Predictive maintenance, also known as condition-based maintenance, is a proactive maintenance strategy that monitors the condition and performance of an asset 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 utilized frequently and correctly, predictive maintenance can help you avoid costly repairs and equipment downtime and could save your business thousands of dollars in the long run.
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 machine 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.
We’ll explore the differences between predictive and preventive maintenance more fully in the section below.
Predictive maintenance should not be confused with preventive maintenance, which is also a proactive maintenance strategy that has the same goal of predictive maintenance in preventing or minimizing the likelihood of equipment breakdowns. 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 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 assess 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 data generated from the sensors to learn the normal behavior of an equipment and then when it has enough data, it can find anomalies in the performance of an equipment. Machine learning depends on relevant and quality data to make accurate predictions.
- Computerized maintenance management system (CMMS) – A CMMS is crucial for the maintenance aspect of predictive maintenance, it creates a work order for a technician to complete when an anomaly occurs.
Condition-monitoring devices and a combination of these other predictive maintenance technologies may be used on any of your assets or machinery.
There are also notable cost differences between preventive and predictive maintenance. Due to the fact that preventive maintenance only involves labor costs compared with the high initial capital investment on sensors, software, setup, and training that are indicative of predictive maintenance, preventive maintenance costs significantly less money to implement for your business.
Predictive maintenance requires a high capital investment in technology and labor to implement. If a business does not have many expensive equipment or strict health and safety standards, then they should consider a preventive maintenance program rather than a predictive maintenance program.
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 equipment would be best for our business’s needs?
- Is my business financially able to invest in predictive maintenance technologies or experts at this time?
- These are just a few things to consider before you decide to move forward in implementing predictive maintenance in 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. Afterwards, 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, 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 predictive maintenance 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 sensors on your equipment — The failure modes that you have identified for an 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 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 and machine learning 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 a anomaly in an equipment is detected
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)—predictive maintenance analyzes and suggests these steps just before any significant damage or equipment downtime occurs.
- Fewer or no lost-time incidents due to surprise malfunctions and reactive maintenance repairs—predictive maintenance keeps your business functioning like a well-oiled machine.
- Maximized equipment lifespan due to necessary repairs and upkeep—predictive maintenance helps prevent any real equipment damage from occurring, so you can have peace of mind about the functionality of your assets.
- Increased revenue—predictive maintenance, while it may be costly upfront, could end up saving your business major dollars down the line by protecting your most valuable equipment.
While there are many benefits to predictive maintenance, there are also many disadvantages. A few of them are:
- Cost of implementation—there’s no getting around the fact that predictive maintenance is extremely pricey at the outset. Even more than the cost of installing necessary technology, predictive maintenance may also require skilled employees who can interpret the data accurately.
- Learning curve and downtime—once you’ve installed predictive maintenance technology, the relevant staff will have to understand how to read and use it effectively. This learning curve may be substantial, and may result in lost time on the job.
- Possibility of misinterpretation—for the amount you’ll be spending on predictive maintenance, it’s only fair that you’ll expect 100% 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.
- Other maintenance strategies may be more beneficial – Not all assets that have failures are costly. For those assets other maintenance strategies such as preventive maintenance or reactive maintenance may be more suited for your business.
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 of predictive maintenance 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 predictive maintenance 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, locating faulty terminations in electrical circuits, and more.
- 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.
We already mentioned that predictive maintenance is pricey, but you might be wondering whether it’s feasible for your business—and what the cost entails. Here’s a breakdown of what it may cost for your business:
- 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 $1000.
- Software — Depending on your predictive maintenance program 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 data from all your sensors, this can start from around $200 depending on the software you choose.
- Installation — If your maintenance technicians are not skilled enough 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.
- 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.
All these expenses to implement a predictive maintenance program quickly adds up and may not be realistic for your business.
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 could benefit from the implementation of predictive maintenance. In such cases, the cost of repairs or new equipment would probably outweigh 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 which relies on manufacturing could suffer major losses, which may well outweigh the installation and operational costs of predictive maintenance technology.
- Power & 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% of the time, and predictive maintenance systems help make that target possible. In particular, the energy industry will benefit from increased asset efficiency if implementing predictive maintenance, which boosts profitability.
- 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. Predictive maintenance can help keep all of these in check.
- 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.
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%-40% 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.
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.
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.
Oil and gas industry often lack 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 issue that is out of the norm is detected.
Those who own or manage buildings can control and monitor buildings 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.
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.
If an airplane has an unexpected breakdown 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.