Vibration Analysis

Vibration analysis measures and interprets equipment vibration patterns to detect mechanical problems in rotating and reciprocating machinery. Accelerometers capture vibration signals which are analyzed in frequency domain revealing specific fault signatures for bearings, gears, belts, and structural components before failures occur.

Analysis Techniques

Time Domain Analysis Vibration amplitude over time reveals impact events, intermittent problems, and overall vibration severity. Peak values, RMS levels, and crest factors indicate developing problems.

Frequency Domain Analysis Fast Fourier Transform (FFT) converts time signals into frequency spectra showing vibration energy at specific frequencies. Each mechanical component generates characteristic frequencies when defective.

Fault Signatures Bearing defects create vibration at specific frequencies related to bearing geometry and speed. Imbalance appears at running speed, misalignment at 2X running speed, and looseness at multiple harmonics.

Why It Matters

Early Detection Vibration analysis detects bearing problems months before failure, gear tooth cracks weeks early, and alignment issues immediately after occurrence. Early warning enables planned maintenance preventing catastrophic failures.

Specific Diagnosis Rather than generic "bearing problem" assessments, vibration analysis identifies specific defects: outer race bearing failure, gear tooth spalling, or belt misalignment. Precise diagnosis ensures correct repairs and parts availability.

Condition Trending Continuous monitoring tracks vibration progression predicting remaining equipment life. Trending distinguishes gradual degradation requiring eventual maintenance from stable conditions needing no action.

Related Terms

• Accelerometer - Sensor measuring vibration acceleration
• FFT (Fast Fourier Transform) - Mathematical algorithm for frequency analysis
• Bearing Fault Frequencies - Characteristic vibration patterns from bearing defects
• Route-Based Monitoring - Periodic vibration measurements using portable equipment

Example

A CNC machining center's spindle historically fails every 18-24 months requiring $55,000 rebuilds and causing 60 hours lost production. CleverIoT implements continuous vibration monitoring on the spindle with analysis tracking bearing fault frequencies.

The system establishes baseline spectra during healthy operation monitoring for elevated energy at bearing characteristic frequencies. Ten months after implementation, analysis detects rising vibration at outer race bearing frequency indicating early-stage spalling.

Trending predicts failure in 6-8 weeks. The facility schedules spindle rebuild during planned two-week shutdown for facility maintenance, performing $18,000 in preventive bearing replacement. This avoids emergency failure requiring $55,000 crash rebuild, $37,000 in lost production, and potential workpiece/machine damage. The predictive capability pays for the monitoring system in the first detected failure.