What statistical method is most commonly used to detect trends and anomalies in vibration data from rotating equipment?

The statistical method most commonly used to detect trends and anomalies in vibration data from rotating equipment is spectral analysis, specifically Fast Fourier Transform (FFT). Spectral analysis transforms vibration data from the time domain (amplitude versus time) to the frequency domain (amplitude versus frequency). This transformation allows for the identification of specific frequencies that are associated with different machine components and potential faults. The Fast Fourier Transform (FFT) is an efficient algorithm for computing the discrete Fourier transform, which is used to convert a time-domain signal into its frequency components. In the frequency domain, the vibration data is represented as a spectrum, which shows the amplitude of vibration at different frequencies. Each rotating component in the equipment, such as bearings, gears, and shafts, generates vibrations at specific frequencies related to its rotational speed and geometry. By analyzing the vibration spectrum, it is possible to identify frequencies that are associated with specific faults, such as imbalance, misalignment, looseness, or bearing defects. For example, an imbalance in a rotating shaft will generate a vibration at the shaft's rotational frequency. A bearing defect will generate vibrations at specific frequencies related to the bearing's geometry and rotational speed. By tracking changes in the amplitude of these frequencies over time, it is possible to detect trends and anomalies that may indicate developing problems. For instance, a gradual increase in the amplitude of a bearing defect frequency may indicate that the bearing is deteriorating and needs to be replaced. Alarm levels are set for specific frequencies, and if the vibration amplitude exceeds these levels, an alert is generated, prompting further investigation and maintenance action.