Predictive Maintenance

button-pdf

Predictive Maintenance: Stay Ahead of Machine Failure


Summary

Mosaic Data Science is proud to announce an Internet of Things (IoT) solution in the Predictive Maintenance (PdM) space. Mosaic’s predictive real-time capabilities allow for businesses to start predicting mechanical degradation and failures in time to do something about it – stage inventory, schedule maintenance, and deploy field engineers.

Current maintenance paradigms cover two primary types of maintenance:

  • Preventive maintenance is typically performed according to a fixed schedule.
  • Reactive maintenance is performed after a failure or drop in performance is observed. This unscheduled maintenance translates to higher maintenance costs, greater downtime and opportunity cost, and increased customer dissatisfaction.

Fortunately, as the cost of sensors decreased and companies preformed Value of Information (VoI) analyses, adoption increased, leading to a wealth of real-time data access.

Mosaic brings the skillsets necessary to get insights from this perishable, high-velocity data. Our data scientists apply predictive analytics to historical and current machine data, giving users the automated decision support tools and understanding needed to minimize mechanical downtime, reduce the frequency of scheduled maintenance, optimize field technician dispatch, and maintain very high service levels. For every effort, we work with the customer to develop return on investment models to estimate, then validate the impact to the business.

Benefits

As an organization begins to build out a predictive IoT capability, they can begin to take advantage of some of the monetary benefits that this solution offers. If a company can predict when their mechanical assets are going to fail, they can optimize their maintenance response with scheduled as opposed to unscheduled maintenance. Organizations can now ensure increased equipment availability, effectiveness and run time predictability through understanding historical, current and predicted part availability and replenishment requirements. With greater visibility into equipment health, companies can extend preventive maintenance cycles. Users can start monitoring equipment performance leveraging real time sensor data to accurately predict run time failures and take action to prevent them, as well as identifying maintenance needs that cannot be completed during planned downtime periods. IoT diagnostics shorten downtime by ensuring that the right skills, parts, and tools are part of the maintenance response the first time around.

This solution also offers the ability to handle large volumes of data and turn big data into an opportunity to save money, improve customer service and differentiate product offerings for customers from competitors without similar capabilities. Early adopters will be able to interact more optimally with service providers and receive process data in real-time to quickly respond to changing business and customer needs. Technicians will be provided with greater insight, allowing them to be better prepared for the opportunities to delight their customers versus disappointing them.

Details

Mosaic will partner with you to understand your operational and IT environments and capability to move forward, catalog your data and technology assets. Once we have a good idea of what data you are collecting, what data you will need, and where it is being stored, Mosaic begins iteratively developing and implementing predictive models to identify mechanical failure and other necessary metrics and variables for prediction. Once the models and infrastructure are in place, we can integrate the model predictions into a production grade decision support system. Mosaic’s experience in aggregating, summarizing and analyzing multiple data sources gives us an edge over the competition. Help bring dollar savings to the bottom line and allow your maintenance team become more data driven. Contact Mosaic today