A novel method for speed recovery from vibration signal under highly non-stationary conditions. (November 2018) - Dziedziech, Kajetan; Jablonski, Adam; Dworakowski, Ziemowit
Highlights: Speed signal estimation method is capable to cooperate with high speed fluctuations. The method efficiency is based on the manual selection by operator. The method works well with the time-frequency components crossings. The method is able to work with low Signal-to-Noise Ratio signals. The method is verified on three industrial applications. Abstract: Many engineering structures operate under non-stationary conditions resulting in signals containing time-varying frequency components. Analysis of such signals in time or frequency domain naturally yields smeared results. Typically implemented solution to this problem is the angle and order domain analysis. However, in some scenarios, acquisition of speed signal may be not available, due to practical limitations. If the speed fluctuations are limited, or recorded vibrational signal is free from environmental and measurement disturbances, it is possible to extract the speed signal from raw time waveforms. State-of-the-art methods include e.g. semi-automatic tracking of the ridges of the Short-Time Fourier Transform for speed signal reconstruction. However, these methods can fail if the time-varying frequency components cross with each other. Moreover, it is very common that complicated structures operating in non-stationary conditions are controlled by sophisticated electronic systems, which can introduce constant frequency noise to the measuring equipment. This can lead to some interference of these time-invariant and time-variant frequency components. The paper presents a simple and easy way to use a tool for hand selection and tracking of desired components, thus making complex time-frequency filtration easy. Finally, instantaneous frequency is calculated with use of Centre Moments of Frequency to extract the speed signal. The presented method shows how a particular tool developed within 3-dimensional image processing branch could be implemented in analysis of 2-dimensional signals characterized by time-frequency varying components.