Any business that deals with equipment on a regular basis needs a way of preserving that equipment. In industries like construction and manufacturing, vital tools can break down without anyone noticing the signs; and that’s where predictive maintenance comes in.
Predictive maintenance allows your business to tackle issues proactively and improves the life cycle of your equipment. It’s an approach that’s unique in that it leverages automation to indicate when repairs are needed.
Table of contents
- What is predictive maintenance?
- Predictive maintenance vs. preventive maintenance
- Benefits of predictive maintenance
- Disadvantages of predictive maintenance
- Predictive maintenance techniques
- Sample predictive maintenance process
What is predictive maintenance?
Predictive maintenance is a form of equipment maintenance that relies on data to anticipate issues or equipment failures. It’s a way for your business to be proactive and to make precautionary measures part of your daily routine.
The core of successful predictive maintenance is real-time information. The more consistently you’re receiving information about your equipment, the easier it is to pick up on and avoid serious defects.
Predictive maintenance has soared in popularity over the past few years, as sophisticated AI and automation technology allows businesses to monitor their equipment more closely. This monitoring aims to reduce the overall frequency of equipment failures by helping you use collected data to continuously improve machine performance.
How does predictive maintenance work?
Predictive maintenance measures conditions using AI like sensors and alarm systems which are built-in or attached to your equipment. These systems can measure things like fuel use, component speed, machine blockages, and temperature to detect when machinery operates atypically.
The data you get doesn’t only help you address current issues. It also allows you to make predictions about how likely similar malfunctions are in the future and how you’ll need to adjust your processes to mitigate them. This brings you closer to operational excellence by minimizing equipment downtime and maximizing productivity.
Predictive maintenance vs. preventive maintenance
Predictive and preventive maintenance are the two types of maintenance most often confused for one another. That’s because preventive maintenance is also a proactive way of assessing equipment, but it doesn’t rely on automation or AI. Instead, predictive maintenance entails conducting regular equipment inspections to detect issues. This means a person still needs to be there to carry out the inspection.
A second difference between preventive and predictive maintenance is frequency. Since preventive maintenance doesn’t use data but instead relies on established maintenance schedules, it tends to be more frequent (and more general) than predictive maintenance. Predictive maintenance only occurs when your AI tools detect or anticipate a problem, whereas preventive maintnenance inspections are often conducted daily.
This focus on performance monitoring means that predictive maintenance explicitly targets problem areas or defects in your equipment, and is more precise about where those defects are likely to occur. While it is possible to establish patterns using preventive maintenance, you have to get luckier than you do when you have access to objective data.
Benefits of predictive maintenance
A maintenance plan of some kind is necessary to make sure any equipment in your business – including building infrastructure like HVAC systems – continues to function and doesn’t become a threat to workplace health and safety. But why should you opt for predictive maintenance over any of the other types of maintenance?
Lower maintenance costs
Right away, the most obvious benefit of predictive maintenance over other types of maintenance is the cost. Because you aren’t attempting to constantly inspect and repair every piece of machinery in your workplace, you spend less money on repairs. At the same time, you aren’t sacrificing quality, since the areas you’re ignoring are functioning properly.
Predictive maintenance tools are also more precise about which aspect of your equipment is malfunctioning, so you won’t always need to take apart the entire thing in order to fix the problem. Less intensive repairs are less expensive and result in less equipment downtime. Finally, because predictive maintenance is AI-based, once the systems are installed, you save significantly on labor costs.
No more surprises
When performing other types of maintenance, even if they’re also proactive, discoveries take you by surprise. But since predictive maintenance processes and updates equipment condition in real-time, it can report things well before they turn into needed repairs.
Predictive maintenance tools can measure any data type to make sure observed values reflect normal operating conditions. That means you aren’t only aware that there is a problem, you’re aware of exactly what the problem is – maybe your machine is too hot or too cold or burns fuel too quickly or is moving erratically. Simply define the norm and let your system do the rest.
Longer equipment lifespan
Predictive maintenance is the surest way to keep your equipment around. Unlike predictive or planned maintenance, where repairs often require parts to be replaced, preventive maintenance preserves the original machine by regulating variables associated with machine performance. Keeping equipment intact prevents it from losing its value.
An AI-driven performance monitoring approach also measures the likelihood of future breakdowns, which helps you predict how long you’ll be able to use equipment in-between repairs. Accurate performance information helps you make data-driven decisions when it comes to scheduling repairs and replacements, leaving only the absolutely necessary amount of downtime.
