Key points
The digital revolution
In the past decade, there has been a real shift and change in attitudes across all industries when it comes to inspection and maintenance. Traditional intervention was periodic, and monitoring was the physical task of manually reading gauges with reliance on the experience of personnel to interpret warning signs that could indicate the deterioration of an asset prior to failure. Now, operators are focussed on finding digital solutions to optimise processes and move from preventative to predictive maintenance.
Manual gauges have been replaced with electrical transducers, which are quickly becoming wireless in more and more applications, to simplify installation and significantly reduce the cost associated with running cable and conduit.
Original equipment manufacturers (OEM) are increasingly looking to add value to their traditional mechanical product lines by including instrumentation packages and processing algorithms, in order to add value to their product offering in line with industry trends.
The automotive industry has led the way in demonstrating the value of data in supporting asset monitoring and generating efficiency. Historically, a car would have likely had a fuel gauge and oil light; these days your car can self-diagnose what’s broken and order you a coffee while it directs you to the nearest garage.
The pit falls
While there have been many successful implementations of digital solutions for supporting process optimisation and asset maintenance, the benefits of these methods have been overlooked by many installations. This can be attributed to poorly defining the requirements, quality of implementation or the lack of action based on the results.
Knee jerk
One of the common problems is the knee jerk reaction to industry trends. ‘We need to have a digital solution; we need to be doing predictive maintenance’. Companies rush to implement ‘A’ solution, without fully considering the problem they are trying to solve and, more importantly, why they are trying to solve it. In other words, who cares? As a result, the wrong solution is developed.
Bean counting
Another challenge is that the engineers design an appropriate solution, but the bottom line is deemed too high for investment without considering the potential return on investment.
As such, a compromise is made to reduce cost, which can lead to a good solution becoming unusable as critical features and infrastructure have been removed that add the value. In the end, the implementation becomes a ‘tick box’ exercise that adds no value and is essentially a waste of resource.
Ignoring the outputs
Changing behaviours and processes is possibly the largest hurdle to overcome. An appropriate solution has been designed and implemented, but operations continue as is – ‘because that’s how we do it’.
It can be compared to going to a professional to improve your golf swing, being instructed how to do it, but ignoring them and continuing as before. You now know what you should do, don’t see any improvement, and are now lighter in the pocket for the effort. Unless you are willing to action the feedback, you will never realise the benefits.
Starting with the why!
When starting a project there is always an engineering tendency to start with the what or the how. A technical solution is well underway on the basis ‘because we can’ rather than asking the important question why?
For example, the purpose is not to develop a predictive maintenance suite, the purpose is to increase profit or improve safety. How is this achieved? By preventing failures in the field, removing unwanted downtime, reducing maintenance cost, extending the working life of asset.
How are those objectives to be achieved? In addition to addressing a technical solution, i.e., the data collection and analytics, the workflow must be considered.
What are the mechanisms to generate actions based on the output? Taking the holistic approach will ensure the true value of the solution and, therefore, the return on investment.
What are the key parameters that should be monitored? These are defined, for example, by the failure mechanisms of the asset. Understanding the value proposition will enable the project team to determine what is the acceptable value for the system. For a non-critical, low value item that can be quickly replaced, it is not worth investing five times the value in a monitoring suite, as you will never see a return.
Where the impact of an asset being out of commission has a more significant impact, choices must be made on what is the most appropriate and optimal solution to ensure a high return. From an operator’s standpoint, this return is a gain in uptime and operational efficiency which improves the profitability of the business. From an OEM or service providers’ perspective, the insight enables quality of service and the perceived value to their customer. Knowing when spare parts are required supports supply chain management, whereas operational performance of a product allows design enhancements to improve the reliability of product.
Creating the optimal design
To create an optimal design solution, knowing what instrumentation and measurements are available is vital.
Understanding the difference between inferred measurement and direct measurement in achieving the accuracy of output that is desired, as well as balancing the cost implication between permanent monitoring and temporary inspection, which are impacted by the location and accessibility of the asset, are equally crucial.
Several available systems are based on traditional measurements, such as pressure, temperature, flow, and vibration. Asset condition can then be inferred based on variation in these parameters. With advances in signal processing and data analysis models, these techniques are becoming more sophisticated and, therefore, accurate.
However, there are also advances in sensor technology that are enabling direct measurement of key parameters such as wear, lubricant properties, bulk stress, and interfacial contact. These measurement parameters provide insight that has not been achievable to date using traditional methods, providing a true enabler towards accurate predictive maintenance.
Summary
To implement a successful solution that will provide benefit throughout the value chain, a clear understanding of why you are doing it in the first place has to be identified. If you do not have a clear value proposition, then you will not be able to define a clear set of requirements for the system.
When implementing any form of asset monitoring solution, you must understand the failure mechanism occurring. Only then can you identify an appropriate way to determine changes within the system that will eventually lead to failure. You must also consider how to quantify the level of change and/or the extent of degradation to predict when the asset is or will be at risk of failure. Relying on inferred measurement can lead to misinterpretation of data, causing important condition information to be missed or generate disruptive false positives and unnecessary alarms.
Something is better than nothing is not always right. A solution that provides false data or misses key signs can add more risk to operations than having nothing.
Finally, processes have to be implemented ensuring information from the system is actionable so that it can impact business operations in a positive way.
