Peter Eitnier, Sr. Applications Engineer, Wilcoxon Sensing Technologies

Mattias Hoehnen, Technical Product Owner, KCF Technologies

Every minute of unplanned downtime for a paper machine represents lost revenue. Maintenance and reliability engineers monitor vibration to avoid costly repairs and breakdowns by detecting early warnings of faults such as imbalance, misalignment, looseness, and lubrication issues.

Below are the key environmental and monitoring considerations for the primary sections of a paper machine. You can download this as a PDF for future reference.

Forming / Press section

Production assets

• Fan pump
• Breast roll
• Couch roll
• Suction pick-up roll
• Table roll

Environment

• Caustic chemicals
• High moisture

Accelerometer considerations

• Wide bandwidth for accurate low and high frequency detection
• Hermetically sealed 316L stainless steel to resist corrosion, inhibit moisture ingress, protect internal electronics, and prevent premature sensor failure

Data processing considerations

• Continuous monitoring to detect transient conditions
• Identifying process-related machine damage
• Tracking machine health over time

Dryer section

Production assets

• Dryers
• Dryer can
• Felt roll
• Ventilation fan
• Combustion fan
• Vacuum pump

Environment

• High temperature
• High moisture

Accelerometer considerations

• Low frequency, low signal-to-noise ratio to capture low amplitudes at slow speeds
• Hermetically sealed 316L stainless steel
• High temp operation to 165ºC

Data processing considerations

• Analyzing vibration measurements on slow speed bearings requires granular data resolution and high frequency enveloping

Calendering section

Production assets

• Nip roll
• Calender drive system
• Reel

Environment

• High temperature

Accelerometer considerations

• High temp operation to 165ºC with extended range components for continuous operation in hot environments without degradation

Data processing considerations

• Processing data integration to correlate process variables, such as speed and product type, with machine health

Mattias Hoehnen is a Technical Product Owner for artificial intelligence, machine learning, and IoT hardware at KCF Technologies, driving the development of condition monitoring solutions to make manufacturing more reliable, efficient, and safe. He holds a Bachelor of Science and a Master of Science in Mechanical Engineering from Michigan Technological University and is an ISO CAT II certified vibration analyst.