To solve the intractable problems of optimal rank truncation threshold and dominant modes selection strategy of the standard dynamic mode decomposition (DMD), an improved DMD algorithm is introduced in this paper. Distinct from the conventional methods, a convex optimization framework is introduced by applying a parameterized non-convex penalty function to obtain the optimal rank truncation number. This method is inspirited by the performance that it is more perfectible than other rank truncation methods in inhibiting noise disturbance. A hierarchical and multiresolution application similar to the process of wavelet packet decomposition in modes selection is presented so as to improve the algorithm’s performance. With the modes selection strategy, the frequency spectrum of the reconstruction signal is more readable and interference-free. The improved DMD algorithm successfully extracts the fault characteristics of rolling bearing fault signals when it is utilized for mechanical signal feature extraction. Results demonstrated that the proposed method has good application prospects in denoising and fault feature extraction for mechanical signals.

Total serum bilirubin level is an independent risk factor for CAD. It has a strong relationship with coronary artery lesion types.

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Dynamic mode decomposition (DMD) has certain advantages compared with the traditional fault signal diagnosis method. By exploiting the strength of DMD algorithm in signal processing, this paper proposes a joint fault diagnosis scheme to extract the spatial and temporal patterns and evaluate them for the complexity to diagnose the fault for one-dimensional mechanical signal. The multiscale method is adopted to decompose the reconstructed matrix of standard DMD modes into multiple scales with a given level parameter. Total least squares DMD algorithm is performed on each level to solve the noise sensitivity problem. Approximate entropy (ApEn) is performed on the grouped multiscale spatiotemporal modes that represent the dynamic characteristic information of the original signal. ApEn values are used as a fault recognizer to identify fault types. By applying the algorithm on three experimental mechanical vibration data, we verify the effectiveness of the proposed method. The result demonstrates that the proposed scheme can effectively recognize different fault forms as a fault diagnosis method.

The real weld toe geometry is generally not mathematically perfect, resulting in obvious stress concentration effects, both on the weld section and along the longitudinal direction of the weld toe. The true stress-strain state at the local weld toe directly affects the fatigue performance and behavior of the welded structure. Therefore, a Fiber Bragg Grating (FBG) sensor based method for testing the cyclic strain at the weld toe was proposed. Cruciform welded joints were fabricated as specimens on which FBG sensors were arranged at several characteristic points along the weld toe curve. Strains at all the characteristic points under cyclic tensile load were measured and recorded, which showed the proposed measuring method could accurately obtain the complete local strain time histories along the weld toe. The strain time histories clearly reflected the cyclic hardening phenomenon in the early stage and the plastic yielding phenomenon in the final stage. Furthermore, based on the cyclic stress-strain constitutive model of the weld material, the stress-strain response curves of all the characteristic points were drawn. Combined with the fatigue fracture morphology, the mechanism of the unsynchronized initiation of the multiple cracks in the weld toe was investigated.