Why move from condition monitoring to predictive maintenance? – Part 1
Has your organization adopted condition-based or predictive maintenance? The addition of these maintenance strategies 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). Yet, for many people, predictive maintenance is a new term. Many also confuse predictive maintenance with condition monitoring. In this 2-part blog series, I will break down the difference between the two, the origins of both, and what to expect next.
The main difference in predictive maintenance and condition monitoring is the timing. Both monitor the health and condition of a rotating asset like a pump, fan, compressor, mixer, agitator, or conveyor. But condition monitoring focuses on real-time conditions, while predictive maintenance has focused on the very early detection of defects 60 or 90 days in advance.
Condition Monitoring 1.0 – 1980s and earlier
Much like the red alerts on the dash of your car, legacy condition monitoring in an industrial environment has included lagging indicators like:
- Low lube oil pressure
- High temperature
- Irregular pump discharge pressure
- Low or high seal pressure
- Low or high seal pot level
An alert condition on these measurements means a failure has, or is already, taking place and timely response is required. In the predictive maintenance circle’s this is referred to as “condition-based-reactive maintenance.” The indicator, although useful, does not give enough time to plan. Production does not have enough time to plan and maintenance does not have enough time to line up the right parts, the right tools and the right skills.
Condition Monitoring 2.0 – 1990s – 2000s
A second wave of measurements have been adopted and have dramatically improved the detection of defects. Motor current, speed and power are the result of variable speed drives that have been deployed to improve efficiencies in electrical energy consumption. And additional vibration, and bearing, temperature measurements are more reachable. This is largely due to cost reductions, reliability improvement, IO systems infrastructure, and easy magnetic mounting of sensors. The second wave of measurements include:
- Motor current
- Overall vibration
- Bearing temperature
A variance in any one of these measurements can indicate a condition of the pump or pumping system that needs attention.
Using this second wave of alerts for diagnosis
Using these measurements has proved fruitful for diagnosing problems. However, setting the alert thresholds for use with automated alerting has proved challenging. The varying nature of the process, product recipe, or season has made nuisance alarms common. This has challenged the simplicity and clarity of the approach which then requires in-house or 3rd party expertise to realize success. Motor current, flow and pressure can vary with process conditions and require human analysis or some type of intelligence to identify a fault or anomaly in the normally varying measurements. For example, consider the ability to distinguish 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 is time consuming and has resulted in misses for pre-existing faults.
ISO 10186 helped improve problem identification
Overall vibration deployed with knowledge of the ISO 10186 alert standards has helped identify pre-existing conditions. Clarification of the failure modes detected by overall vibration have helped explain misses that have occurred. Failure modes detected by overall vibration include:
- Late stage bearing failure
Overall vibration is a direct measurement for detecting and monitoring imbalance, misalignment and looseness of a rotating asset. The units for overall vibration are inches per second – peak, which is a velocity measure. Today overall vibration is typically calculated from an acceleration reading measured using a $100 to $200 accelerometer. This measurement has been around for decades and an ISO standard, ISO 10816, defines how to measure and set alert thresholds. For example, the ISO 10816 standard calls for a 2-1000Hz 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.
Overall it is excellent at detecting the presence and severity of imbalance, misalignment and looseness. But many would argue it is not predictive. The 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 fix the problem while it is still small. Repairing the problem early will result in less cost than if you wait too long and fix the problem after it has caused additional collateral damage. For example, you wouldn’t want to wait until a pump shaft is broken when you could have just aligned the motor, the pump, and the inlet and outlet piping.
Condition Monitoring 3.0 – Predictive Maintenance – 2010s
IIoT measurements for predictive maintenance is much akin to the discussion around business ‘leading versus lagging’ indicators. The monitoring described above could be good but still lagging or condition-based.
For some, predictive maintenance is synonymous with technologies like:
- Infrared thermography (IR)
- Partial discharge testing
- Monthly vibration routes and analysis of vibration spectrum by trained and experienced professionals
With IIoT and its seven elements data structure, (see article January 2019 Pumps and Systems) more intelligent sensors, coupled with more intelligent processing, communication, storage, alerting and translating has emerged.
New failure modes targeted with predictive maintenance
The failure modes targeted by this new intelligence includes 60- and 90-day advance detection of:
- Lubrication defects
- Bearing defects
- Pump seal failure
Why do we care about these failure modes? Because multiple industry studies agree that lubrication is the root cause of failure on 50-80 percent of rotating assets. Past technologies of overall vibration, or bearing temperature, were simply too late or too difficult to establish meaningful alerts. Pump seal failures are being predicted based on the understanding that the common root cause of a pump seal failure is shaft deflection. A leading indicator of shaft deflection is the ultrasonic or high frequency vibration that is transferred through the bearing housing to a sensor.
Identifying defects up to 90 days in advance
In Condition Monitoring 3.0, overall vibration in combination with high frequency, or ultrasonic, provides the opportunity to realize “predictive” maintenance. This is where a fault condition is identified 60 or 90 days in advance allowing operations and maintenance to plan and schedule a repair. All done with the right parts, right tools, and right skills at the right time.
We are now entering Condition Monitoring 4.0
The capabilities presented here set the stage for the current state of condition monitoring and the introduction of artificial intelligence into the process. In an upcoming blog, I will be discussing this dramatic transformation that has allowed innovative organizations to move from simple identification of a malfunction to proactive correction of the underlying problem.
Learn more about predictive maintenance and asset performance management
To see how your organization stacks up and steps you can take towards predictive maintenance, take the APM assessment.
For more information, visit assetscan.com
Dan Yarmoluk can be reached at email@example.com
Craig Truempi can be reached at firstname.lastname@example.org
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