Non-dominated solution set based on time-frequency infograms for local damage detection of rotating machines

The determination of an index to balance the impulsiveness and cyclostationarity of an expected component is an interesting research topic in mechanical health monitoring. Many proposed indices focus on evaluating the impulsiveness or cyclostationarity of an analyzed signal, thus resulting that an expected component cannot dominate other modes using these indices under the interferential circumstance of occasional shocks and harmonic signals. Moreover, several parameters have to be determined in advance, thereby leading to taxing consumption even if some methods can achieve a trade-off between impulsiveness and cyclostationarity via multi-objective optimization algorithms. A non-dominated solution set based on time-frequency infograms is then proposed in this study to preferably synthesize the impulsiveness and cyclostationarity of detecting the local damage of rotating machines. Specifically, the proposed method is conducted through four steps: (a) decompose the measured signal into certain levels, (b) construct the time-frequency infograms, (c) define the non-dominated solution set by Pareto filtering, and (d) perform Hilbert transform on these non-dominated solutions to diagnose the health condition of the rotating machines. Several case studies, including a simulated signal, and experimental data from defective bearing and gear respectively, were conducted to validate the proposed method. Analysis and comparison results indicated that the proposed method outperforms the fast kurtogram, infograms and some advanced indices in identifying fault features from rotating machines.