Revolutions are synonymous with disruptions. Industry 4.0 is nothing different. It demands new and advanced technologies for manufacturing plant maintenance and discarding obsolete plant maintenance processes at a much faster pace. It sometimes becomes overwhelming to understand and adopt new technologies as a plant head. So, here, we have an article to help you choose the right maintenance solution for your plant in 2022.
In this article, we’ll majorly talk about how you can choose the right Predictive Maintenance solution for your plant. But let’s start with the 3 basic types of industrial plant maintenance solutions available in the market.
7 most important questions to consider before choosing a Predictive Plant Maintenance solution
Predictive Plant Maintenance Solution comprises equipment and sensors, gateway, cloud service, and management to sense, record, and provide actionable insights on the machine’s condition. Artificial intelligence, machine learning, and IoT always try to yield accurate results.
But before you buy a predictive plant maintenance solution, consider these 7 critical aspects of it to decide which predictive maintenance solution is right for you.
Easy-to-Use and intuitive for everybody
The ideal Predictive Maintenance solution must be easy to use for all, from onsite plant operators and technicians to the plant manager & plant head. It should be intuitive and user-friendly to be mainly accessible to everyone required. If you need a data scientist every time to decode the insights provided by this software, then you are set up for sudden asset failures due to delayed responses. The right predictive plant maintenance solution can empower the onsite condition monitoring/ maintenance teams with the correct machine data at the right time for successful plant maintenance assessments with actionable insights.
Finding the root cause, not just alerts
Some Predictive Maintenance solutions indicate only alerts of anomalies, while the others yield insightful data alerts with what might be causing them. Those insights can be used to get a 360º condition of working equipment, and plant engineers can trace the root cause of the problems and plan a more effective solution. It also helps to distinguish the false alerts from the true ones.
For example: Just pointing out an issue with an exhaust fan of a kiln in a cement plant may lead to 1000 causes, but a solution that analyzes this further and points to a loose bearing that may be the cause can lead to a different level of agility for your maintenance teams.
Are the outcomes measurable or just hopeful?
Ensure that the maintenance technology brings you the results in some way or the other. And the results must be measurable and not hypothetical, which means you should be able to calculate the profits that the technology is bringing against its cost.
The average cost per hour of equipment downtime is $260,000. Look for a predictive maintenance solution that can save you the downtime cost and increase profits. Predictive maintenance can reduce machine downtime by 30%-50% and increase machine life by 20%-40%. (McKinsey)
Usable across assets and manufacturers
A plant usually has various types of equipment from multiple manufacturers and suppliers, depending upon the quality and cost. The Predictive Maintenance solution you are planning to install must easily integrate and comply with every piece of equipment in the plant- regardless of its age, type, and manufacturer.
Having different data collection mechanisms for different equipment is costly and leads to entropy & silos that obstruct the whole picture. Technology, along with human intelligence, functions to streamline complex processes and increase efficiency, and not the opposite.
Experience around process plants
Process manufacturing plants differ from other industries in various aspects. Predictive maintenance solutions request historical data to function reliably, but process plants have limited historical machine data, making it difficult for the predictive solution to function properly. Make sure your vendor has experience working with process plants to tackle the situation constructively.
Deployment & scaling time
One of the most popular hesitation in IoT-driven Plant maintenance deployments is the time taken to deploy the solution. If the deployment takes months, the internal enthusiasm built around the deployment dies down, and so does the ROI.
It is also essential that the deployment velocity is maintained when the solution is scaled up-whether from some machines to the entire plant or across plants.
Look for a predictive maintenance vendor that can integrate the solution in your plant and enable working within a few weeks and not months.
Predictive Maintenance in mining can cause many benefits – direct & indirect.
To Know more about How to choose a maintenance solution: https://www.infinite-uptime.com/how-to-choose-a-maintenance-solution-for-your-plant-in-2022/
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