It goes without saying that keeping your equipment functional helps you work more productively. Optimizing performance means tasks are done faster in the same timeframe, and fewer breakdowns mean fewer delays.
But another reason predictive maintenance helps you be more productive is that it keeps your workers safe. Malfunctioning machinery can easily lead to workplace injuries, which may leave employees unable to work. In fact, workplace hazards caused by faulty equipment are some of the most common safety risks.
Safe workers are more productive not only because they avoid injury, but because they’re generally more motivated. It makes sense, since committing yourself to workplace safety shows that you care about your employees’ well-being and inspires similar loyalty on their part.
The end goal and end result of all these benefits is higher revenue. Predictive maintenance increases profits in two ways: by generating more money and by spending less money. Knowing exactly what condition all your equipment is in helps you allocate time effectively and produce more.
As discussed, predictive maintenance also costs you less than other forms of equipment maintenance, since it’s much more specific in focus. Additionally, it’s not regularly scheduled like other types of proactive maintenance, so you don’t need to maintain as large of an inspection team.
Disadvantages of predictive maintenance
Considering all the reasons that a predictive maintenance strategy is good for your business, you might wonder what the obstacles are. The challenge with predictive maintenance is almost entirely implementation.
When you first implement predictive maintenance, you’ll need to spend time teaching and programming your systems. Predictive maintenance suceeds based on the amount of data you have, so you need to ensure your systems recieve relevant and accurate information. If data is missing or entered incorrectly, these systems won’t be nearly as useful.
Setting up predictive maintenance tools requires specialized knowledge regarding data science, analytics, and machine learning. If there’s no one at your company who has this knowledge, then you’ll need to bring in outside experts.
The reason predictive maintenance is so specialized is the sheer amount of data and variables involved, which only increase the more complex your equipment is. Implementation processes can easily take several months and sometimes even years, especially if you’re bringing in external consultants. That means adopting a predictive maintenance strategy, while cost-effective in the long-term, can be cost-prohibitive in the short-term.
Predictive maintenance techniques
If there’s a variable involved in operating your equipment (like how hot or cold it gets, the speed of moving parts, etc.) you can use predictive maintenance to monitor it. The factors you decide to measure depend on your priorities. That said, there are a number of variables that businesses commonly use predictive maintainence to regulate.
Vibration analyses are important because if a machine is vibrating more than expected, this can be a sign of underlying problems including:
- Mass unbalance
- Bearing wear
- Structural issues
- Shaft misalignment
Differences in vibration are caused by equipment wear, deterioriating equipment components, or both. Certain vibration levels (which are different depending on the equipment or component in question) indicate potential equipment failure, so registering changes early is vital.
First, establish the normal vibration level of each component that needs measuring. Once you’ve provided this data, your vibration sensors can alert you when they detect atypical behavior. You can even map certain vibration patterns to certain outcomes, so that you know what defect is being indicated.
Temperature is another near-universal variable in every equipment maintenance process. Preventing components from overheating is critical in performance monitoring, since excessive heat makes machinery unsafe to operate. If equipment is too hot for too long, it’s going to break down, because overheating is usually caused by faulty circuitry or overburdened fuses.
Temperature is measured via thermography, which is an AI-generated map or image showing infrared radiation in different areas of your equipment. Using thermal sensors ensures you can always maintain or replace problematic components before an incident occurs.
Ultrasound analysis measures sound waves by increasing their frequency to a point where they can be detected by humans. It does this by measuring the friction levels produced by rotating equipment, and then converting these into low-frequency sounds people can hear.
Ultrasound analysis is usually used to find leaks during gas transportation, electrical current leaks, and mechanical defects. It’s often the first technique to register an issue, since it only has to measure decibel levels. Since it’s much simpler, it’s not as complete a predictive maintenance tool as vibration analysis is, but it has more uses.
Only mechanical equipment vibrates, but leakage of various energy sources, like gas and electricity, produces ultrasound waves and can be detected with ultrasound sensors. This helps avoid wasting energy in your workplace.
While it’s true that many surface-level cracks are noticeable during manual inspections, automated crack analysis is more comprehensive. Sensors use magnetic particle analysis to scan for cracks both on and beneath equipment surfaces.
