What Is Predictive Maintenance? (Benefits, Cost & Examples)

In November 2010, Qantas Flight 32 took off on a flight from Singapore to Sydney, Australia, but an engine failure shortly after takeoff forced an emergency landing back in Singapore. Luckily, no one was hurt, but the delays and disruptions that ensued could have been avoided with more robust predictive maintenance solutions in place.

What Is Predictive Maintenance?

Predictive maintenance, also known as condition-based maintenance, is a proactive maintenance strategy that monitors asset condition and performance in real time to predict when an asset needs maintenance before it breaks down.

Using a combination of sensors, Internet of Things (IoT), machine learning, data analytics and modeling, predictive maintenance will determine whether there are any warning signs for impending failure. This allows you to schedule maintenance when your equipment and machinery actually needs it, rather than merely guessing. If used frequently and correctly, predictive maintenance can help you avoid costly repairs and equipment downtime — like that of an entire Airbus A380 — and could save your business thousands of dollars in the long run.

Goals of Predictive Maintenance

Since predictive maintenance is primarily executed by technological means (rather than manpower), its aim is to increase efficiency and productivity as well as to reduce maintenance and equipment costs for businesses over time. Predictive maintenance achieves this by predicting when maintenance is required, allowing the maintenance frequency to be as low as possible for an asset.

Another major goal of predictive maintenance is to significantly reduce unexpected breakdowns and unplanned downtime compared with more traditional methods of maintenance that involve manual inspections by a technician. In this sense, predictive maintenance might be viewed as a more advanced type of preventive maintenance, simply detecting faulty conditions more quickly and before the equipment failure occurs.

Predictive Maintenance vs. Preventive Maintenance

However, predictive maintenance should not be confused with preventive maintenance, which is also a proactive maintenance strategy that has the same goal in preventing or minimizing the likelihood of equipment breakdowns. But preventive maintenance is primarily performed at the urging of time-sensitive triggers (i.e. weekly, monthly or annually), or based on usage (i.e. inspection takes place after every 1,000 miles used). Because preventive maintenance is done on a set maintenance schedule, it may lead to doing too much maintenance on an equipment. Alternatively, predictive maintenance requires more advanced steps for accuracy and uses technology to optimize when equipment requires maintenance. Examples include:

• Condition-monitoring devices — Sensors are used to collect information on the performance on an equipment. Depending on the type of sensor you used, information such as temperature, vibration, noise and pressure can be collected.
• Internet of Things —  IoT translates information collected by sensors such as temperature or vibration into digital signals that can be measured and analyzed over time to make predictions on when maintenance is required on an equipment.
• Machine learning — Machine learning uses the sensor data to learn the normal behavior of a piece of equipment, and when it has enough data, it can find anomalies in the performance of said equipment.
• Computerized maintenance management system (CMMS) — A CMMS is crucial for the maintenance aspect of predictive maintenance, as it creates a work order for a technician to complete when an anomaly occurs.

Is Predictive Maintenance Right for Your Business?

Predictive maintenance requires a high capital investment in technology and labor to implement. If a business does not have much expensive equipment or strict health and safety standards, then they should consider a preventive maintenance program rather than a predictive maintenance one.

Here are some questions your business should consider before you decide to implement a predictive maintenance program:

• What is the approximate value of our equipment/machinery?
• Is it necessary for our machinery?
• What has the equipment history been like thus far?
• What do our records show regarding equipment downtime, defects, losses and safety threats?
• What sort of predictive maintenance tools would be best for our business’ needs?
• Is my business financially able to invest in these kinds of data-driven technologies or experts at this time?

How to Implement a Plan for Your Business

Implementing a predictive maintenance plan in your business requires that you first identify a list of critical assets for the program’s implementation. Afterward, establish a baseline of all identified equipment, and monitor each piece of machinery regularly to assess condition. It is not possible to determine whether machinery may be malfunctioning if it’s not monitored regularly.

After identifying your most valuable assets and equipment, here are additional steps to help you begin applying predictive maintenance to your business:

• Step 1: Use pre-existing data to analyze your equipment — Collect the data from sources such as maintenance records, a CMMS or data that your machine already produces for future incident logging or historical analysis. This data will give you actionable information about the machine behavior and will set a baseline for the program.
• Step 2: Identify failure modes — Perform an analysis of the critical assets that you’ve chosen to establish failure modes. You should focus on the frequency of failures, severity of machine failure and the difficulty of identifying failure.
• Step 3: Install IoT sensors on your equipment — The failure modes that you have identified for a piece of equipment will help you determine the type of sensors that you’ll need to install. For instance, a rotating equipment will need a vibration meter to detect the common faults a rotating equipment is prone to.
• Step 4: Work with one series of algorithms, and watch for improvement — Apply one standard algorithm to all of your equipment rather than to each one. Over time, these algorithms will automatically improve, optimizing performance as they accumulate more data.
• Step 5: Analyze data in bulk — Cloud-based technology enables efficient data readings for hundreds of machines at a time. Rather than bombarding or retraining your engineers to crunch these numbers, let artificial intelligence get the job done faster for you.
• Step 6: Set up alerts for detections — Connect your condition-monitoring devices to a CMMS to alert your technician to do a manual inspection when an anomaly in an equipment is detected.

