Shuren Qin

Bio: Shuren Qin is an academic researcher from Chongqing University. The author has contributed to research in topics: Instantaneous phase & Hilbert spectral analysis. The author has an hindex of 4, co-authored 4 publications receiving 185 citations.

Accurate instantaneous frequency estimation with iterated Hilbert transform and its application

Abstract: Iterated Hilbert transform (IHT) is a new method for multicomponent demodulation. The principle of IHT is introduced, and some of its properties are researched. Although the amplitudes of the demodulated components obtained by IHT are accurate, there are limitations in the direct estimation of instantaneous frequencies via the extracted phases, so a smoothed instantaneous frequency estimation (SIFE) method based on difference operator and zero-phase digital low-pass filtering is proposed. The simulation results show that the proposed approach has higher performance than the adaptive segmentation algorithm and Hilbert-Huang transform. Finally, IHT and SIFE are applied to fault diagnosis of a rolling bearing, and the results show that the multicomponent fault vibration signal can be demodulated correctly and the weak feature of fault signals can be extracted efficiently with IHT and SIFE.

On the HHT, its problems, and some solutions

Abstract: The empirical mode decomposition (EMD) is reviewed and some questions related to its effective performance are discussed. Its interpretation in terms of AM/FM modulation is done. Solutions for its drawbacks are proposed. Numerical simulations are carried out to empirically evaluate the proposed modified EMD.

Review of Automatic Fault Diagnosis Systems Using Audio and Vibration Signals

Abstract: The objective of this paper is to provide a review of recent advances in automatic vibration- and audio-based fault diagnosis in machinery using condition monitoring strategies. It presents the most valuable techniques and results in this field and highlights the most profitable directions of research to present. Automatic fault diagnosis systems provide greater security in surveillance of strategic infrastructures, such as electrical substations and industrial scenarios, reduce downtime of machines, decrease maintenance costs, and avoid accidents which may have devastating consequences. Automatic fault diagnosis systems include signal acquisition, signal processing, decision support, and fault diagnosis. The paper includes a comprehensive bibliography of more than 100 selected references which can be used by researchers working in this field.

Application of Bandwidth EMD and Adaptive Multiscale Morphology Analysis for Incipient Fault Diagnosis of Rolling Bearings

Abstract: This paper presents a novel signal processing scheme, bandwidth empirical mode decomposition, and adaptive multiscale morphological analysis (BEMD-AMMA) for early fault diagnosis of rolling bearings. In this scheme, we propose a bandwidth based method to select the best envelope interpolation method. First, multiple envelope algorithms are defined and separately subtracted from the original data to obtain the preintrinsic mode functions (PIMFs). Second, an IMF with the smallest frequency bandwidth is selected to be the optimal IMF (OIMF). Third, this OIMF is subtracted from the original signal, and then repeat the sifting process until the residual is a constant or monotonic. Since the OIMF has the smallest frequency bandwidth, the mode mixing phenomenon can be significantly weakened. After that the OIMFs with clear fault information are used to construct the main component of the original signal. Then, the AMMA is introduced to demodulate the constructed main component. Simulation and experimental vibration signals are employed to evaluate the effectiveness of the proposed method. Results show that the proposed method outperforms EMD-AMMA, ensemble empirical mode decomposition-AMMA, and generalized empirical mode decomposition-empirical envelope demodulation in detecting early inner race fault.