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Predictive Maintenance: Reduce Unplanned Downtime

Unplanned downtime is an unexpected shutdown or failure of equipment due to unforeseen failures. It can occur at any time, causing disruption to operations and leading to financial losses. 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. Deloitte estimates that poor maintenance strategies can reduce overall productivity by 5% to 20%.

To prevent it, a maintenance strategy is extremely vital in terms of efficiency and cost savings. GE Oils & Gas’s survey reveals that traditional maintenance approaches are less efficient than predictive ones. Because the predictive approach embraces disruptive technology innovations, it is more effective, leading to an average annual unplanned downtime of 5.24%. In comparison, a planned maintenance approach results in 7.96% and a reactive one in 8.43% unplanned downtime. 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. Internet-connected sensors enable constant equipment performance monitoring by attaching them to machines or equipment and allowing them to monitor machine health data in real time. A predictive maintenance platform 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. A functional predictive maintenance program can result in a tenfold increase in ROI. It can also lower maintenance costs by 25%-30% and reduce downtime by 35%-45%, according to the U.S. Department of Energy.

To set up a predictive maintenance program, businesses must have attached sensors, IT infrastructure, and trained personnel. Transforming and analyzing data from monitor sensors on a platform and implementing dashboards and communication systems are necessary for companies to notify users of advanced alerts. Additionally, companies require experts’ and data scientists’ knowledge to build and maintain a predictive maintenance program. Training personnel to operate and handle information precisely can be a huge obstacle for small and medium-sized businesses to overcome.

Condition-based monitoring and predictive analytics

NOVO AI offers a machine monitoring solution called AVa, which uses an I-IOT signal sensor. AVa can identify anomalies in critical machine parts and provide information on maintenance conditions. It can gather and transfer data to the cloud via Wi-Fi. 

The Watchmen platform processes and analyzes data in the cloud, and users view the resulting information 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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