Predictive maintenance is nothing new. Also known as condition-based maintenance, predictive maintenance has been utilized in the industrial world since the 1990s. Although its history is not documented, the start of predictive maintenance may have been when a mechanic first put his ear to the handle of a screwdriver, and pronounced that it sounded like a bearing was going bad.

As with all scientific advancements, however, predictive maintenance methods evolved. Today, it is possible to predict when you need maintenance on existing equipment by leveraging industrial artificial intelligence solutions. Throughout our experience with industrial partners, we define predictive maintenance as the anticipation of critical events based on data that is captured and collected in the process. With our customers, we use methods like feature engineering to extract the most essential information from data and machine learning to understand, and generalize patterns that indicate critical events.

In a nutshell, predictive maintenance, will provide:

• The right lifetime analytics of industrial equipment

• Streamlined spare parts handling

• Greater plant safety and fewer human errors

• Fewer complications with negative environmental impact

But in order to achieve these outcomes, the decision on the right predictive maintenance solution lies in the customer’s hands. In the following paragraphs, we have tried to depict important considerations for deciding between off-the shelf, standardized solutions and customized, tailored predictive maintenance solutions.

Off-The-Shelf Solution for Predictive Maintenance

These types of solutions can work great for use cases where data is already collected and represented in a standardized way. This makes the integration and use of the provided service much more feasible, because the products and algorithms used for predictive maintenance are typically very opinionated about the data. As a matter of fact, many off-the-shelf services offer dedicated software tools to connect sensors to their platform in order to streamline this critical part of the process. Additionally, companies that can turn to cloud infrastructure are best suited for off-the-shelf solutions.

Industrial companies usually gather high volumes of data from a wide range of sensors across their facilities. In general, these types of solutions are recommended for problems that can be isolated to one or a few sensors, with no or only little interference with the remaining data. This also means that the problem needs to be well-defined in the first place. Ideally, domain knowledge will point to the relevant actuators, which can then be processed and configured as inputs to the available predictive maintenance model.

The available methods range from simple rules or statistics to more complex machine learning methods. Automated Machine Learning (Auto ML) frameworks promise an easy solution which requires no technical expertise in the matter. However, in our experience, these offerings usually fall short for industrial problems and due to lack of transparency.

There is also a vast difference in understanding the complexity of predictive maintenance for the sensors of one single machine versus use cases that should cover all machines and sensors of a production line or the whole facility.

Unfortunately, in more complex processing facilities, with a multitude of different sensors, complicated workflows and intricate data structures and representations, an off-the-shelf solution is unlikely to suffice.

Machine suppliers could benefit from building a customized solution for their machines and then offering it as an off-the-shelf predictive maintenance service to their customers, together with their product. This would be an added-value alongside their machines, where the end-customer would have the opportunity to predict future maintenance of their machines. It’s a win-win situation, where the machine supplier would gain a loyal customer base and an outstanding product portfolio, while the end-customer would avoid servicing issues and costs.

Customized Solutions for Predictive Maintenance

The custom approach entails the implementation and adaptation of proven data processing and machine learning methodologies to the specific problem at hand. It is best suited when the facility does not have a standardized process for data collection, storage, representation, and processing. Especially in a use case where we are dealing with complex systems, the process flow needs to be reflected in the design of the machine learning architecture.

At the inception of any predictive maintenance project, we do extensive data analysis, and we prepare the available data infrastructure to make it AI-ready.

We partner up with our customers to create a machine learning algorithm that answers these questions:

• How can this particular business problem be framed into a machine learning problem?

• How much prior knowledge is there about the causes and symptoms that lead to machine failure?

• How can we discover the ground root of the issue?

  • Is there a way to define historic machine failures?

  • Who in the organization defines and registers it?

Often, at the intersection of a failure occurrence and its data entry, there is a lack of information.

• How and where can this data be accessed and processed?

• How can the available data be cleared from corrupt, ambiguous or redundant information?

In real life, planned and unplanned downtimes might not always be explicitly labeled in the data and can result in large spikes and outliers which bias the dataset. Typically, breakdowns or failures are rare, but machine learning algorithms are biased to pick up on frequent patterns and disturbances like gaps, drifts and spikes. Therefore, a customized predictive maintenance solution is adapted to account for these issues.

