Tackle the 5 drivers of motor failure with energy-based condition monitoring
Motors fail — it’s a fact of life that’s nearly as certain as death and taxes.
Though we commonly blame asset age for failure not all failures are age-related, some failed for “random” reasons unrelated to the age of the asset.
So why do motors fail? According to EASA and other motor experts, here are the top five reasons for motor failure :
1. Bearings: Causing 51% of motor failures, bearing issues are the most common cause of motor failures. Triggers for bearing failure include over- and under-lubrication, inappropriate mechanical loads, shaft currents, excess heat, and contamination.
2. Stator & windings: Winding issues cause 16% of motor failures. When windings fail, electricity arcs from one part of the motor to another, causing a short inside the motor. Potential causes of winding failure include over-voltage operation, excessive current, high ambient temperature or loss of cooling, an excessive number of motor starts or full reverses, physical damage, and penetration of contaminants.
3. External factors: Causing 16% of motor failures, external factors include environmental- and load-related failures, such as inappropriate mechanical loads, contamination, ineffective maintenance practices, and Acts of God or War.
4. Rotor: Rotor issues — broken rotor bars, broken end-rings, and core delamination — cause 5% of motor failures. Potential causes include excess vibration; an excessive number of starts or reverses, or too-short of an interval between starts and reverses; long starting times; physical damage to the rotor; and excess heat.
5. Shaft/Coupling: In 2% of motor failures, the shaft or coupling fails. Causes of shaft failure include improper installation, excessive loading, corrosion, and physical damage to the shaft.
In the remaining 10% of motor failures surveyed, the root cause was either not investigated or was listed as undeterminable.
How condition monitoring saves your motors
Now that we understand how motors fail, let’s discuss how we can use this information to preserve the health of your motors and motor-driven equipment.
Your motor’s health is inversely proportionate to the total level of stress it’s experiencing. Common motor health diagnostics measure the level of one stressor on your motors; for example, thermography measures thermal stress, while vibration detects mechanical stress. Much like your doctor tracks your temperature and blood pressure, monitoring your motor’s vital statistics — i.e., its normal operating parameters and stress levels — can indicate an issue long before it’s symptomatic of a problem.
Condition monitoring works by collecting, sorting, and analyzing streaming data from sensors on your equipment. Then, an asset performance management (APM) software applies complex algorithms to the incoming values to detect problematic conditions and update the virtual model — also known as its “digital twin” — of how your equipment operates. The software compares your equipment’s current performance to its manufacturer specifications and historical readings to identify performance “non-conformities” that require action. Finally, the APM generates a report with asset history and sensor data and alerts you to these non-conformities, enabling you to determine the proper intervention — such as correcting a potentially motor-damaging stress before it creates a bigger issue or scheduling downtime to replace equipment.
The advantages of energy-based condition monitoring
Energy-based condition monitoring offers three advantages over more common condition-monitoring techniques, like vibration and thermography. First, it’s difficult and expensive to monitor vibration, thermography, and ultrasound remotely so these tools tend to be interval based — e.g., quarterly or annually. With the Internet of Things and rapidly declining sub-metering costs, energy monitoring is an increasingly affordable method for providing continuous, remote monitoring.
Second, ultrasound, vibration, and thermography only identify whether your motor is operating normally or not. While these diagnostics are useful for troubleshooting an issue if there one exists, in the absence of an issue, they consider a motor healthy. But, energy-based condition monitoring can detect motor-damaging electrical stressors, such as voltage unbalance, before they damage your asset. With that knowledge, you have a chance to intervene and correct the issue before it harms your asset.
If damage has already occurred, then energy-based condition monitoring provides intelligence that helps you understand your motor’s current performance and health. With this information, you can make an educated decision that balances this motor’s performance, operating costs, and remediation costs with the risk and consequences of failure.
Third, energy-based condition monitoring uses energy efficiency as a leading indicator of motor failure. Whether a motor is just beginning to arc between windings or has a bearing issue emerging, the motor consumes more energy to generate the same output. That means its efficiency has declined. These slight efficiency changes signal that your motor is stressed. Because energy-based condition monitoring platforms continuously monitor your motors and compare new to historical measurements, these platforms detect that your motor needs attention — often before ultrasound, vibration, and thermography.
Advancing on the maturity curve
Whatever method you choose, the most important benefit of condition monitoring is that it helps you advance on the maintenance maturity curve [see figure, below]. Collecting and analyzing condition monitoring data support condition-based maintenance practices, where asset performance drives maintenance activities instead of interval-based schedules.
While proactive and not time-based, condition-based maintenance is still a preventive maintenance paradigm. Certainly, there are times where condition monitoring delivers predictive maintenance solutions. However, the full potential of condition-based maintenance will only be realized as we begin to analyze the reams of condition-monitoring data we’re now collecting.
As we collect and analyze condition monitoring data from different assets in various applications, new insights about what’s normal vs. abnormal will emerge. We’ll unlock knowledge about what affects the P-to-F time of assorted assets in distinct application classes. And with that knowledge, we will unlock previously unthinkable levels of optimization.
Nicole Dyess is director of client solutions at Motors@Work. She has nearly 20 years’ experience optimizing the performance of motor-driven systems. Nicole holds degrees in mechanical engineering and public administration. Motors@Work is a CFE Media content partner.