Benefits of Predictive Maintenance

With the decision to use predictive maintenance for your business comes several benefits. A few of these might include:

• Funds spent only on necessary inspections, repairs or part replacement (no guesswork)
• Fewer or no lost-time incidents due to surprise malfunctions and reactive maintenance repairs
• Maximizes equipment lifespan due to necessary maintenance tasks and upkeep
• Increased revenue by protecting your most valuable equipment

Disadvantages of Predictive Maintenance

While there are many benefits to predictive maintenance, there are also many disadvantages. A few of them are:

• Cost of implementation — Even more than the cost of installing necessary technology, predictive maintenance may also require skilled maintenance teams who can interpret the data accurately.
• Learning curve and downtime — Once you’ve installed predictive maintenance software, the relevant staff will have to understand how to read and use it effectively.
• Possibility of misinterpretation — For the amount you’ll be spending, it’s only fair that you’ll expect 100 percent accurate equipment readings. Unfortunately, this isn’t always the reality. Technology can fail to take essential factors into account when analyzing an asset, such as age of equipment and weather.

Types of Predictive Maintenance Technologies

To give you a better idea of your options for predictive maintenance, here are a few of the most commonly used technologies:

• Acoustic monitoring — Systems that can imitate the hearing abilities of experienced workers to diagnose malfunctions by sound. This method is commonly used in industrial environments, though it can be difficult to detect certain sounds above the background noise of a facility.
• Infrared technology — This approach involves checking the temperature of equipment frequently, which enables the easy tracking of operating conditions. This method may include identifying “hot spots” in electronic equipment, identifying fuses that are nearing capacity and locating faulty terminations in electrical circuits.
• Vibration analysis — This method looks for and monitors significant changes from a machine’s standard vibration. To increase accuracy, typical vibrations should be recorded multiple times so that deviations are more easily noticed.
• Oil analysis — This type of analysis involves testing oil from a machine for viscosity, wear particles, and the presence of water, to name a few. This method may be most effective and relevant to the transportation industry (e.g. trains, buses, trucks).
• Motor circuit analysis — Used across a variety of industries — from automotive to the marine industry — motor circuit analysis measures a motor’s stator and rotor, in addition to detecting contamination and ground faults. Motor circuit analysis can test new motor inventory before installation of equipment as well as existing motors for system health.

How Much Does Predictive Maintenance Cost?

Due to the fact that preventive maintenance only involves labor costs compared with the high initial capital investment of predictive maintenance, you might be wondering whether predictive maintenance is even cost-effective for your business — and what exactly the cost entails. Here’s a breakdown of what it may cost for your business:

1. Sensors — Price for sensors depends on the type of sensors you need and the brand that you purchase from. They can range from $100 to thousands of dollars per sensor. For example a temperature sensor can cost around $100 whereas a vibration sensor can cost around $1,000.
2. Software — Depending on your setup, you may need more than one software. The first software you need is a CMMS software, which is usually priced per user and starts around $400 per user a year. The other software you may need is a data analytics tool to collect and analyze the historical data from all your sensors, this can start from around $200 depending on the software you choose.
3. Installation — If your technicians don’t have the maintenance techniques and skills required to install the sensors and connect them to the software themselves, then you will need to hire a specialist to install them for you. Depending on the number of sensors you need to install and the type of equipment, this can start from the low thousands and up to tens of thousands of dollars.
4. Skilled maintenance expert — An experienced maintenance engineer is required to accurately interpret the data that is coming in from the condition-monitoring devices. The salary range for a maintenance engineer from Glassdoor is around $86,000 a year. However, a maintenance engineer may not be necessary for your predictive maintenance program if you have a clear understanding of the failure modes of your equipment.

Industries That Benefit From Predictive Maintenance

While most businesses could technically use and benefit from predictive maintenance, there are a few specific industries for which the splurge is most worthwhile. Essentially, any industry that requires large and valuable assets would see a cost savings. That’s because, in such use cases, the cost of repairs or new equipment would likely be more than the installation costs of predictive maintenance systems.

