The benefits of proactive and predictive maintenance are undeniable. There are several methods on how to apply either one. In this article, we use rotating machinery as an example. Most methods fail in accuracy, efficiency, or they are prohibitively too expensive from a modern perspective. Balancing cost, efficiency, and accuracy is sometimes hard at the time of purchase as there are several variables in play. The goal of maintenance is reliability and uptime, being the payback for all actions. The second benefit from monitoring next to the maintenance goals is understanding the use of the asset. How is the asset being used, contributing even more to the equation in the form of end-user satisfaction, training needs as well as feedback to R&D?
Many of the traditional solutions are based on the idea of an on-premise engineer with a relevant skillset to collect the data and to analyze the information. Having people with relevant skills is not given, though. And developing deep knowhow in e.g. vibration analysis is not as easy as one might think. Knowledge also moves along with the person, keeping it in-house is as demanding as retaining good people.
Another approach is to make something simple and low cost. The low-end approach is not a bad idea, as something is better than nothing. These systems can detect that something is wrong for sure, when something happens. The question many times is, what is the process that follows the analysis and detection of a potential fault? Further inspections, root cause analysis, ordering of spare parts, scheduling, shutdown, or a maintenance break? There is a huge benefit from having a lead time, to anticipate and react based on observation, a symptom. The cost of reactive vs. predictive is embedded in the process.
What if there was an option to reduce the cost of traditional monitoring methods significantly, reduce the need for on-premise experts, give lead time to react, and increase the accuracy in our analysis?
To achieve this, accurate measurement data needs to be collected from, e.g., IEPE/ICP vibration sensors. With continuous, statistically valid highly accurate measurements, the operations- and maintenance organization has a continuous pulse on the asset. In the case of rotating machines, as we might be looking at something with low, high, or variable speed, the vibration data can be supplemented by RPM sensors and other process measurements like temperature or pressure to build a more comprehensive view and to see how these values trend and correlate over time.
With this method, depending on the asset and use, the repair cost can be reduced on average by 30%. This is achieved simply by having the insight from the analysis and a heads-up to what repairs to do. This lead time and insight allow for correct and accurate planning, contributing to the many elements of cost in operations and maintenance processes. Moving from manual inspections to automatized monitoring will pay back the system in less than 12 months. The cost elements include less manual work, less field crew drive time, lead time to plan and react, and less downtime, just to name a few. On the other side of the equation, consistent data from assets to R&D for customer-centric design, longer life of assets, circular economy targets, and new business models, combined, can lead to payback much faster. The continuous, accurate data is also a perfect source for machine learning, AI and other applications.
Notably, with a 3-month inspection interval, the theoretical potential failure between inspections is 25%, when with continuous monitoring the corresponding figure is only 0,01% (as continuous monitoring gives close to real-time information). The most significant source of savings is to avoid downtime. In any case, the payback is less than 12 months with higher accuracy.
With the Condence solution, it is possible to use standard “commercial off-the-shelf” sensors, making the system flexible and fast to deploy. The solution is offered with IP67 graded casing to match installation- and environmental requirements in demanding industrial applications. Each device, smart terminal, can have up to 8 vibration sensors and a variety of other measurement points with no need for long cabling – it is easy and fast to install.
The communication uses a built-in GSM-modem while edge computing capabilities, embedded processing of data, allows for optimizing of data transfer volumes. Fully integrated from sensor to user interface, the system is efficient and easy to run, update and maintain.
The architecture results in savings on sensors, installation and data transfer, leading to a significant reduction in both investment and operational costs. Data and knowledge that before used to remain on the premise and often is tacit in nature, is now shared across the entire organization, resulting in both cost savings and new opportunities.
Janne-Pekka Karttunen, CEO