How can predictive maintenance help reduce unplanned downtime?

How can predictive maintenance help reduce unplanned downtime?

Unplanned downtime is an unexpected shutdown or failure of equipment or machines that can occur at any time as a result of unforeseen failures. According to a survey from ServiceMax in 2017, there are different failures, including hardware (46%), software malfunctions (40%), and operator mistakes (17%). Unplanned downtime can cause a huge disruption to manufacturing, which costs significant time and money, namely, repair costs and labor costs. It is an outcome of poor maintenance strategies, which is estimated by Deloitte to reduce overall productivity by 5% to 20%.

To prevent it, a maintenance strategy is extremely vital in terms of efficiency and cost savings. According to GE Oils & Gas’s survey of gas and oil manufacturers, traditional maintenance approaches are less efficient than a predictive approach, which embraces more disruptive innovations in technology. Predictive maintenance has an average annual unplanned downtime of 5.24%, compared to a planned maintenance approach of 7.96% and reactive maintenance of 8.43%. It helps companies decrease nearly 60% of their financial impact.

How does predictive maintenance work?

Predictive maintenance is a data-driven and technology-based approach that starts with collecting data from machines or equipment. This is achieved through constant equipment performance monitoring, enabled by internet-connected sensors. These IoT sensors are attached to machines or equipment. They monitor critical machine health data in real-time and communicate with a predictive maintenance platform that uses data analytics to identify potential issues. The platform can send technicians advanced warnings in real-time.

The initial investment for predictive maintenance creates some costs, but it is worth the future ROI. Based on data from the U.S. Department of Energy, a functional predictive maintenance program can generate a tenfold increase in ROI, lower 25%–30% in maintenance costs, and reduce 35%–45% in downtime.

Setting up a predictive maintenance program needs business-attached sensors and IT infrastructure, and trained personnel. Data from monitor sensors must be transformed and analyzed on a platform. Then dashboards and communication systems must be implemented in order to notify users of advanced alerts. Experts and data scientists’ knowledge is required to build and maintain a program. Personnel are required to be trained to operate and handle information precisely. These are huge obstacles for small and medium-sized businesses to overcome.

Condition-based monitoring and predictive analytics for predictive maintenance

With AVa, an I-IOT signal sensor, NOVO AI offers a machine monitoring solution that can identify anomalies in critical machine parts and make statements about maintenance conditions. It can gather and transfer data to the cloud via Wi-Fi. 

Through the Watchman platform, data is processed and analyzed in the cloud. The result information is viewed on the “Watchmen Dashboard”. The platform places the customer in the cockpit of the production process and allows them to access the real-time status of the machine.

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