Analysis Automation Bearings Condition Monitoring Equipment Motor Testing Motors & Drives Predictive Maintenance Reliability

Bearing PdM Increases Uptime

EP Editorial Staff | December 4, 2019

Vibration analysis alerted personnel to textbook fluting with this motor bearing, installed in a vertical turbine pump that supplies drinking water to a local municipality. The problem was discovered in time to avoid unpredicted downtime. Photo: Motion Industries Canada

Predictive-maintenance strategies will move your operation out of fire-fighting mode, improving overall system performance.

By Justin Lesley & Scott Moeller, Motion Industries Inc.

When we visit manufacturing operations, we see a mixture of reliability strategies for bearings ranging from run-to-failure, which is no strategy at all, to time-based preventive-maintenance routes. The lack of a reliability program leads to maintenance teams working reactively to correct problems after an asset has reached a critical performance level and caused a failure. With this fire-fighting approach, a frozen bearing on a 500-hp motor can cause extended equipment downtime, along with thousands of dollars of repair and replacement costs. Depending on length of downtime and product involved, a single frozen bearing can also cause many thousands of dollars in missed-opportunity costs from lost production time. Adding to all of this are safety concerns that escalate as workers scramble to get production up and running again.

In contrast, a preventive-maintenance (PM) approach keeps equipment in optimal running condition by following a regular maintenance schedule. Inspections and component replacements occur at a specific time. Maintenance crews perform extensive checks on specific assemblies and components during a scheduled outage and another set of tests during another maintenance routine. Inefficiencies are inherent with this process because of the need for increased staff or because of the unneeded replacement of parts.

While both methods can work, rising costs and the complexity of modern equipment have pushed companies to consider a predictive-maintenance (PdM) strategy. Because of the need for reliable operation and cost containment, PdM strategies that monitor key assets and provide early detection of bearing problems help managers plan for outages and have the required resources and components on hand, when needed. This approach maximizes the useful life of assets, eliminates unnecessary maintenance that occurs with PM strategies, and allows workers to perform maintenance in less-stressful conditions, thus increasing safety. This approach also plays a major role in significantly increasing overall operational reliability.

Predictive-maintenance systems include libraries of detailed information about thousands of bearings and assemblies, including vendor specifications, known failure causes, and the type of inspection technology used to identify a problem.

How Does PdM Work?

Predictive maintenance creates the intelligence needed to predict bearing failures that would otherwise lead to 70% to 85% of downtime for equipment. Rather than repair equipment after a failure or conduct tests because of a schedule, PdM compares actual operating conditions against a baseline consisting of average bearing operating conditions.

Many PdM systems include libraries of detailed information about thousands of bearings and assemblies and provide methods for finding the brands/models used in a particular plant. The component information housed within a library includes vendor specifications, known failure causes, and the type of inspection technology used to identify a problem. The historical trends for the components become part of the comparative analysis of actual and average conditions.

Sensors and transducers attached to the x- and y-axes of bearings compare changes in vibration frequencies that occur during real-time operation against the baseline operating conditions. Intelligent sensors can record real-time vibration data, establish baseline information, and create alarm thresholds. If vibration exceeds set limits, notifications inform the reliability team that a bearing failure is likely in the near future.

Advanced PdM solutions use machine-learning algorithms to determine the cause of increased vibration by interpreting raw vibration data. Some systems go beyond simple notification and prescribe potential resolutions.

Predictive strategies indicate a bearing problem far enough in advance to allow maintenance personnel to plan for repair or replacement rather than react to a failure.

PdM Tools

When working with bearings, it’s understood that certain operating conditions produce common types of failures. For example, poor lubrication of rolling elements can cause different types of bearing damage, such as spalling, while vibration can result in fretting or damage to retainers. Given the range of failures and causes that exist, predictive maintenance relies on a variety of analysis tools.

Vibration monitoring and measurement is the classic starting point for bearing PdM strategies. Using a motor operating at a constant rotational velocity as an example, the rotational speed of the inner race establishes the angular velocity of the bearing elements. When a fault appears on the external race, movement of a ball past the fault causes an impact that, in turn, generates a mechanical vibration peak and a unique, identifiable frequency.

Vibration monitoring detects the higher frequencies emitted by a defective bearing through sensors located close to the bearing. When considered as part of a trend, the frequencies indicate a deteriorating condition. Spalling, for example, generates a narrow-band frequency spectrum that features an increase in the amplitude of defect frequencies and sidebands. The analysis considers the vibration level as a function of time, compares the vibration with known levels of vibration, indicates a change in condition, and predicts the best time to take the equipment down for repair.

Frequency analysis identifies bearing components. Every cause of bearing failure—ranging from lubrication problems, contamination, and misalignment to poor fit and temperature—produces an increase in bearing vibration. Each element within a bearing has an individual characteristic frequency. While vibration monitoring is one method for identifying bearing faults, frequency analysis increases the opportunity to detect faults at an earlier stage.

High-frequency resonance analysis cuts through machine noise that can mask vibrations that indicate bearing failure. Envelope analysis goes further than vibration or frequency analysis by extracting periodic impacts of random noise from a rolling bearing. Using historical data that shows the natural frequencies of the outer and inner rings, envelope analysis looks for the high-frequency burst that occurs when the bearing hits a fault and then isolates that burst in time. Each impact of a bearing on the fault produces another burst of energy. The use of high-pass and low-pass filters generates an envelope of the signal that aligns with the repetition rate of the defect.

Benefit from PdM

Because PdM solutions continuously monitor the condition of equipment as it operates, reliability teams gain the time needed to order parts and can arrange for a repair or replacement during a time that has the least impact on operation. PdM strategies detect degradation before the failure occurs and provide the trending information needed to systematically plan for a repair. With maintenance occurring only when needed, and well before any breakdown happens, significant gains in efficiency can be realized. EP

Justin Lesley is Industry 4.0 innovation manager at Motion Industries Inc., Birmingham, AL (motionindustries.com). Scott Moeller is product sales manager of bearings, seals, and accessories at Motion Industries. For more information, visit MotionIndustries.com/efficientplant or bearings.com.

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