Turning the dream of predictive maintenance into reality

MathWorks Australia

By Philipp H F Wallner*
Friday, 12 June, 2020



Turning the dream of predictive maintenance into reality

Predictive maintenance has been heralded as a solution to manufacturers’ and engineers’ woes thanks to its potential to give equipment users the ability to anticipate imminent malfunctions and failures that can have a disastrous impact on the whole business — so it is understandable why this technology is perceived to have huge value. According to Deloitte, predictive maintenance can reduce overall maintenance costs by between 5% and 10%.

But the key phrase here is ‘perceived value’. While predictive maintenance has the capability to be truly transformative, when the time comes to implement the technology on existing equipment the process is not always straightforward, and this fact is reflected in the low number of businesses that have implemented predictive maintenance in operation.

So why is the rate of adoption so slow? Industry commentators have highlighted four factors that seem to be the key stumbling blocks that need to be conquered by equipment builders and operators to realise the dream of successful predictive maintenance solutions.

Encouraging teamwork and knowledge sharing during the design process

It can be difficult for businesses to cultivate a collaborative environment where powerful algorithms for predictive maintenance based on statistical methods can be designed — and in such a way that they integrate the domain knowledge and expertise of both data scientists and domain experts. Furthermore, how can domain experts and data scientists work together to make sure that the key elements of each predictive maintenance application are fully leveraged?

The best predictive maintenance applications include both of these components: statistics-based data analytics methods (like machine learning) and domain expertise about equipment that the R&D engineers possess. If predictive maintenance is approached with a singular data analytics mindset, users will not capture all of the useful information retained by the operations and engineering teams that build the equipment and are responsible for their ongoing upkeep.

Determining how to train algorithms without access to sufficient failure data

Training an algorithm on data from the field is a fundamental part of machine learning. Those creating the algorithm must include ‘good’ data from everyday production on top of a variety of failure data taken from the numerous error scenarios that can happen while the equipment is operating. However, if the goal is to never allow the equipment to break in the first place, where can the failure data be obtained?

This is turning out to be an increasingly important problem to solve for businesses utilising predictive maintenance for their industrial systems. What’s more, it is irrespective of use cases and can range from air compressors to wind turbines. To overcome this issue, simulation models can be brought in to produce artificial failure data, so the algorithms have something to be trained on when there isn’t enough measured failure data from the factory floor.

Taking the algorithms from the design stage to real-world operation

After the training and design of the predictive maintenance algorithm has been carried out on the desktop, the next step is deployment onto the equipment. The difficulty level of this process directly correlates with the condition of the existing IT and OT infrastructure. Whereas some algorithms are applied on a real-time hardware platform, there will be some that are in the cloud or will be merged with the current non-real-time infrastructure (for example, an edge device). At a growing rate, businesses are taking the option of implementing an efficient way of using toolchains that facilitate automatic generation of C, C++ or IEC 61131-3 code, .NET components or standalone executables.

Proving the potential ROI of predictive maintenance solutions

When any organisation kicks off a predictive maintenance project, the most important question it has to be able to answer at the outset is, how can I prove the ROI of this investment? In the absence of an answer to this question, any plan to implement a predictive maintenance plan and solution will quickly run aground. Identifying a concrete business case and developing an approach for how to monetise predictive maintenance will prove vital when trying to persuade a management team to approve the investment.

The most obvious benefit will be the reduction in equipment failure during operation. While this often justifies the investment for operators, for equipment builders building a case is more difficult. However, there are a number of ideas that have been proposed that can contribute to building a solid business. Examples are:

  • Linking service fees to predictive maintenance of the equipment used by the operators.
  • Taking advantage of IP protection to sell the deployed predictive maintenance algorithm itself.
  • Moving to a new business model based on usage (for example, selling elevator usage hours rather than entire elevators, or cubic metres of compressed air rather than compressors).
     

It will be only a matter of time before the C-suite — armed with the information of these possibilities — jumps on board with realising predictive maintenance in all its glory.

*Philipp H F Wallner is industry manager, Industrial Automation & Machinery at MathWorks.

Image: ©stock.adobe.com/au/Myron Standret

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