Unplanned downtime can be a killer for manufacturing costs. Predictive maintenance helps eliminate unplanned downtime by allowing manufacturers to be proactive and use data to guide their work. This article in IndustryWeek provides a good overview of predictive maintenance and how it can be transformative for manufacturers.
“Predictive maintenance brings a more data-driven approach to industrial-maintenance programs.
It uses predictive analytics and machine-learning algorithms based on historical and real-time data to identify specific issues on the horizon. Often these issues won’t be showing any physical signs of degradation – even a sharp human eye or an intuitive and well-trained maintenance technician wouldn’t be able to catch them.
Often, companies can invest a lot of time and resources in maintenance but lack data to know if their strategy is effective and addressing their actual needs. Predictive maintenance can help uncover unnecessary maintenance, which could save millions of dollars per year in some industries.”
The concept and benefits of predictive maintenance may make sense to most manufacturers, but they may be concerned that it will cost too much to implement, or they don’t know where to start. The article ends by providing a helpful overview of what it takes to implement predictive maintenance.
Predictive maintenance doesn’t require an extensive overhaul of your infrastructure. Rather, it can be deployed on your existing integrated control and information infrastructure.The process begins with discussions to identify what data you want to collect, what potential failures or other issues you want to predict, and what issues have arisen in the past. From there, the relevant historical data is collected from sensors, industrial assets and fault logs.
Predictive maintenance analytics software then examines this data to determine root causes and early-warning indicators from past downtime issues. Finally, the analytics software develops and deploys “agents” that monitor data traffic either locally or in the cloud.
Analytics software uses two types of agents. The first type is failure agents, which watch for patterns that are known to predict a future failure. If such patterns are detected, the agents alert plant personnel and deliver a prescribed solution. The second type is anomaly agents, which watch normal operating patterns and look for changes, such as operating or environmental condition changes. These agents also alert personnel of any detected changes so they can investigate and take corrective action if necessary.
To get a conversation about Predictive Maintenance started for your company, please contact Larry Carr at firstname.lastname@example.org.