiting the bottom line
