Monitoring Bearing Looseness on Dryer Cylinders to Prevent Shaft Damage

by | Articles, Bearings, Maintenance and Reliability, Predictive Maintenance

During routine condition monitoring, a case of looseness was identified between the inner ring of the bearing and the shaft on one of the dryer cylinders located on the drive side of the machine.

Due to the lack of a scheduled shutdown and no immediate availability from operations to stop the process, the decision was made to increase the data collection frequency and closely monitor the vibration behavior of the asset.

Spectral analysis confirmed the inner ring was rotating on the shaft, creating a clear risk of shaft damage and bearing seizure.

Over time, vibration levels gradually increased, eventually reaching 3.52 GE. The spectral analysis clearly indicated looseness and excessive friction, with consistent signs that the bearing’s inner ring was rotating on the shaft. This condition posed a risk of shaft surface damage and the potential for bearing seizure due to loss of fit and continuous friction.

Given this scenario, and with confirmation through spectral data, we formally recommended an urgent maintenance window to intervene. When the machine was eventually shut down for planned maintenance, disassembly confirmed the diagnosis: the inner ring had rotated on the shaft, leaving visible wear marks on both the ring and shaft surface.

Visible wear marks on both the bearing ring and shaft surface

Bearings

Bearing Race

Despite the damage, it was concluded that in a future opportunity, the shaft could be repaired via on-site machining. For the immediate return to operation, a new bearing was installed with reduced fit tolerances — adjusted to the minimum allowable limit — to ensure interference and prevent future relative motion between the ring and the shaft.

After machine startup, new measurements were taken, and the point continues to be monitored.

This case reinforces the critical role of vibration analysis in guiding maintenance decisions. Enabling early detection and accurate tracking of failure progression allowed timely intervention and helped prevent more severe consequences to the equipment.

Vibration Spectrum

Author

  • Felipe Shishito

    Felipe Shishito is a 28-year-old predictive analyst with nearly a decade of experience in vibration analysis. Starting at age 19, he developed a strong interest in anticipating failures in rotating equipment to improve operational reliability. With a background in mathematics and three years of engineering studies, he specializes in turning complex vibration data into precise diagnostics and actionable insights. His experience spans multiple industries, including pulp and paper, automotive, steel, food, and energy. Known for driving efficiency and reducing downtime, Felipe focuses on data-driven strategies that deliver measurable improvements in performance and reliability.

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