The single-phase differential boost inverter (SPDBI) possesses unique single-stage boost capability, which conventional voltage source inverters lack. While the discontinuous modulation strategy offers superior efficiency advantages, the inherent nonlinear distortion issues in SPDBI remain unresolved. This paper proposes a dual-loop control strategy based on pulse train control, which achieves excellent voltage tracking and dynamic performance through well-designed duty cycles for two pulse train sets. Simulation and experimental results validate the effectiveness of the proposed control strategy.

Operating condition monitoring of wind turbine (WT) key components is of significant importance to preventative maintenance and the improvement of WT reliability. To realize this industrial target, health indicator (HI) construction is a crucial and indispensable step. While most of the recently reported HIs are emphasized effective in stationary cases, they are insufficiently applicable to variable speed conditions. To address this issue, a novel HI through operating speed slicing and discrepancy compensation is proposed in this article for WT generator bearing condition monitoring. First, signal probability distributions of the collected degradation data are appropriately characterized by an optimized multiparameter regression method. Then, benchmark distributions established at the normal state are identified through operating speed slicing, and the discrepancies induced by the time-varying operating condition are subsequently calibrated with a compensation strategy. On this basis, a globally comparable metric, by quantitatively evaluating the degree to which the currently established distribution deviates from the corresponding slice-related benchmark, is accordingly constructed. Experimental tests demonstrate that the proposed HI can make a more effective health state assessment for WT generator bearing under variable speed conditions when compared with the conventional indicators.

Domain adaptation has been widely used for knowledge transfer. However, the aligning targets of the existing domain adaptation mechanisms dynamically vary during the training, which leads to the loss oscillation, slow convergence, and poor robustness. To overcome this main problem, a novel domain adaptation mechanism named intermediate distribution alignment (IDA) is proposed. For implementing the end-to-end diagnostic tasks, a feature extractor based on deep convolutional neural network with wide first-layer kernel is first built to fit the posterior distributions of source and target domains. Then through the KL divergence, IDA maps the learned features from the source and target domains into a specific intermediate distribution. It is proved theoretically that IDA can align the prior distributions of two domains. The proposed IDA mechanism is successfully applied to the fault transfer diagnosis of planetary gearboxes without labeled target-domain samples. The comparative results show that the proposed IDA mechanism has higher diagnostic performance than the typical domain adaptation mechanisms.

To detect the incipient faults of rotating parts used in electromechanical systems widely, a novel transient feature extraction method based on the improved orthogonal matching pursuit (OMP) and one-dimensional K-SVD algorithm is explored in this paper. First, the stopping criterion of adaptive spark is developed, and then the corresponding OMP algorithm is used to remove the modulated and harmonic signals adaptively. Second, the residual signal is reformulated as a signal matrix by period segmentation and circulating shift, and the initial transient dictionary is constructed via the time-domain average technique. Subsequently, a novel K-SVD algorithm is proposed to get the optimized transient dictionary for the one-dimensional signal. Finally, the repetitive transient signal is recovered by the optimized dictionary. The simulated and experimental results show that the proposed method can not only much faster extract the fault characteristics than the traditional K-SVD method, but also more accurately detect the repetitive transients than the infogram method and the traditional K-SVD method.

Diagnosis of a civil aircraft engine, which is operating under speed variation conditions, is a representative problem encountered in aeronautic industry. It is still very challenging to estimate the instantaneous angular speed (IAS) through the aircraft engine vibration signal when no encoder or tachometer is available due to cost or technological reasons. However, for the currently available tacholess order tracking algorithms, many vital parameters must be initialized manually in advance, which lead to user-friendliness, even false diagnosis. To address this issue, a novel method is proposed and the merits of nonlinear mode decomposition are inherited, so the IAS can be estimated adaptively without prior knowledge. The vibration signal collected from a civil aircraft engine is used for validation; the experimental results exhibit that the proposed method is more accurate and flexible when compared with the conventional methods.

Sometimes the result of single tracker can be unreliable under some situation like illumination variation, occlusion, object size change, etc. Combining multiple estimates is a usually strategy to improve the performance of visual tracking, the ensemble approach can combine the advantages of difference models and overcome this limitation. In order to better fuse the results, we propose an adaptively fusion method that can select the weight of each track result automatically. Moreover, we propose an adaptively update strategy to avoid the “drift” during tracking. The assemble method and update strategy are selected by analyzing the situation of tracking response map. We expand Staple using our methods and evaluate the performance on famous object tracking benchmark. Experimental results show that our proposed method outperforms state-of-the-art tracking methods.