Incipient Fault Detection of Rolling Bearing with heavy background noise and interference harmonics is a hot topic. In this paper, a new method based on parameter optimized fast EEMD (FEEMD) and Maximum Autocorrelation Impulse Harmonic to Noise Deconvolution (MAIHND) method is proposed for detecting the incipient fault of rolling bearing. Firstly, the FEEMD method with parameters optimization is used to reduce the noise and eliminate the interference harmonics of the fault signal. As a noise assistant improved method, the FEEMD can reduce the mode mixing and enhance the calculation efficiency significantly. Secondly, a new indicator is developed to select the sensitive IMF. Finally, a novel MAIHND method is employed to extract impulse fault feature from the sensitive IMF. Simulation and experiments results indicated that the proposed parameter optimized FEEMD–MAIHND method can effectively identify the weak impulse fault feature of rolling bearing. Moreover, the excellent performance of the proposed indicator for sensitive IMF component selection and MAIHND method is verified.

Incipient fault detection of rolling bearing using maximum autocorrelation impulse harmonic to noise deconvolution and parameter optimized fast EEMD

階層ベイズ的 Deconvolution について

Autocorrelation energy and aquila optimizer for MED filtering of sound signal to detect bearing defect in Francis turbine

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.

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

Bearing incipient fault feature extraction using adaptive period matching enhanced sparse representation

In the marine systems, engines represent the most important part of ships, the probability of the bearings fault is the highest in the engines, so in the bearing vibration analysis, early weak fault detection is very important for long term monitoring. In this paper, we propose a novel method to solve the early weak fault diagnosis of bearing. Firstly, we should improve the alternating direction method of multipliers (ADMM), structure of the traditional ADMM is changed, and then the improved ADMM is applied to the compressed sensing (CS) theory, which realizes the sparse optimization of bearing signal for a mount of data. After the sparse signal is reconstructed, the calculated signal is restored with the minimum entropy de-convolution (MED) to get clear fault information. Finally we adopt the sample entropy. Morphological mean square amplitude and the root mean square (RMS) to find the early fault diagnosis of bearing respectively, at the same time, we plot the Boxplot comparison chart to find the best of the three indicators. The experimental results prove that the proposed method can effectively identify the early weak fault diagnosis.

Yangde Gao

Papers

Spare optimistic based on improved ADMM and the minimum entropy de-convolution for the early weak fault diagnosis of bearings in marine systems.