The Role of Machine Learning in Predictive Maintenance for Manufacturing

Machine Learning and Artificial Intelligence have changed the way to go about our daily lives. We’ll hold off calling it the wheel of the modern era, but it certainly is making some major impact on almost every industry.

So, it should come as no shocker that they’ve infiltrated the world of Enterprise Asset Management (EAM) and Computerized Maintenance Management Systems (CMMS). And what for? To put it in a nutshell, reduce downtime.

The question then becomes, how does it do so? By facilitating predictive minutiae efforts. Let’s explore more about this in detail.

What is Predictive Maintenance?

Most industries, since the Industrial Revolution, ran their maintenance procedures by relying heavily upon two types of maintenance, namely reactive and preventive.

While all maintenance has its value in the hierarchy, for better understanding, we’ll put reactive maintenance at the bottom. It is used when equipment breaks down, and there is no other option but to fix it. Preventive maintenance is a step ahead in our hierarchy, where a set maintenance schedule is followed to prevent any unexpected breakdowns.

Back in the day, predictive maintenance was primarily used in the aviation and manufacturing industries, where equipment failures could have serious consequences. Unlike the other two types of maintenance we spoke of, predictive maintenance keeps an active eye on equipment performance and other parameters in real-time, so issues can be addressed long before they become major problems.

Take, for example, an industrial motor. Before this machine breaks down, it sends off many signals: high levels of vibration, spikes in temperature, and sudden noises. Sensors and IoT are used to measure these and take appropriate actions accordingly.

While the foundation for predictive maintenance algorithms may have been laid during the latter half of the 20th century, its widespread application and effectiveness are products of our modern times.

How Does Predictive Maintenance Work in Manufacturing?

Even when we talk about predictive maintenance in the manufacturing industry, the basic concept remains the same: anticipate equipment failures beforehand so there is time to intervene and fix the issue, avoiding unplanned downtime. Here’s how it works, step by step:

1. Data Collection

Without data, the entire system falls apart. Manufacturers equip their machinery with IoT devices and sensors to collect data in real-time. Based on the equipment, specific parameters are set. However, for reference, a few common ones are vibrations, temperature, pressure, and noise levels.

For example, in a food processing plant, temperature and pressure sensors on pasteurizers monitor for irregularities that could compromise product quality or machine performance.

2. Data Transmission

This collected data is then transmitted to a centralized system, like LLumin’s CMMS+ (Computerized Maintenance Management System), often facilitated by the Industrial Internet of Things (IIoT), which allows the equipment to communicate with central databases in real-time.

3. Data Processing and Storage

Once this data is transmitted, it is stored on the platform. Modern systems use the cloud to store data, but older systems still rely on good old hard disk space. This data is organized and managed within the program.

4. Analysis and Pattern Recognition

The program interprets and analyzes the data, looking for patterns and trends using threshold-based monitoring, where alerts are triggered if a specific parameter exceeds the predefined limit, such as temperature.

Another method involves machine learning models that use algorithms to analyze historical and real-time data to detect subtle anomalies that may cause failures.

5. Generating Alerts

When the system identifies an anomaly or predicts a potential failure, it generates alerts for maintenance teams. Alerts can include detailed diagnostics, such as:

• The specific part at risk
• The severity of the issue
• Recommended maintenance actions

6. Maintenance Scheduling

Based on these alerts, the maintenance team schedules repairs or replacements, whichever is required. This makes sure that maintenance is performed only when necessary. It also helps you reduce downtime with predictive maintenance analytics.

Here is how it all works together. Say there is an automotive manufacturing plant. For them, predictive maintenance would involve:

• Sensors are attached to the assembly line machinery to monitor vibrations or temperature
• Data is collected in real-time, with even subtle increases in parameters taken into account
• Based on this data, the system predicts an upcoming failure and sends out alerts
• The maintenance team takes action and replaces the part in question during scheduled downtime, preventing the entire production line from being shut down

With the help of predictive maintenance, our hypothetical automotive manufacturing plant has minimized downtime, made operations smoother, saved money, and performed maintenance on its equipment to prolong its lifespan.

How Machine Learning Powers Predictive Maintenance

Let’s talk about the biggest problem with predictive maintenance. What do you think it is? If your guess was the sheer amount and complexity of data, then ding ding! You got it right. The volume of data gathered is massive and becomes a herculean task to handle manually.

