Accelerometer
When should a reliability engineer choose Piezoelectric vs. MEMS?
Choose Piezoelectric for high-frequency, high-sensitivity applications (e.g., gear meshing, pump cavitation) up to 30kHz; choose MEMS for low-speed assets requiring DC response, low power/wireless IoT, or cost-effective triaxial monitoring on non-critical assets.
How does an accelerometer enable PdM in ServiceGrid?
It provides the vibration signal that, when fused with failure-history labels from the CMMS/EAM, allows ML models to predict upcoming failures and schedule maintenance during planned downtime.
What signal processing techniques are standard for accelerometer data?
Time-domain analysis (RMS, Peak, Crest Factor) for overall severity and Frequency-domain analysis (FFT, Spectral Plot) to identify specific fault frequencies (e.g., bearing natural frequencies, 1x/2x RPM for imbalance/misalignment).
An accelerometer is a transducer that measures proper acceleration (vibration) of machinery, converting mechanical motion into an electrical signal to detect early signs of asset degradation in Condition-Based Monitoring (CbM) and Predictive Maintenance (PdM) systems.
In ServiceGrid or shop-floor environments, a reliability engineer mounts a magnetic accelerometer (often piezoelectric or MEMS) directly to the bearing housing to capture exact vibration frequencies propagating through the metal, enabling diagnosis of faults like misalignment or imbalance before unplanned shutdowns occur. Piezoelectric accelerometers are preferred for heavy, high-end machinery (e.g., windmills, pumps, compressors) due to their wide frequency response (1Hz–30kHz) and high sensitivity for early failure detection. MEMS accelerometers are increasingly used for lightweight, low-cost IoT monitoring on lower-priority assets, offering DC response (useful for very low-speed imbalance) and triaxial data capture. Data is processed via RMS (overall vibration severity) and FFT (frequency spectrum analysis) to classify fault patterns, often integrated with AI/ML models for anomaly detection.
Incorrect Mounting: Failure to mount the sensor directly to the bearing housing or using poor magnetic coupling introduces signal noise, masking critical frequencies and leading to missed early warnings.
Bandwidth Mismatch: Selecting a sensor with insufficient frequency bandwidth (e.g., using a low-bandwidth MEMS sensor for high-speed gear meshing analysis >20kHz) results in undetected cavitation or bearing defects.
Ignoring DC Response: Relying solely on piezoelectric sensors (which cannot measure DC) for very low rotational speed assets causes failure to detect imbalance, as piezoelectric sensors require motion to generate a signal.