Vibration monitoring using sensors mounted on machines is widely used for rotating machinery fault diagnosis. The periodic overlapping group sparsity (POGS) method has been developed in previous work of authors, and is an effective technique for detecting faults induced in rotating machines. However, the regularization parameter of the POGS problem is roughly specified via a look-up table provided in the original work. To address this problem, a data-driven diagnostic method, which is termed the adaptively regularized periodic overlapping group sparsity (ARPOGS), is proposed in this paper. The non-stationary fault feature ratio which is defined in the Hilbert domain is employed to guide the optimal regularization parameter. The criterion of setting the interval of candidate regularization parameters is also discussed. The ARPOGS is developed in terms of a convex optimization problem, while non-convex regularizations are used to further promote the sparsity. Since the non-convex penalty term is used and the whole objective function is constrained as a convex optimization problem, the sparsity of useful fault features is maximally induced. A simulated signal is formulated to verify the performance of the proposed method for periodic feature extraction. Finally, the effectiveness of the proposed ARPOGS method is validated by analyzing real data collected from a wind turbine transmission system. The results demonstrated that the proposed method can effectively and automatically extract periodic-group-sparse features from noisy vibration signals.

Composite structures always undergo temperature variations in service. In this procedure, the external loading coupled with thermal cycling will lead to their accelerated failures. In addition, the different loadings and microscopic structural parameters also play the important role in nonlinear deformation behaviors. This article presents a coupled thermo‐mechanical micromechanical model for investigating the compressive deformations of composite laminates with consideration of thermal residual stress. To this end, an effective microscopic model with consideration of coupled thermo‐mechanical behaviors for continuous fiber composites was established. The presented micromechanical method was verified by comparing with experimental data. On this basis, thermal cycling and compressive loading effect on effective deformations of unidirectional lamina composites, as well as laminate composites are both considered. It is indicated that thermal cycling tends to decrease stiffness behaviors first then increase stiffness behaviors under compressive loading. POLYM. COMPOS., 40:2908–2918, 2019. © 2018 Society of Plastics Engineers

This paper is aimed at studying the effective mechanical property of shape memory polymer composites (SMPC) reinforced with natural short fibers. To this end, a novel modeling scheme was presented. The SMPC was firstly equivalent to the composite laminates, and the natural short fibers are also subtly equivalent to the ellipsoidal inclusions distributed in the matrix materials periodically. Moreover, a represented volume element along laminate thickness can be easily chosen, and its elastic constants are accurately acquired by employing a proper microscopic mechanical model. Herein, the high-fidelity generalized method of cells, which represents a good ability in predicting the effective mechanical behaviors of composites, was used. On this basis, the classic laminate theory was improved to suitable for describing the elastic constants and failure strength the SMPC with respect to ambient temperature. Numerical results show a good consistency to the experimental data. Moreover, a higher ambient temperature tends to sharply decrease their final failure strength. It is also revealed that the presented modeling method shows a great potential in calculating the effectively mechanical property of the natural short fiber-reinforced composites.

Mechanical equipment is always exposed to the poor working environments, such as humid, high temperature and heavy load, which may lead to a serious damage in the key components. It is critical to identify the initial fault in time to avoid huge economic losses and casualty accidents. In extracting fault characteristics of the rolling bearing, its characteristic frequency is always disturbed by strong noise. In order to accurately separate the fault features from the strong noisy signal, an improved sparsity-enhanced decomposition signal method via using the nonconvex penalty term of generalized minimax-concave (GMC) and the dictionary term of tunable Q-factor wavelet transform (TQWT) is presented in this paper. An adaptive method for selecting regularization parameters is presented to subtly minimize the signal-to-noise ratio and root mean square error. Moreover, in order to reduce calculation cost, the forward-backward splitting algorithm is employed to maintain the convexity of the proposed sparsity. A simulation study and two practical fault experiments are used to validate the effectiveness of the proposed method in rolling bearing fault.

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