Predictive Maintenance: What it is, Benefits, and Tech

Your organization relies on its assets each and every day. Whether it’s a pipeline at your biggest refinery, process equipment at a core site, or a newly purchased backhoe, an unexpected malfunction can result in expensive downtime, even more expensive repairs, and even missed production timelines. Staying ahead of the curve by anticipating future maintenance needs before breakdowns occur should be a priority for every enterprise.

Predictive maintenance allows companies to identify maintenance needs before breakdowns occur, so downtime is planned and expenses are reduced. The predictive maintenance market is expected to grow 26.2% by 2030 . Read on to learn how your company can get in on the trend and use predictive maintenance to keep projects on schedule and within budget.

What is predictive maintenance?

Predictive maintenance is a forward-looking strategy that leverages modern technology and sophisticated algorithms to anticipate and address maintenance needs. The goal of predictive maintenance is to minimize unnecessary maintenance and reduce the need for reactive maintenance after something goes wrong. It relies on sensors, data analysis, performance modeling, and other techniques to accomplish this.

Predictive maintenance vs. preventive, reactive, and condition-based maintenance

Predictive maintenance is one of the best ways to handle the maintenance needs of a wide variety of assets. But as just a single stage of the maintenance maturity model , it’s not a be-all, end-all approach. Instead, it should form the core of a reliability-centered maintenance strategy that aligns with your enterprise’s needs. Reliability centered-maintenance is all about understanding the condition, risk, and importance of an asset, which helps your enterprise make informed decisions about which maintenance approach makes the most sense for each asset. . You don’t need a suite of sensors to anticipate when a light bulb will go out, after all.

These alternate approaches include preventive,reactive, and condition-based maintenance. Preventive maintenance, also called planned maintenance, is a less-complex approach that schedules maintenance based on the time elapsed since the last time equipment was maintained. Like predictive maintenance, this strategy aims to eliminate unexpected downtime. However, preventive maintenance can easily result in over-maintaining equipment due to the lack of data predictive maintenance relies on, increasing both costs and scheduled downtime.

Reactive maintenance simply repairs equipment after it breaks down. While this method cuts down on initial overhead, it’s likely to be more expensive over the long term. Budgets are impossible to stick to, production slows, and employees are being paid for time when they aren’t able to actively work.

Condition based maintenance, a part of reliability-centered maintenance, involves aligning your maintenance strategy with the current condition of an asset. Basing asset management on condition alone can be more effective than preventative maintenance in scaling back workload. However, it doesn’t provide enough insight into the health of your company’s assets in real time, as it requires qualitative and quantitative asset inspections on a frequent basis.

Maintenance maturity and asset strategies aren’t one-size-fits-all for your enterprise’s various assets, sites, and businesses. Leveraging tailored strategies across multiple sites and asset types helps maximize asset performance and your maintenance resources.

Benefits and challenges of predictive maintenance

Predictive maintenance is a cost-effective, dependable strategy. Let’s dive into a few of its many benefits.

Increased uptime

Tired of moving from one unexpected failure to the next, or never knowing whether the next scheduled maintenance is really necessary? Hexagon’s research has shown that an asset management solution that supports predictive maintenance can result in a 5-15% average decrease in critical asset downtime . Predictive maintenance provides your company with the data it needs to reduce unanticipated downtime and keep scheduled downtime to what’s strictly necessary for asset performance.

Reduced maintenance costs

Similarly, only maintaining assets when they actually need it, while reducing downtime and the more expensive repairs that often result from equipment failure, results in less money spent. McKinsey found that 50% of fixed costs at a typical chemical plant are maintenance-related.

Predictive maintenance greatly reduces these costs. For example, Tecnichapa , a metal component producer, decreased their maintenance costs by 15%, lowered plant energy

cost by 30%, and reduced the cost of external subcontracting by 25% by using an asset management solution that supports predictive maintenance. With more insights into the current state of all your assets, you can feel more confident that your company will stick to its maintenance budget.

Enhanced productivity

Predictive maintenance helps assets perform as they should more consistently. McKinsey has also found that unplanned downtime is the biggest cause of lost production at a typical chemical plant. And an asset management tool with predictive maintenance capabilities can lead to a 10-30% improvement in maintenance workforce productivity . As a result, organizations see increased sales and higher equipment efficiency. Everything stays on track, workers get more down, and productivity rises.

Of course, predictive maintenance comes with its share of potential challenges as well. Here are a few difficulties organizations may face when implementing a predictive maintenance strategy.

