How the ability to detect defects has evolved and continues to advance today.
by Craig Truempi & Dan Yarmoluk
March 18, 2019

Adding condition monitoring and predictive maintenance for legacy or aftermarket industrial pumps and equipment is touted today as a significant source of return on investment (ROI) for the industrial internet of things (IIoT). For some, predictive maintenance is a new term, one many confuse with condition monitoring.

The main difference in predictive maintenance and condition monitoring is the timing. While both monitor the health and condition of a rotating asset like a pump, fan, compressor, mixer, agitator, conveyor, etc., condition monitoring focuses on here-and-now conditions. Predictive maintenance focuses on the early detection of defects, 60 or 90 days before the defect causes collateral damage or impacts production. Here is information highlighting the history and transgression of condition monitoring.

Condition Monitoring 1.0—the 1980s & Earlier

Much like the red alerts on the dash of your car, legacy condition monitoring in industry has included lagging indicators such as:

  • low lube oil pressure
  • high temperature
  • low or high pump discharge pressure
  • low or high seal pressure
  • low or high seal pot level

An alert condition on these measurements means a failure has already or is currently taking place and a timely response is required. In predictive maintenance, this is referred to as “condition-based-reactive maintenance.” Although useful, the indicator does not give enough time to strategize. Production does not have enough time to plan and maintenance does not have enough time to line up the right parts or skills.

Condition Monitoring 2.0—the 1990s to 2000s

A second wave of measurements has been adopted and has dramatically improved the detection of defects. Motor current, speed and power are the results of variable speed drives that have been deployed to improve efficiencies in electrical energy consumption. Additional vibration and bearing temperature measurements are more reachable thanks to cost reductions, reliability improvement, input/ output systems infrastructure and easy magnetic or epoxy mounting of sensors. This second wave of measurements includes motor current, speed, power, overall vibration and bearing temperature.

A variance in any of these measurements can indicate a condition of the pump or pumping system that needs attention. Using these measurements has proved fruitful for diagnosing problems, but setting the alert thresholds for use with automated alerting has proved challenging.

The varying nature of the process, product recipe or season of the year has made nuisance alarms common and has challenged the simplicity and clarity of the approach, thus requiring in-house or third-party expertise to realize success. Motor current, flow and pressure can vary with process conditions and require human analysis to identify a fault or anomaly in the normally varying measurements.

One example is distinguishing a normal inrush current from an abnormal high current during steady state operations. Novice users have tried to baseline and set statistical alerts. Unfortunately with traditional methods and systems, this can be time consuming and has resulted in misses for pre-existing faults.

Overall vibration deployed with knowledge of the International Organization for Standardization (ISO) 10186 alert standards has helped identify pre-existing conditions. Clarification of the failure modes detected by overall vibration has helped explain misses. Failure modes detected by overall vibration include imbalance, misalignment, looseness and late-stage bearing failure.

Overall vibration is a direct measurement for detecting and monitoring imbalance, misalignment and looseness of rotating assets. The units for overall vibration are inches per second (ips)-peak, which is a velocity measure. Overall vibration is typically calculated from an acceleration reading measured using a $100 to $200 accelerometer. This ISO standard measurement has been around for decades.

ISO 10816 defines how to measure and set alert thresholds. For example, the ISO 10816 standard calls for a 2-1000 hertz (Hz) frequency range and recommends alert levels for typical machines at 0.2, 0.5 and 1.0 ips-peak for minor, warning and critical alert levels.

While overall vibration is excellent at detecting the presence and severity of imbalance, misalignment and looseness, many would argue it is not predictive and that overall vibration is a lagging indicator, as the problem or defect already exists. Yet, finding an imbalance or looseness defect when it is small has significant benefit if operations and maintenance have enough time to take the machine down to fix the problem while it is still small.

It is best to repair the problem early, when it is a small cost, in comparison to waiting too long and fixing the problem when it has caused additional collateral damage. One example is waiting until the pump shaft is broken, versus aligning the motor, pump, and inlet and outlet piping.

Condition Monitoring 3.0—Predictive Maintenance—2010s

IIoT measurements for predictive maintenance is much akin to the discussion around business key performance indicators (KPI)-leading versus lagging. The monitoring described earlier, although good, is still lagging or condition based.

For some, predictive maintenance is synonymous with technologies such as:

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