Overcoming challenges to realising predictive maintenance for Industry 4.0

MathWorks Australia

By Philipp H F Wallner*, Industry Manager, Industrial Automation & Machinery, MathWorks
Thursday, 05 May, 2022


Overcoming challenges to realising predictive maintenance for Industry 4.0

Predictive maintenance offers the ability to anticipate equipment malfunctions before they occur, arrange repairs proactively, avoid stalling factory floor operations and most importantly, prevent the failure of machinery to keep businesses running efficiently. Figures from Deloitte show predictive maintenance can reduce the costs of maintenance by 5 to 10% overall.

However, very few businesses have actually implemented predictive maintenance so far, as the process of doing so with existing equipment isn’t without its difficulties. There are four major challenges engineers must overcome in order to work with data scientists and realise predictive maintenance capabilities amid Industry 4.0.

1. Cultivating a collaborative environment

To make the most of the benefits of predictive maintenance, it is necessary to create a collaborative environment in which domain experts in engineering and data scientists work together. If predictive maintenance is approached with a singular data analytics mindset, not all of the insights from the engineering teams that built the equipment and maintain it on an ongoing basis will be captured, and vice versa.

Powerful algorithms based on statistical methods that integrate the expertise and domain knowledge of engineers as well as data scientists are needed to ensure the key elements of each effective application are fully leveraged. With the right approach, it is possible for engineers to work together with data scientists effectively and realise the best predictive maintenance applications they can.

2. Training algorithms with not enough failure data

An important challenge for engineers implementing predictive maintenance to solve is how to train algorithms properly with failure data. Often engineering teams are easily able to include ‘success’ data from everyday production, but if the aim is to avoid it malfunctioning in the first place though, how can teams obtain failure data to train the algorithms?

The answer lies in simulation models, which can be used to produce artificial failure data. This data is irrespective of use cases and can range from wind turbines to air compressors. Using simulation to create failure data is a more efficient way to train AI than relying on the results of the factory floor which may not provide enough, or any, insight into failed mechanics at all.

3. Implementing algorithms in the real world

Once the algorithms have been fully trained on the desktop, the next challenge is deploying them into the industrial system equipment. How easy or difficult this task is depends on the condition of the existing IT and OT infrastructure. Some algorithms are applied onto real-time hardware platforms such as industrial PCs, embedded controllers or PLCs, while others are in the cloud or merged with current non-real-time infrastructure, for example an edge device running on Linux.

More and more, organisations are using toolchains to implement predictive maintenance in the real-world efficiently. These toolchains facilitate automatic generation of code, components or standalone executables.

4. Creating a business case for predictive maintenance based on data evidence

All the aforementioned challenges have available solutions, leaving one key problem — how to build a business case for predictive maintenance in the first place. Senior management will need to understand the return on investment that would be achieved before approving it, so detailing a comprehensive, data-driven plan is imperative.

To do this, engineers must develop an approach for how they will monetise predictive maintenance and calculate estimates on savings, such as on the reduction in equipment failure during operation.

Predictive maintenance is a vital part of engineering and manufacturing in Industry 4.0. By combining data science with engineering domain expertise, using simulation to create failure data, toolchains to deploy algorithms and a variety of techniques to build a solid business case, more engineers can implement this vital technology and start realising its value.

From reducing equipment downtime, to generating significant cost-savings, to boosting efficiency throughout the production line, the benefits of investing in predictive maintenance are too great to ignore.

*As industry manager for the industrial automation and machinery field at MathWorks, Philipp Wallner is responsible for driving the business development of this industry segment that comprises energy production, automation components and production machines. Prior to joining MathWorks, Philipp worked in the machine builder industry, where he held different engineering and management positions.

Image: ©stock.adobe.com/au/dusanpetkovic1

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