An adaptive time–frequency demodulation method and its applications in rolling bearing fault diagnosis

Aiming at the problems of poor time-frequency aggregation and severe noise interference when traditional time-frequency analysis methods deal with complex multi-component signals, this paper proposes a new time-frequency analysis method – local maximum synchrosqueezing reassigning chirplet transform (LMSRCT). The core idea of the method is to introduce the principle of general linear chirplet transform (GLCT) into synchro-reassigning transform (SRT), followed by reassigning the results of local maximum synchrosqueezing chirplet transform (LMSSCT), and then introducing the innovative synchro-reassigning operator (SRO). This results to a novel three-step method for extracting instantaneous frequency, which ultimately yields the final time-frequency representation. This method ensures the integrity of each instantaneous frequency, solves the problem of energy divergence problems, and improves aggregation. Simulation and experimental results show that the LMSRCT can provide better characterization results compared to other methods and can effectively solve the different situations that occur between the instantaneous frequencies of complex signals. The proposed method can effectively estimate transmission speed, which achieves fault diagnosis under tacholess conditions, and the order analysis results are more accurate and reliable.

Section: Introductionmentioning

confidence: 99%

Local maximum synchrosqueezing reassigning chirplet transform and its application to gearbox fault diagnosis

Wang,

Tian,

Dai

et al. 2024

“…The method based on spectral kurtosis (SK) [6,7] and the method based on cyclic kurtosis entropy [8] integrate traditional time-frequency analysis with other techniques suitable for transient detection. The use of short-time Fourier transform [9,10] and wavelet transform [11,12] techniques for feature extraction has been a long-standing focus. In recent years, several methods based on the signal decomposition approach have been proposed successively, such as empirical mode decomposition (EMD) [13,14], variational mode decomposition [15,16], local mean decomposition [17], the stochastic resonance technique [18] and blind deconvolution algorithms [19].…”

Section: Introductionmentioning

confidence: 99%

Periodic group-sparse method via generalized minimax-concave penalty for machinery fault diagnosis

He,

Wen,

Liu

et al. 2024

Diagnosing faults in large mechanical equipment poses challenges due to strong background noise interference, wherein extracting weak fault features with periodic group-sparse property is the most critical step for machinery intelligent maintenance. To address this problem, a periodic group-sparse method based on a generalized minimax-concave penalty function is proposed in this paper. This method uses periodic group sparse techniques to capture the periodic clustering trends of fault impact signals. To further enhance the sparsity of the results and preserve the high amplitude of the impact signals, non-convex optimization techniques are integrated. The overall convexity of the optimization problem is maintained through the introduction of a non-convex controllable parameter, and an appropriate optimization algorithm is derived. The effectiveness of this method has been demonstrated through experiments with simulated signals and mechanical fault signals.

“…equipment [1,2]. Due to harsh and complex operating conditions, they are susceptible to various types of faults.…”

Section: Introductionmentioning

confidence: 99%

HTG transformation: an amplitude modulation method and its application in bearing fault diagnosis

Qiao,

Zhang,

Zhang

et al. 2024

Self Cite

Rolling bearings are critical components in modern mechanical equipment, and the health monitoring and predictive maintenance of bearings are crucial for the normal operation of machinery. Hence, there is a compelling need to delve into advanced methodologies for enhancing the detection of fault characteristics in bearings. Faulty bearings produce periodic impulses during constant-speed rotation, which can typically be detected through envelope analysis. However, in some complex conditions, the relevant fault frequencies may be hidden within interfering components. This paper presents an amplitude modulation technique called the hyperbolic tangent Gaussian (HTG) transformation, designed to extract weak fault components from signals. Firstly, a family of amplitude modulation functions, known as the HTG functions, is constructed. These functions modulate signals with normalized amplitudes to obtain a series of modulated signals. Simultaneously, a frequency domain amplitude ratio (FDAR) metric is used for the automatic selection of the optimal components. Finally, the HTGgram is introduced, a spectral decomposition method based on trend components, aiming to identify the best combination of filtering and modulation components. Simulations with multi-component bearing fault signals and experimental signals with composite bearing faults demonstrate that this method not only highlights fault features and suppresses noise interference but also adaptively selects frequency bands related to faults, enhancing fault information. This approach exhibits excellent adaptability and effectiveness in complex operating conditions with multiple interference components.