Crack analysis is used to check for:
- Cold joints
- Double lamination
- Lack of penetration
Oil analysis focuses on the lubricant used to keep your equipment running smoothly. Lubrication status is an important factor in machine performance, and it’s measured by assessing:
- Oil contaminants
- Machine wear
- Additive levels
Keeping track of these factors will tell you when it’s time for an oil replacement. Assuring consistent lubrication levels is vital, because insufficiently lubricated components can lead to:
- Gear failures
- Misaligned components
- Additive degradation
- Deterioration of components
Sample predictive maintenance process
Businesses implementing predictive maintenance tools typically proceed in three steps. Those are:
- Identifying the most critical equipment
- Tracking and analyzing failures
- Notifying the maintenance team
Identifying critical equipment
Critical equipment is equipment that would cost your business the most money or the greatest delay in production if it were to fail. Naturally, this is where you should start implementing predictive maintenance first. Measure the importance of equipment according to its role in your production process.
One way to quantify equipment importance is with an ABC analysis. ABC analysis asks three questions:
- How likely is equipment failure?
- How difficult are failures to detect?
- What would the impact of equipment failure be?
This is called an ABC analysis because after you answer these questions, you’ll classify items as high criticality (A), medium criticality (B), or low criticality (C). A and B-level equipment should be the first pieces of machinery you analyze with predictive maintenance.
Track and analyze failures
Once you’ve decided where to start with predictive maintenance, it’s time to move to performance monitoring. Your goal here is to figure out the root causes of issues and the corresponding variables. Tracking equipment failures means settling on appropriate predictive maintenance tools for measuring key variables.
You can measure chosen variables using online or offline predictive maintenance. Online predictive maintenance is AI-driven and automatic once set up, so all you need to do is respond when your sensors find a problem. By contrast, offline predictive maintenance is more like other forms of equipment maintenance. Despite using all the same techniques, inspections are conducted manually, which means they’ll need to be regularly scheduled to achieve the same consistency as offline predictive maintenance.
Notify the maintenance team
The final step is to inform maintenance personnel when issues are detected. Leverage the real-time status updates your predictive maintenance tools provide so that your maintenance team can address possible failures before they develop into breakdowns.
Developing and implementing predictive maintenance plans is easier with workflow automation software like Lumiform. With inspection templates for nearly every piece of equipment you’ll use in your business, and the ability to create custom checklists, it’s easy to establish patterns and identify common issues with your machinery. Automatically report any defects you find via the convenient mobile app so that they can be addressed quickly.
Documentation and data analysis are automatic with the creation of weekly and monthly reports based on your maintenance inspections. Use these analyses to communicate with stakeholders and better utilize vulnerable equipment.
Predictive maintenance is a proactive maintenance strategy that uses data, sensors, and advanced analytics to predict when equipment or machinery is likely to fail so that maintenance can be performed just in time to prevent the failure. Instead of following a fixed schedule or waiting for equipment to break down, predictive maintenance aims to optimize maintenance activities by identifying potential issues early.
Cost Savings: Predictive maintenance helps organizations optimize their maintenance activities. By identifying potential issues before they lead to equipment failure, companies can schedule maintenance activities when they are most cost-effective, minimizing downtime and reducing the need for emergency repairs.
Minimized Downtime: Anticipating equipment failures allows for proactive maintenance, reducing unplanned downtime. This is especially critical for industries where downtime can be extremely costly, such as manufacturing, energy, and transportation.
Extended Equipment Lifespan: By addressing issues before they escalate, predictive maintenance can contribute to extending the lifespan of equipment. This, in turn, helps organizations maximize their return on investment in expensive machinery and assets.
Improved Safety: Predictive maintenance can enhance safety by preventing equipment failures that could lead to accidents or injuries. Regular monitoring and timely maintenance reduce the risk of unexpected malfunctions that may compromise safety in industrial settings.
Condition Monitoring Sensors:
These sensors are placed on equipment to monitor parameters such as vibration, temperature, pressure, humidity, and oil quality. Examples include accelerometers, temperature sensors, and pressure transducers.
Internet of Things (IoT) platforms facilitate the collection, storage, and analysis of data from connected devices. Platforms like AWS IoT, Microsoft Azure IoT, and IBM Watson IoT can be used to manage and process data from sensors.
Data Storage and Databases:
Robust data storage solutions and databases are essential for storing and managing the large volumes of data generated by sensors. Examples include databases like MongoDB, InfluxDB, and time-series databases.
Data Analysis and Machine Learning Tools:
Tools for data analysis and machine learning are crucial for processing sensor data and making predictions. Popular tools include:Python libraries: Pandas, NumPy, SciPy
Machine learning frameworks: TensorFlow, PyTorch, Scikit-learn