With the help of machine learning, all this information can be analyzed. It is capable of analyzing and finding patterns in terabytes of data collected by sensors, integrating with different data sources, all without human intervention.

Now, that was theoretical. Let’s talk about practical application. When the system powered by machine learning analyzes this data and finds anomalies, it starts to generate an alert. The maintenance team is alerted and reported about the risks, the severity, and the recommended action.

That is where our employees can take over. Remember, software is there to assist human employees. The maintenance team can now, with information about the existing issues, schedule repairs or replacements. This process makes sure that maintenance is performed only when necessary, reducing the wastage of resources like time and money.

And time is our friend here. With time, the more data you feed to machine learning, which it collects and analyzes, the more predictive maintenance systems refine their models. It is similar to having a full-time employee who gets better at their job as they gain experience and understand their work better and better.

Let’s take a feature of machine learning for predictive maintenance as an example. It’s called feature engineering, and it involves extracting meaningful insights from raw data. The goal here is to understand what parameters constitute an alert.

For example, a common machine used in manufacturing is a CNC (Computer Numerical Control) milling machine, which shapes materials like metal or plastic. For it, increased vibrations are a major red flag. So, spindle (the rotating part of a machine tool) vibration is monitored. An alert is sent when vibration levels exceed 10 mm/s RMS or when there are sudden spikes in amplitude.

Benefits of Using Machine Learning for Predictive Maintenance

Let’s shed some light on the direct impact on business. Does it provide any savings? How is the ROI? The advantage of predictive maintenance using machine learning is that it refines the process of reducing downtimes. Predictive maintenance doesn’t directly increase business because it merely manages resources better.

However, according to Forbes, downtime in the manufacturing industry costs businesses a huge $50 billion a year. Here’s how predictive maintenance can help you save that:

• Equipment Monitoring: Consider it 24×7 surveillance for your machines. It continuously monitors data from machines, looking for any anomalies and ensuring no issues go unnoticed. Immediate alerts are sent when anomalies are detected, allowing for quick responses.
• Increased Equipment Lifespan: Fixing a part when required means it gets the attention it needs exactly when needed. This keeps the machine performing at optimal operating conditions. Addressing issues early on ensures they last longer and operate as they should.
• Improves Safety: Every plant has a moral and legal obligation to protect its staff. With machine learning for predictive maintenance, equipment runs reliably, enhancing safety. This is especially crucial for high-stakes industries like oil and gas where ML-powered systems can detect early signs of pipeline corrosion, preventing dangerous leaks or explosions.
• Scalability: As your business grows, machine learning systems grow with it, as they can handle data from thousands of machines simultaneously.
• Resource Utilization: Knowing which machines need repairs or replacements and when is extremely helpful for better resource utilization. You don’t want to be fixing the spoiler when the engine is gone. Priority is given to equipment based on its relevance to overall operations.

For example, in a fleet, machine learning for predictive maintenance helps monitor engine performance. When the engine starts to cross certain thresholds, it alerts the fleet, indicating it’s time to visit the garage.

How to Implement Predictive Maintenance with Machine Learning

To understand the implementation better, we’ll divide the process into two stages:

Stage 1: Data Preparation and Model Training

The first stage focuses solely on gathering and providing all the data your predictive maintenance system would need. This would require collecting data from your equipment that is under monitoring. For example, sensors are attached to track things like temperature, vibration, or pressure. You’ll need sensors if your machines don’t already have them.

After gathering the data, the next step is cleaning it. This means filling gaps, removing inconsistencies, and converting everything into a usable format. Once it’s cleaned up, you’ll select a machine-learning model. You’ve got some options here:

• Regression Models predict when a machine is likely to fail by analyzing historical trends.
• Classification Models categorize machines as either “healthy” or “at-risk” based on real-time data.
• Anomaly Detection Models spot anything unusual in the data, such as a sudden spike in temperature that could signal an issue.

Once you’ve picked your model, the next step is training. The more data you feed the system, the faster it will learn to recognize patterns that indicate normal operation versus potential failures.

Stage 2: Deployment, Monitoring, and Scaling

Once your model is ready, it’s go-time. The first step is testing the model, so you’ll use a separate set of data (data the model hasn’t seen yet) to check its prediction capabilities. This model, here, is going through a testing phase to be checked for accuracy and reliability. If it passes the test, it’s time to integrate it into your daily operations.

It will now start receiving real-time data from your machines and continuously monitor them to predict any maintenance requirements. It will alert the team as and when required, providing them with information on which parameters were exceeded and which parts need to be fixed/replaced.

