Mechanical equipment often works on variable speed condition, its corresponding vibration signal presents multi-component, modulation coupling with fast time-varying instantaneous frequency (IF), how to effectively compute IF and realize fasting varying non-stationary signal decoupling separation plays an important role in mechanical system fault diagnosis. In this paper, a sparse representation method called multi-scale chirp sparse representation (MSCSR) is introduced to identify, extract, and trend IF for achieving a highly concentrated time-frequency energy. Simulation demonstrates that the proposed method performs better than traditional IF estimation method. Furthermore, an adaptive time-varying filter is constructed using the extracted instantaneous frequency to decouple non-stationary fast signal. Ultimately, by rapid instantaneous frequency fluctuation experiment, the effectiveness of proposed method for fast strong time-varying signal is validated, it can effectively extract rapid oscillation instantaneous frequency, and the error is less than 10%.

Weak fault detection of rolling bearing presents difficult, because the periodic transient signature produced via localized incipient damage is easily submerged by various interference components and background noise. Hybrid intelligent fusion method is a breakthrough strategy for revealing feature frequency of rolling bearing fault by comprehensively using a variety of intelligent signal processing technologies, possessing the advantage of each technology. Considering the rolling bearing often construct a transmission device combination with gear and shaft, its vibration signal is often vulnerable to other multi-morphology components, such as harmonic modulation, noise. Thus, how to identify the fault frequency in repetitive transients is crucial to accurately identify rolling bearing fault detection. To address this issue, a novel hybrid intelligent method is proposed to effectively apply on periodic transients extraction, enhancement and rolling bearing fault diagnosis. The innovation of this method is to solve three problems, namely, the separation of multi-morphology components, noise reduction without periodic transients distortion, weak fault frequency enhancement. The proposed method is tested and validated on simulated signal, rolling bearing fault signal from accelerated rolling bearing degradation rig. In addition, comparisons with other classical rolling bearing fault detection methods have been conducted to highlight the superiority of the proposed methodology.

Autonomous system is an emerging AI technology based on latest advances in intelligence, cognition, computing and systems science without human intervention, which is widely applied in the field of Internet of things, mechanical condition monitoring system and so on. With the rapid development of industry 4.0, autonomous system’s natural characteristic and application field determine that its corresponding signal presents multi-component, modulation coupling, non-stationary feature with fast time-varying instantaneous frequency (IF). Considering non-stationary signal contains system state information, how to effectively compute IF and realize signal decoupling separation plays an important role in autonomous system perception and making decision. In this paper, a sparse representation method called multi-scale chirp sparse representation (MSCSR) is introduced to to identify, extract and trend IF for achieving a highly concentrated time-frequency energy comparable to the standard IF estimation methods, simulation demonstrates that the proposed method performs better than traditional Hilbert-Huang transform and variational mode decomposition(VMD) combination with synchrosqueezing wavelet transform. Furthermore, an adaptive time-varying filter is constructed using the extracted instantaneous frequency to decouple non-stationary fast signal. Ultimately, by rapid instantaneous frequency fluctuation experiment, the effectiveness of proposed method for fast strong time-varying signal is validated, it can effectively extract rapid oscillation instantaneous frequency, and the error is less than 5%.

In general, during the whole life cycle operation of mechanical system, its corresponding condition signal often presents multi-component polymorphic-oscillatory characteristic and is accompanied with strong interference noise. In order to identify system operating status, how to achieve polymorphic signal decomposition is an unavoidable focus. Weak signal features corrupted by heavy background noise can be effectively extracted through sparse decomposition. In order to solve the problems of classical sparse decomposition method, such as the lack of signal fidelity, the local optimal solution caused by the non-convex objective function, and the poor universality of the model, a novel multi-source sparse optimization objective function with convexity is constructed based on the generalized mini-max concave penalty function. Then the sparse coefficients of unilateral attenuation transient component, bilateral attenuation transient component and harmonic component are calculated respectively based on Laplace wavelet dictionary, Morlet wavelet dictionary and DFT dictionary using forward backward splitting algorithm. Ultimately, each distinct component can be extracted based on these sparse coefficients. Comparison with the classical resonance sparse signal decomposition (RSSD) based on L1-norm, signal adaptive decomposition and spectral kurtosis show that the proposed method can accurately preserve the amplitude of morphological components under the low SNR premise. Experimental case infers that the proposed method compared with double tree complex wavelet (DTCW) possesses potential value of application on mechanical system fault detection without the prior knowledge of specific number of faults.