The term predictive maintenance has been widely used in recent years although there is no agreed industry-wide definition. The result is that some in the industry may dismiss the concept as a buzzword, thereby missing out on an emerging technology that has the potential to be disruptive. In the broadest sense, there are two categories of technology that must be implemented to create a predictive maintenance solution.
One of these is the hardware. Increasingly, the key category of hardware in any predictive maintenance solution consists of smart sensors. These sensors are constantly growing in capability, and a likely future trend will include processing capacity embedded directly on sensors. Gathering data is the easy part. The second key technology for predictive maintenance is software and analytics to understand the data. Since most machine builders don’t possess the ability to write this software in-house, this stage usually requires a predictive maintenance specialist.
The benefits to predictive maintenance are pretty obvious: reduced downtime and increased machine lifetime. So why isn’t everybody doing it? If taken to its full potential predictive maintenance could radically extend the average lifetime of a machine. This will mean new business models are essential for OEMs to maintain revenue streams.
Adrian Lloyd and Blake Griffin from Interact Analysis joined the unPACKed with PMMI to discuss their research that led to PMMI Business Intelligence’s Predictive Maintenance White Paper. The pair examine how they see implementation of predictive maintenance technologies playing out in two different parts of the plant and how these trends will merge over time.
