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Condition monitoring (colloquially, CM) is the process of monitoring a parameter of condition in machinery (vibration, temperature etc.), in order to identify a significant change which is indicative of a developing fault.
Shock pulse method (SPM) is a technique for using signals from rotating rolling bearings as the basis for efficient condition monitoring of machines. From the innovation of the method in 1969 it has been further developed and broadened and is a worldwide accepted philosophy for condition monitoring of rolling bearings and machine maintenance.
The uses of a vibration spectrum analyzer in machine condition monitoring allows to detect and identify machine faults such as: rotor imbalance, shaft misalignment, mechanical looseness, bearing defects, among others. Vibration analysis can also be used in structures to identify structural resonances or to perform modal analysis.
Through wear particle analysis, you can identify the composition of the solid material present and evaluate particle type, size, concentration, distribution, and morphology. [11] The use of Model Based Condition Monitoring for predictive maintenance programs is becoming increasingly popular over time. This method involves spectral analysis on ...
Vibration analysis (VA), applied in an industrial or maintenance environment aims to reduce maintenance costs and equipment downtime by detecting equipment faults. [7] [8] VA is a key component of a condition monitoring (CM) program, and is often referred to as predictive maintenance (PdM). [9]
A particularly well developed part of it applies specifically to rotating machinery, one of the most common types encountered. To identify the most probable faults leading to failure, many methods are used for data collection, including vibration monitoring, thermal imaging, oil particle analysis, etc.
Structural health monitoring (SHM) involves the observation and analysis of a system over time using periodically sampled response measurements to monitor changes to the material and geometric properties of engineering structures such as bridges and buildings. In an operational environment, structures degrade with age and use.
machine vibration analysis based on harmonic patterns (gearbox faults, turbine blade failures, ...) [2] [4] [5] Recently, cepstrum-based deconvolution was used on surface electromyography signals, to remove the effect of the stochastic impulse train, which originates an sEMG signal, from the power spectrum of the sEMG signal itself.
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