Predictive Maintenance - Avoiding Mechanical Failure With IIoT

An excellent predictive maintenance program has several benefits for businesses in all industries. A correctly implemented predictive maintenance program includes ten times greater RoI, 70% fewer breakdowns, 35-45% less downtime, and a 25-30% decrease in maintenance costs.

Despite these benefits, many businesses continue to use traditional methods. They're more affordable and more comfortable to implement. The issue is that they're not as effective at preventing mechanical failure. Predictive maintenance uses sensors to collect data. It runs the information through machine learning algorithms and combines it with IIoT connectivity. This sensor information flow allows businesses to create an effective repair schedule. It also increases customer satisfaction and reduces downtime and repairs.

Machine learning and IIoT technology are powerful tools on their own. They're even more powerful when combined with a proper predictive maintenance program.

Read on to learn more about each of these components. You'll discover how they work together to prevent mechanical failure.

What Is Predictive Maintenance?

Developing a proper predictive maintenance definition starts by comparing it to traditional methods.

Preventative maintenance includes any traditional system of equipment monitoring. Simple examples include traffic light health systems on individual machines. More complex systems use connected networks of sensors that send data to centralized dashboards.

Preventative maintenance uses several factors to determine machine health. They include manufacturer lifetime predictions, human operators, and direct sensor data. It remains popular because AI and machine learning are expensive and difficult to adopt. You may not realize what you're missing out on by not becoming an early adopter.

Predictive maintenance is driven by predictive analytics. It uses computer algorithms to determine when machines will fail. It's perfect for critical devices that have a high probability of imminent failure.

There are several benefits of predictive maintenance. It prevents expensive failures, reduces overstock, and increases customer satisfaction.

Drawbacks of predictive maintenance include the high level of expertise and increased costs. These problems have kept several businesses from adopting it in the past. The adoption rates should increase as the required technology grows. Over time, it will become more accessible, easier to use, and more affordable.

What Is the IIoT?

The IoT is also known as the internet of things. It includes connected devices that can be linked together, such as those in a smart home. It's currently a multi-trillion-dollar industry. The IIoT is also known as the Industrial Internet of Things. It includes hardware that uses IoT connectivity. The difference is that industries must use it to enhance manufacturing processes. It's a more specialized sector of the internet of things market. The IIoT market is worth approximately $200 billion. It continues to be one of the largest sectors of the IoT market.

What Is Machine Learning?

Machine learning is a form of AI or artificial intelligence. It refers to the process of "teaching" computers to handle new situations. Each approach is based on information that is already collected. Machines can quickly react to changes such as failing equipment.

There are several ways to implement machine learning. These techniques include:

• Rote learning
• Parameter adjustment
• Macro-operators
• Chunking
• Explanation-based learning
• Clustering
• Issue correction
• Case recording
• Multiple model management
• Backpropagation
• Reinforcement learning
• Genetic algorithms

A machine learning program can teach a computer almost anything. One of its most useful applications is predictive maintenance. It leverages past and future data to create models. They help businesses optimize maintenance operations and minimize downtime.

The Connection

Predictive maintenance, machine learning, and IIoT provide several benefits on their own. When combined, they create an effective system for preventing mechanical failure.

The IIoT predictive maintenance process involves several steps. Start by collecting data from potential failure points. Combine that with equipment metadata, usage history, and maintenance data.

Look for patterns that can help you identify potential mechanical failures. Machine learning algorithms can also create predictive models. They will allow you to see how the health of your machines will change in the future.

Let predictive maintenance, machine learning, and IIoT work together. Doing so provides several benefits, including:

• Increased accessibility
• Lower initial investments
• Increased RoI
• Larger scale
• Improved effectiveness
• Advanced notice of problems
• Savings of up to 40%
• Improved operational efficiency

These are only some of the benefits you may experience by developing a predictive maintenance program. Be sure that it uses the power of machine learning and IIoT connectivity.

Case Study of IIoT Predictive Maintenance

IIoT technology continues to grow. This expansion makes it easier for all businesses to implement predictive maintenance. An example of a digital transformation success story is the HiPerform III Plus control system that Solvative developed for the Paul Mueller Company.

The data collected helped create alerts and a preventative maintenance schedule, making it possible to fix mistakes or failing equipment before it became a significant issue. Read the full case study to see how Solvative helped bring the vision to life.

Paul Mueller Company improved its workflow with predictive maintenance. Use the right tools and create an effective program to get the same benefits for your organization.

Where to Get Help With Developing a Predictive Maintenance Program

Predictive maintenance is the best way to monitor each component of your business. It helps you prevent mechanical failures before they happen. It stands far above the traditional, human-led method of preventative maintenance.

Predictive maintenance requires IIoT connectivity and complex machine learning algorithms with the right set of data input sensors. The technological aspects necessary for this can make it difficult for businesses to implement. IIoT is making it more manageable.

A predictive maintenance system is a powerful tool for your business. It allows you to keep track of your equipment and maintain it before a serious issue arises, preventing downtime and reducing maintenance costs.

Solvative is a software company that takes the time to understand what clients need and how to help them. Contact us today to see what we can do for you.

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