The Doppler Quarterly Fall 2017 - Page 9

Predictive Maintenance Any machine with moving parts will eventually break down and require service. If a pipe bursts or a con- veyor belt jams, production lines stop, and immediate repairs are required. These are examples of condition maintenance. Fortunately, many system parts have duty cycle ratings, and their replacements can be planned and scheduled. We refer to this as preventa- tive maintenance. It is fair to say that planned / scheduled downtime for preventative maintenance is preferable to unplanned downtime for condition maintenance repairs. Furthermore if a proactive replacement or repair can happen simultaneously with another condition repair, it is possible for field service teams to help avoid fur- ther unplanned downtime and larger, more costly repairs down the road. On the other hand, changing parts just for the sake of changing them on a predetermined schedule may actually be a waste of time and capital. A part may be rated at 10,000 hours but in practice have a life of 20,000 hours. Others may fail well ahead of schedule. So what’s to be done? Is it worth changing that part on a schedule, or should you wait? How expensive is that part? What’s the lead time for procuring a replacement? Do I need to keep one in inventory, just in case, and pay for it as an insurance policy? How do you win at this game, keeping your line running at top efficiency while managing the financial risk? These are difficult questions to answer, and they are the raison d’ etre for Predictive Maintenance, which seeks a data-driven, best-of-both-worlds approach to optimally keeping the line running at reasonable cost. Many systems and machines (although not all) have long been equipped with sensors that track telemetry or performance data. That data is typically raw and without context. Some companies (although not all) had the foresight to capture that raw data in a special database, called a data historian. More often than not, the data historian sits, in some cases for years, in an un- or underutilized fashion. Going big doesn’t mean everything goes to the cloud. FALL 2017 | THE DOPPLER | 7