It is clear predictive maintenance has huge value in the engineering and manufacturing industries, but this is no real surprise. In the era of Industry 4.0, real-time data analytics, automation, machine learning and other technologies are enabling teams to speed up operations and reduce costs.
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.
Powerful algorithms based on statistics 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.
In a competitive marketplace, engineers are under pressure to perform, but they are also resilient problem-solvers with a great deal of ingenuity by nature. 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 – ones that include both statistics-based data analytics methods such as machine learning in addition to engineering domain expertise.
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.
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. For example, international packaging and paper product manufacturer Mondi installed predictive maintenance software into its manufacturing line to reduce waste and machine downtime in its plastic web production.
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.
There are several other good suggestions proposed by some of our clients for creating a business case for engineers to consider, such as linking service fees to predictive maintenance of the equipment used by the operators (equipment builders’ customers).
Another idea is taking advantage of intellectual property protection to sell the deployed predictive maintenance algorithms themselves. An additional area for consideration is moving to a new business model based on system usage, for example, selling cubic metres of compressed air rather than compressors, or lift usage hours rather than whole lift systems.
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.