Taking this proactive approach (sounding like a broken record here), saves money on maintenance and time lost due to downtime.

As it collects more data, your machine-learning model should be continuously monitored and updated. Over time, the model will improve and refine its prediction capabilities as more data flows in. It’s best to schedule retraining for the model to keep it accurate. The system will scale up to handle additional data and update its model to manage various types of new equipment so all your machines are covered.

Challenges in Machine Learning-Driven Predictive Maintenance

No technique is perfect, and there are roadblocks in the way of predictive maintenance fueled by machine learning. Despite these issues, it is important to understand that machine learning has, for lack of a better word, evolved the way we perform predictive maintenance. But here are a few challenges to keep in mind:

• Data Quality and Availability: Predictive maintenance relies on a large amount of quality data. If you lack enough data, if it’s incomplete or unreliable, the predictions will be incorrect. 
• Sensors and Equipment: Not every machine has the necessary sensors to collect the right data. Older machines or equipment without modern sensors might not provide the data needed for effective predictions, so it is necessary to invest in them.
• Integration with Existing Systems: Manufacturing plants often use legacy systems, which are old and incompatible. This presents a challenge in terms of integration and a puzzle for IT to solve. Cases like this require a vendor and program that supports the integration process.
• Scalability: As operations scale up, so does the amount of data. The system must bear this load to keep working, which requires a large amount of storage and computational power. Solutions like LLumin’s CMMS+, a cloud-based platform, solve this problem.
• Initial Costs: As mentioned before, if you don’t have sensors attached to your machines, you’re going to have to invest in those. Along with that, for machine learning-driven predictive maintenance, you’ll also need to bear the cost of software and training. While the returns in the form of long-term savings are absolutely worth it, the initial cost can be a barrier to entry for many companies.
• System Updates and Model Training: As equipment and failure patterns change over time, the predictive maintenance system must be regularly updated and trained with new data. If the model isn’t retrained and periodically checked for accuracy, prediction quality can drop drastically.

These are the main obstacles that companies face when implementing machine learning for predictive maintenance. However, with knowledge of these challenges, preparing a plan of action to tackle them and achieve the desired results is indeed possible.

How LLumin Can Help with Predictive Maintenance

To combine the power of predictive maintenance and machine learning, you’ll need software capable of doing so. Enter our CMMS+ solution, LLumin. It takes the capabilities of a traditional system further than what was previously possible. Think CMMS, but on steroids.

By using it, you can uncover cutting-edge technologies shaping predictive maintenance for industries like manufacturing, health, and hospitality. Moreover, your company will be able to:

• Minimize downtime with the help of predictive and preventative maintenance
• Gain valuable AI-driven insights from detailed reports
• Have a centralized platform for information with easy-to-access dashboards and overall UI
• Automate various tasks like maintenance scheduling and receive instant alerts on mobile
• Collect data from all sources, as it integrates with older systems in place
• Scale up to business requirements, thanks to being a cloud-based platform

Our expert team provides training and rapid implementation to get you started as soon as possible. Schedule a demo today to leverage machine learning for predictive maintenance success.

Conclusion

It’s time to take a step ahead and use predictive maintenance powered by machine learning to keep your equipment running at peak performance for as long as its natural lifespan.

Days of maintaining equipment on a fixed schedule are behind us, save both money and time by maintaining equipment as and when required. Make sure you have a proper implementation plan in place before jumping in to avoid facing unwanted challenges in the process.

FAQs

What role does machine learning play in predictive maintenance?

With the help of machine learning maintenance, analyzing a large set of data from various machines becomes possible, predicting failures before they happen. So, instead of waiting for something to go wrong, machine learning models spot patterns in the data and can warn you ahead of time, allowing you to fix issues before they cause downtime or major damage.

How does AI improve data accuracy in CMMS systems?

AI analyzes and interprets a very large sample size of data collected in real-time, detecting anomalies, identifying trends, and then predicting failure. This is all backed up further by historical records. Moreover, it also eliminates the risk of human errors and biases.

Are there industries where AI-driven CMMS is most effective?

AI-driven CMMS programs are particularly effective in industries with heavy machinery and complex equipment like manufacturing, healthcare, hotel management, oil and gas, energy, and transportation. Generally, any industry that relies on expensive equipment would find AI-driven CMMS effective in helping them avoid unnecessary downtime and reduce costs.