How to See Your Machinery’s Future with Predictive Maintenance

Everything is running smoothly, until it doesn’t. Then there’s an unplanned disruption; a machine is serviced and then brought back online. Or, in the best-case scenario, everything is running fine and nothing goes wrong, but you must perform some regularly scheduled machine maintenance, halting production and causing a planned disruption.

Predictive maintenance is the middle ground between performing maintenance before it’s needed and after it’s too late. It prevents small problems from becoming costly production disruptions and times your maintenance precisely when it’s needed, and not before.

Identify developing problems before they occur

In technical terms, predictive maintenance is the systematic application of statistical and machine learning methods to uncover hidden patterns and structures from process and machine data, which is used and make forecasts on their condition. In simple terms, it’s using artificial intelligence to see your machines’ future.

Makers of machinery and equipment already generate significant revenues from maintenance contracts and service offerings, in addition to selling machines. Predictive maintenance makes their service operations much more efficient and reveal cost-saving potential. It also helps their customers benefit from higher availability and simplified service processes, boosting their satisfaction.

When operators of machines and production systems know of operating anomalies and upcoming maintenance or repair work, they can better plan personnel resources and spare parts requirements. Frequent, unplanned downtime is reduced and converted into shorter, planned downtime for maintenance activities. Unnecessary maintenance measures, such as routine, preventive replacement of components, are eliminated.

Applying data-driven predictive maintenance to your human processes will boost your equipment effectiveness, which, along with the financial benefits, will help your people do their jobs better.

Turning your data in predictive maintenance insights

Sensors integrated into machines supply data covering a wide range of information: about upcoming maintenance, wear and tear, malfunctions, and the performance of their customers' equipment. This is the byproduct of the industrial version of the Internet of Things. As time passes, this pool of data grows deeper and more useful.

To make it usable for your own predictive maintenance project, statistical methods are employed to detect recurring patterns from that breadth of data. These patterns are correlated with logged events, such as:

• Past defects
• Quality problems
• Plant settings
• Formulas
• Processed materials

We use standardized algorithms and analysis methods developed specifically for these purposes, but we have adapted, trained, and extended them to meet your unique needs. In practice, a targeted combination of several algorithms is often used to fit your business case and systems.

Fig. Standardized analysis methods (german only)

Good preparation and a structured project approach are crucial for the successful introduction of predictive maintenance. A key success factor is interactive and interdisciplinary collaboration between domain experts and data scientists, which we will cover in the next section.

The 3-step predictive process

Graphic procedure model (german only) 

1) Identify

Before you involve the algorithms, gather your domain experts discuss and prioritize concrete problems with our data scientists in a workshop. Focus on ensuring that the predictive maintenance concept is suitable for your environment.

For every problem you want to solve with AI, you must first understand how human intelligence would solve it. Only then can we create an AI that successfully solves the problem.

Together, sift through existing data sources and evaluate them both quantitatively and qualitatively to make sure you have the right data for your needs. Then, bring together all relevant data, and process and analyze it to reveal what the data says about machine behavior.

This step will give you a clear understanding of the use case as well as a complete, integrated data set.

2) Prototype

Now we move on to implementing the algorithm. For this, we use methods from signal processing, statistics, classical machine learning, and deep learning. Based on these methods and the information and insights from the “Identify” phase, our data scientists develop an algorithm that works best for your use case.

The algorithm developed specifically for the individual use case is the basis of a successful predictive maintenance solution.

This is the foundation of your predictive maintenance solution. It’s also the most difficult to describe in a blog post, because it’s tailored to the unique specifications of your environment and business case.

3) Operationalize

Now, we transfer the predictive maintenance solution to your system architecture or to the cloud. The algorithm is adapted to process continuous data flows. The relevant data sources are connected to the system via defined interfaces. A practical and user-friendly user interface makes the system easy to use and won’t scare away your users.

Now your predictive maintenance solution is available and ready to use.