The more customized a predictive model is to your unique organizational complexities, the more “healthy” and impactful its outcomes will be. However, it necessitates the availability of highly skilled mechanics, processors, and data scientists dedicated to creating validated models. This can truly pay off for your operational and resource efficiency. Although a customized approach is often more pricey than standardized systems, the end result makes it worth it.

Often, there is no such thing as a one-size-fits-all solution. In our opinion, any solution will have to be customized to some extent. It all depends on the current state of the IT infrastructure, the complexity of the problem at hand, the in-house know-how and capacity with regard to data engineering and data science. In the long run, going the custom route might cost you less time and money.

For instance, when mechanical engineers use a solution for predictive maintenance, they have to go through a learning curve, to identify the issue. Even if the same hardware is used, each implementation is unique to the equipment and its environment, and therefore needs to be adjusted accordingly.

Off-the-shelf IoT sensors may also not be best suited for component manufacturing because they are not sensitive or specific enough. For example, movements that cause excessive vibrations, potentially resulting in misalignment of small components, may not be discovered by off-the-shelf sensors. As such, customizing your predictive maintenance solution might cost you in the long run less time and money.

There is no “best” or “worst” way to go about it. Instead, it's about finding a way that works well for your specified budget, internal knowledge, and the actual outcome.

Your manufacturing company will benefit in many ways from predictive maintenance instead of preventive or reactive maintenance practices. Each factory has a variety of equipment, and you need each to function effectively for production to go smoothly with no downtime.

When you need to replace a part:

• It costs money.

• It takes a lot of time from your maintenance department.

• You might have to halt production while you replace the part.

With predictive maintenance:

• You’ll be warned ahead of time that a part might break.

• You can fix the problem while it's still small, easy, and cheap to fix.

Early condition-based actions keep minor issues from getting worse without you investing time and effort into it. When you receive an early monitoring alert, you can move the repair to the part to the top of the maintenance schedule. Predictive maintenance allows you to repair a component before it fails. This prevents frequent replacement of expensive parts.

Also, if you're unpleasantly surprised by a partial or complete failure, you may not have replacement parts on hand. As a result, you may need to halt production for a day or two until parts arrive. Your maintenance team is always busy with a schedule of urgent tasks. When a part fails unexpectedly, other duties are put on hold, which could cause a crisis elsewhere in the factory.

Predictive maintenance helps plants by lowering the cost of maintenance. When you use predictive maintenance and condition monitoring for parts, you can then:

• Know when a part is going to break

• Fix it in a way that makes it last longer

• Keep it from breaking down

• Have to replace it less often

A Better Environmental Impact

You are known for quality, right? Predictive maintenance can help maintain that public image by maintaining parts from breaking down when they shouldn't. On top of that, you might want to be known for your environmental practices as well. Predictive Maintenance has an impact on the environment. By preventing accidents such as:

• Oil Leakages

• Fires and explosions

• Pollution incidents

Saving on Time

Predictive maintenance saves time and money by making diagnosis maintenance more efficient. When upkeep is predictive, it ensures that each component is controlled when it is most likely needed. This is better than making your team follow a schedule that gives each part the same time. Did somebody say operational efficiency?

Greater Plant Safety

Predictive maintenance can reduce operational difficulties and boost process employee satisfaction and well-being. In addition, with predictive maintenance, you and your supervisors can get a better grip on your factory.

With predictive maintenance, you can avoid having to react to unforeseen problems such as:

• Sudden emergencies

• Extensive downtime

• Unstable and unpredictable work environments

Mitigating these issues with predictive maintenance will increase your revenue by reducing maintenance costs, expensive shutdowns, and overall downtime.

The rise in importance of customized predictive maintenance solutions for industrial companies, signals unprecedented scope for business growth. Innovation must continue to be part of the ongoing journey of creating innovative tools and applications that can be used in various industries.

Many people mistakenly assume that implementing a predictive maintenance strategy requires a complete restructuring of their work processes. In reality, however, just a few small measures can make a tremendous difference. The key starting point here is “data”. Implementing predictive maintenance requires three basic things: data, time, and analysis. Data is the most important ally in predicting events to increase machine reliability. However, extensive knowledge and a comprehensive data set is needed to identify when and why a piece of equipment is losing reliability. This is precisely why working with trusted data scientists that can customize a solution to make your particular data set work is the way forward. The way in which your predictive maintenance solution can pay tangible dividends.

So, are you looking for a customized and reliable solution for your business?

Why not talk to us about the particular requirements you need? We want to understand and focus on software usability. We use user-centered design to create a solution that is right for you.