A few relevant industries include:

• Food and beverage — Alongside the goal of turning a profit, the food and beverage industry must be focused on preserving the health of their customers. In fact, if people get sick through any fault of a business in the food and beverage industry, it can be a major liability. To prevent this, food storage equipment and tools need to be running optimally. A restaurant must prioritize food safety to prevent potential lawsuits that may occur if an essential equipment is not working properly. In this case, predictive maintenance is a solution worth considering.
• Manufacturing — Another industry where predictive maintenance is worth the splurge is manufacturing, where large and expensive tools and equipment are used regularly. If such equipment were to fail, an entire business that relies on manufacturing could suffer major losses, which may well outweigh the installation and operational costs of predictive maintenance technology.
• Power and energy — Much like caring for any major machinery, power plants are definitely a likely candidate for predictive maintenance. It’s crucial that this type of technology functions properly 100 percent of the time. In particular, the energy industry will benefit from increased asset efficiency and uptime, which boosts profitability, when implementing a predictive maintenance strategy.
• Waste management — Improving asset efficiency for waste management equipment and facilities is a major game-changer for the waste and recycling industry. It’s common for this industry to struggle with keeping their employees and machinery working efficiently, while also reducing maintenance costs, time-sensitive repairs and even the need for replacement equipment.
• Buildings — Though this one perhaps sounds a bit obscure, even buildings can benefit greatly from the implementation of predictive maintenance systems. For example, sensors can detect and display real-time data based on the building’s condition. This information is then stored in a network or database, which can be referenced to determine when maintenance should be scheduled.

The Return on Investment

Naturally, every business is different, but using predictive maintenance for your most valuable equipment may yield significant ROI rates. In fact, research provided by the U.S. Department of Energy shows that predictive maintenance could result in a 30 to 40 percent savings for the business that implements it.

Moreover, many businesses suffer from unexpected downtime due to equipment malfunctions each year. It’s not so much a question of if as it is when. Even worse (depending on the scale of your business), one hour of downtime is worth approximately $250,000 of loss, with the average amount of downtime lasting around 4 hours. Poof — a million bucks, lost. Sounds like a nightmare, right?

These are only a few facts and stats that reveal just how valuable a predictive maintenance program is, should the installation costs cover the approximate expenses of your broken machinery. Predictive maintenance helps you plan for, and avoid, malfunctioning machinery that needs to be inspected or updated, so you can avoid downtime altogether.

Examples of Predictive Maintenance

1. Refrigeration Sensor

In a restaurant, the health of any food storage or cooking utility is paramount to the business’s success. If a restaurant’s refrigerator were beginning to malfunction, or required any type of update, predictive maintenance technology could measure the functionality and alert restaurant staff to any issues. A sensor would be able to evaluate elements of the fridge such as temperature and vibration to determine asset performance. Having this advance notice would enable the restaurant to fix the potential problem before it resulted in downtime.

2. Power Outage Prevention

Power outages can cause major inconveniences for those they affect. With predictive maintenance technology, they can be detected early and therefore avoided. Sensors would again come into play in this scenario to provide insight into assets using artificial intelligence. This intelligence informs companies within the energy industry when equipment is likely to fail.

3. Oil and Gas Industry

The oil and gas industry often lacks visibility into the condition of their equipment in remote offshore places. Maintenance technicians would visit these sites based on a time interval to inspect the condition of the equipment even when it’s not necessary. With predictive maintenance, oil and gas companies can evaluate the health and performance of their equipment and only schedule maintenance when an out-of-the-norm issue is detected.

4. Building Management

Those who own or manage buildings can control and monitor them from any location using predictive maintenance technology — specifically, software for ventilation and energy management. This “smart system” would enable owners and managers to keep the building environment within a certain temperature range, track humidity, and more. This monitoring can help improve overall energy costs for the building.

5. Manufacturing Monitoring

Since a manufacturing plant tends to have many costly assets and valuable equipment, they might invest in infrared imagers to monitor certain elements of assets, such as temperature to prevent overheating. This system of predictive maintenance helps plants avoid overusing essential equipment, pushing machinery to the point of disruptive breakdowns.

6. Aircraft Maintenance

As shown in the Qantas Flight 32 example, if an airplane has an unexpected engine failure, it can lead to delays and incur big costs. Usually aircraft maintenance relies on a set schedule of maintenance for inspections and repairs. However, using predictive maintenance that uses sensors and flight data recorder, it can predict where irregularities may occurs and schedule planned maintenance ahead of time.