Detection
In reliability engineering and CMMS (like ServiceGrid), Detection is the process of identifying a deviation from normal equipment behavior or the onset of a potential failure (P-failure) before functional failure occurs, typically by analyzing sensor data, inspection results, or operator observations.
In shop floor maintenance, Detection is integrated with CMMS to trigger automated work orders from anomalies like bearing vibration exceeding thresholds, linking to asset records for immediate scheduling. The goal is to shorten the time between detection and repair to maximize the P-F Interval, preventing secondary damage. Execution follows ISO 14224 modes: scheduled activities (periodic inspections, vibration/oil analysis), continuous monitoring (real-time IoT sensors), and casual occurrences (operator observations, production interruptions).
How is Detection quantified in Risk Priority Number (RPN) calculations?
Detection is scored as the ease of spotting a failure; in RPN = Severity × Occurrence × Detection, a lower score (harder to detect) increases the RPN, prioritizing the failure mode for mitigation.
What is the role of Machine Learning in modern Detection?
Algorithms like Convolutional Neural Networks (CNN) analyze frequency-domain features (via FFT/CWT) from vibration data to classify bearing faults with >90% accuracy, enabling early fault detection before human inspection would notice.
Which technologies are critical for rotating equipment Detection?
Vibration analysis (imbalance/misalignment), Thermal imaging (hotspots), Ultrasonics (bearing arcing/leaks), and Oil analysis (wear particles) are the primary predictive methods.