RESUMEN
The vibration signal of faulty rotating machinery tends to be a mixture of repetitive transients, discrete frequency components and noise. How to accurately extract the repetitive transients is a critical issue for machinery fault diagnosis. Inspired by reweighted L1 (ReL1) minimization for sparsity enhancement, a reweighted generalized minimax-concave (ReGMC) sparse regularization method is proposed to extract the repetitive transients. We utilize the generalized minimax-concave (GMC) penalty to regularize the weighted sparse representation model to overcome the underestimation deficiency of L1 norm penalty. Moreover, a new reweight strategy which is different from the reweight strategy in ReL1 for sparsity enhancement is proposed according to the statistical characteristic, i.e., squared envelope spectrum kurtosis. Then ReGMC is proposed by solving a series of weighted GMC minimization problems. ReGMC is utilized to process a simulated signal and the vibration signals of a hot-milling transmission gearbox and a run-to-failure bearing with incipient fault. The ReGMC analysis results and the comparison studies show that ReGMC can effectively extract the repetitive transients while suppressing the discrete frequency components and noise, and behaves better than GMC, improved lasso, and spectral kurtosis.
RESUMEN
Mechanical properties of composites manufactured by high-temperature polymer polyether ether ketone (PEEK) with continuous reinforced fibers are closely dependent on ambient temperature variations. In order to effectively study fatigue failure behaviors of composites under the coupled thermo-mechanical loading, a well-established microscopic model based on a representative volume element (RVE) is proposed in this paper. Stiffness degradation behaviors of the composite laminates at room and elevated temperatures are firstly investigated, and their failure strengths are compared with experimental data. To describe the fatigue behaviors of composites with respect to complex external loading and ambient temperature variations, a new fatigue equation is proposed. A good consistency between theoretical results and experimental data was found in the cases. On this basis, the temperature cycling effects on the service life of composites are also discussed. Microscopic stress distributions of the RVE are also discussed to reveal their fatigue failure mechanisms.
RESUMEN
The reliability of cutting tools is critical to machining precision and production efficiency. The conventional statistic-based reliability assessment method aims at providing a general and overall estimation of reliability for a large population of identical units under given and fixed conditions. However, it has limited effectiveness in depicting the operational characteristics of a cutting tool. To overcome this limitation, this paper proposes an approach to assess the operation reliability of cutting tools. A proportional covariate model is introduced to construct the relationship between operation reliability and condition monitoring information. The wavelet packet transform and an improved distance evaluation technique are used to extract sensitive features from vibration signals, and a covariate function is constructed based on the proportional covariate model. Ultimately, the failure rate function of the cutting tool being assessed is calculated using the baseline covariate function obtained from a small sample of historical data. Experimental results and a comparative study show that the proposed method is effective for assessing the operation reliability of cutting tools.