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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 is experiencing 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 are either done after machines fail or maintenance is performed regularly based on the manufacturer’s guidelines. Today, many organizations are using new software and technologies 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 can be used, including infrared testing, vibration analysis, oil analysis, and more.
There is not one singular best maintenance method, and assets within the same facility may benefit from different maintenance strategies. But for assets that are critical to the organization, predictive maintenance is often the best approach.
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 predictive maintenance make the workplace safer and production more efficient, it benefits the end users of the product and your organization’s bottom line.
Here are the major benefits of predictive maintenance:
- 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 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 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-blow 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.
- 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 between predictive maintenance vs preventive maintenance?
Preventive maintenance and predictive 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. 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.
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 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 is more complex to implement. While equipment is operating normally, it can be monitored by 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.
Asset condition data can be tracked and analyzed to help maintenance teams spot patterns and make more informed decisions for future maintenance. Ultimately, the goal of predictive maintenance is to maximize asset availability and minimize the time and cost spent repairing each asset.
Predictive Maintenance Techniques
There are many ways to implement a predictive maintenance strategy. These 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 offsite, 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 can benefit assets in almost any industry. Here are just a few predictive maintenance examples from different industries.
Predictive Maintenance 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 in Food and Beverage
In the food and beverage industry, predictive maintenance 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, and pumps and conveyor belts.
Predictive Maintenance 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 in Life Sciences
Many manufacturers in the life sciences industry are subject to audits from local, state, and federal authorities. Predictive maintenance 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 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 Predictive Maintenance Program
Making the switch from reactive to predictive maintenance doesn’t happen overnight. But advances in predictive maintenance technology, 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 is production impacted?
- 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 conduct predictive maintenance 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 techniques
- 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 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 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 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
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How does Predictive Maintenance Work?
Condition monitoring sensors are installed directly on assets and capture performance data. A number of factors can be measured, such as vibration or temperature, depending on the asset. The sensors can detect issues such as pressure leaks, vibration abnormalities, or unusual voltage.
Cloud technology enables condition monitoring sensors to share the data they collect. Paired with the right predictive maintenance software, alarms and work orders can be triggered when asset conditions surpass defined thresholds.
Data modeling, based on known machine behavior and failure modes, is used to spot issues before they escalate to failure.
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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 their assets and optimize their 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
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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