Increased upfront investment

The cost of implementing a predictive maintenance strategy, including sensors and other tools, deter some organizations. But the return on investment is well worth the initial cost, and your company can reduce expenses to some extent by applying predictive maintenance to only the highest priority assets that stand to benefit the most from this approach. Managing critical assets this way can improve uptime, increase overall production, and boost your enterprise’s bottom line.

Finding the right software

Selecting an intuitively designed platform that includes all the features needed to support predictive maintenance is easier said than done. Look for a holistic solution that focuses on asset performance management and provides all the maintenance features your business needs in one place, from maintenance digitization to building risk mitigation strategies and beyond. It should also support your organization’s front-line workers with a mobile-first approach and an integrated app, so they can capture and review data from anywhere with an internet connection and complete the job more efficiently.

Not one-size-fits-all

Every part of your enterprise’s maintenance strategy should be tailored to its specific needs, and predictive maintenance is no exception. Applying a predictive maintenance approach to all assets indiscriminately will result in needless complexity and excessive costs. Take the time to evaluate which maintenance technique suits each asset best, and select the appropriate measurement tech for the equipment that would benefit from predictive maintenance.

5 predictive maintenance technologies

Predictive maintenance relies on using the right tools and techniques to measure an asset’s key maintenance indicators. Here are five of the most common predictive maintenance analytic methods and when it makes sense to use them.

1. Vibrational analysis

Vibrational analysis is performed on rotating machines, which use kinetic energy to function and emit a measurable amount of vibration. It uses sensors to first establish a baseline vibration. As data is collected from these sensors, subtle changes can be detected and used to schedule upcoming repairs. Vibrational analysis is often used for predictive maintenance in the oil and gas industries where rotating machines are common.

2. Acoustical monitoring

Acoustical, or sound, monitoring is another employes sensors to pick up the unique sound profile of a machine. AI is then trained to detect when sounds differ from this profile. These sensors are highly attuned to sound and can pick up on when parts of the machine are loose or lack lubrication. Acoustical monitoring is most successful in environments where there’s little background noise, which can throw off the sensors.

3. Infrared analysis

Infrared analysis measures the temperature of assets and can help identify issues like the overheating of machinery, which can degrade equipment and injure employees. Leaky seals may also contribute to energy loss and higher costs. This type of analysis can be applied to many different types of equipment, as long as they produce a measurable amount of heat. Sensors can be placed either at one specific location on the machine or at several locations depending on the type of equipment in question.

4. Oil analysis

Oil analysis analyzes a machine’s lubrication. Lack of lubrication or loss of lubrication integrity can cause unplanned breakdowns and excessive downtime in any machine that uses oil to function. When companies can plan oil changes intelligently, production halts are minimized. The most effective type of oil analysis is done using sensors that regularly sample and analyze oil makeup.

5. Machine learning

Machine learning is a key part of predictive maintenance, intelligently and automatically detecting when maintenance is needed. AI and machine learning are involved in most predictive maintenance strategies because they make automating monitoring that previously had to be done manually possible. With a modern, AI-powered asset management solution, maintenance schedules are suited to individual assets and team members are alerted to maintenance needs automatically.

How to get started with predictive maintenance

Building a predictive maintenance plan is a multi-step process. It begins by determining which assets would benefit from a predictive maintenance approach before identifying which metrics best indicate the need for maintenance for each asset. Then it’s time to obtain and set up the necessary sensors and software, connecting each piece of tech to your maintenance platform. Build up a robust database filled with the information needed to make intelligent maintenance decisions for your assets, and leverage historical and manufacturer performance data models to expedite time to value. Finally, when the data indicates it’s time, schedule and carry out the maintenance work itself.

Power predictive maintenance with the right tool

Predictive maintenance is a must for all data-driven organizations that want to develop an intelligent maintenance strategy tailored to their unique assets.

HxGN EAM provides everything your company needs to get started with predictive maintenance and maximize asset performance. It includes powerful tools like Asset Performance Management , a centralized solution for all your enterprise’s asset management and maintenance needs. It ensures your company always knows the best way to maintain its assets at the lowest overall cost. Meanwhile, HxGN EAM Constraint Optimizer streamlines decision-making and increases scheduling efficiency. And the last piece of the puzzle, HxGN EAM Digital Work , boosts data accuracy and helps your company create an intuitive work experience for all employees.

Ready to make predictive maintenance a part of enterprise asset management at your company?Contact us today.