#predictive maintenance solution

Key considerations when using predictive maintenance tools:

Predictive maintenance tools have revolutionized the way companies operate and maintain their equipment. These tools use advanced data analytics and machine learning algorithms to predict when a piece of machinery is likely to fail, allowing for proactive maintenance and reducing unscheduled downtime. However, before implementing predictive maintenance there are some key considerations that must be taken into account.

Firstly, it's important to understand that predictive maintenance tools rely heavily on data accuracy. If the data used by these tools is not accurate or up-to-date, then the predictions made by the tool will be unreliable. It's essential to ensure that all data inputs are accurate and consistent across all systems. This may require investing in additional sensors or upgrading existing ones.

Secondly, it's crucial to have a clear understanding of what equipment needs monitoring and what data points need tracking.

Conclusion:

Predictive Maintenance Solution from Nanoprecise combines AI, IoT, and LTE-enabled monitoring, advanced sensors & prescriptive diagnostics to produce productive outcomes

What is Predictive Maintenance in Mining Sector

Waiting until a vital mining asset has broken down to fix it is no longer a prudent, strategic, or economically wise decision if you want to be ready for the next age of industrial innovation

Equipment breakdowns can be costly on several levels –

Downtime reduces output

Late-stage failures can be more costly to repair

In some cases, the asset has to be written off, and there are also wasted labour & energy costs