Reduce Maintenance Costs with the IIoT and Predictive Asset Analytics
Friday December 2, 2016
Industrial organizations across multiple sectors are under pressure from shareholders, regulators and customers to increase performance and reliability while keeping costs low. These conditions mean it’s more important than ever for organizations to optimize maintenance and operations. While reactive and preventive maintenance continue to be a key part of a comprehensive maintenance program, more proactive condition-based and predictive strategies are required for critical assets.
The growth of the Industrial Internet of Things (IIoT) offers a huge business opportunity to leverage existing data to drive business results by adopting a proactive predictive maintenance strategy. The average plant has over 8,000 data points, and a utility may have more than two million points across their generation fleet. However, without context that data is simply noise, and operators are unable to use it to make informed decisions.
Predictive asset analytics, enabled by the IIoT, is key to reducing unscheduled downtime and maintenance costs. Using predictive analytics, customers are able to predict and diagnose problems before they occur. This shifts maintenance from a reactive process to a proactive strategy, moving the organization higher up the maintenance maturity pyramid.
When selecting a predictive analytics solution, there are some key points to consider. Hardware and software agnostic solutions are critical, as it drastically reduces implementation cost and time to value. Another important factor is ease of use, specifically in the model building process. Look for a solution with an intuitive model building capability that doesn’t require any programming or detailed equipment knowledge.
In addition, for customers who lack the resources to comprehensively monitor their own assets, some vendors provide turnkey remote asset monitoring as a service, including installation, system training, modeling, monitoring, and reporting of anomalies done by an offsite team of experts. This supplements the existing maintenance team, reduces the burden on the organization and is a great way to get started quickly.
