Ridge extraction based on adaptive variable-bandwidth cost functions by edge detection of time frequency images

Dou, Chao; Lin, J. H.

doi:10.1088/1361-6501/ab6278

Cited by 12 publications

(4 citation statements)

References 26 publications

(44 reference statements)

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“…It excels at removing noise and outliers, it is highly suitable for bearing signal data. The steps are shown in algorithm 1: From the above steps, K models, M k , are obtained, and Rènyi entropy is calculated in (16). The IF, M k ′ , with the lowest entropy, is selected as the candidate IF,…”

Section: Ridge Extraction For Iadtfd-ransacmentioning

confidence: 99%

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Bearing fault diagnosis under variable speed conditions on adaptive time frequency extraction mode decomposition

Huo,

Yang,

Yao

et al. 2024

Meas. Sci. Technol.

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Fault diagnosis of rolling bearings is crucial for their operation in variable working conditions in mechanical systems. Time-frequency distribution (TFD) methods are widely used in variable-speed rolling bearing fault diagnosis due to their ability to capture the time-frequency characteristics of signals. However, they suffer from weak robustness, poor resolution, and lack of feature information concentration. To address these issues, we construct a method: adaptive time frequency extraction mode decomposition (ATFEMD) by utilizing the characteristic of instantaneous frequency (IF) energy concentration in TFD. The method first enhances the adaptive time-frequency distribution (ADTFD) via an iterative algorithm, applying random sample consensus (RANSAC) algorithm enables ridges extraction from the TFD. The harmonics of the extracted ridges are then verified by passing the proxy data method, followed by a noise test to determine if the harmonics are true. From the above steps, ATFEMD decomposing the signal into physically meaningful adaptive mode (AM). This article analyzes the simulated bearing vibration and test bench bearing vibration signals to demonstrate the performance of ATFEMD. Results indicated that the proposed method is characterized by strong robustness, and good feature extraction results compared to other methods.

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Section: Ridge Extraction For Iadtfd-ransacmentioning

confidence: 99%

“…Ridge extraction of the TFD can serve to estimate an instantaneous frequency (IF) of a signal [16]. This relies on accurate time-frequency matrix measurements of vibration signals, highlighting the need for measurement accuracy.…”

Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis under variable speed conditions on adaptive time frequency extraction mode decomposition

Huo,

Yang,

Yao

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

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“…Liu et al [87] proposed a method based on generalized demodulation to iteratively extract timefrequency curves from the time-frequency representation of vibration signal components using a fast path optimization algorithm. Dou and Lin [88] proposed an adaptive variable bandwidth cost function (AVBCF) to adaptively search for ridge extraction regions in time-frequency images. Li et al [89] proposed an iterative feature ridge extraction (ICRE) strategy to automatically extract multiple feature ridges on the time-frequency plane.…”

Section: Based On Feature Extractionmentioning

confidence: 99%

Application of Time-Frequency Analysis in Rotating Machinery Fault Diagnosis

Bai

Cheng

Wen

et al. 2023

Shock and Vibration

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Fault diagnosis is an important means to ensure the safe and reliable operation of mechanical equipment. In machinery fault diagnosis, collecting and mining the potential fault information of the vibration signal is the most commonly used method to reflect the operating status of the equipment. In engineering scenarios, in the face of rotating machinery with variable speed, simple time domain analysis or frequency domain analysis is difficult to solve the problem. The time-frequency analysis technology that combines time-frequency transformation and data analysis can solve practical engineering problems by capturing the transient information of the signal. At present, a large number of related literatures have been published in academic journals. This paper hopes to provide convenience for relevant researchers and motivate researchers to further explore by summarizing the published literature. First, this paper briefly explains the concept of time-frequency analysis and its development. Then, the time-frequency transformation method proposed for the characteristics of rotating machinery fault vibration signal and related works of literature are reviewed, and the key issues of the application of time-frequency transformation method in rotating machinery fault diagnosis are discussed. Next, this paper summarizes the relevant literature on the combination of data analysis technology and time-frequency transformation and sorts out its development route and prospects. The study reveals that time-frequency analysis technology is able to detect the rotating machinery fault effectively. The time-frequency analysis technology has made abundant achievements in the field of rotating machinery fault diagnosis. It is expected that this review would inspire researchers to explore the potential of time-frequency analysis as well as to develop advanced research in this field.

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“…The cost function ridge estimation (CFRE) method [19] can partially mitigate the frequency jumps, but manual selection of the objective frequency band is required prior to ridge extraction. Based on this, Dou and Lin [20] and Li et al [21] implemented an adaptive capture of ridge search bands by utilizing an edge detection approach, and then improved the original cost function and weight factor to effectively isolate the interference of neighboring ridges [22,23]. In addition to these, Iatsenko et al [24] proposed an adaptive search algorithm based on dynamic path optimization (DPO) and fixed-point iteration.…”

Section: Introductionmentioning

confidence: 99%

An improved tracking method for bearing characteristic frequencies in the time-frequency representation of vibration signal

Chen,

Qi,

Yun

et al. 2024

Meas. Sci. Technol.

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Rolling bearing is one of the most critical components for support and energy conversion in machines. The fault characteristic frequency of time-frequency representation has received increasing attention in bearing diagnosis under variable speed conditions. However, the FCFs-extracted methods have poor adaptability to amplitude attenuation and noise interference for the reason of local distortions or even transitions in the estimated instantaneous frequency ridges. Consequently, this paper proposes an improved FCFs tracking method for variable speed bearing diagnosis. In which, a strategy for locating distortion intervals is first developed using exponential smoothing and residual distribution. Subsequently, an advanced fast path optimization method, including peak map renewal and curve search optimization, is proposed to extract the interested ridges. Finally, the probability density function of curve-to-curve ratios is designed for accurately identifying bearing fault. The simulation and experiment results demonstrate the effectiveness of the proposed method.

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Ridge extraction based on adaptive variable-bandwidth cost functions by edge detection of time frequency images

Cited by 12 publications

References 26 publications

Bearing fault diagnosis under variable speed conditions on adaptive time frequency extraction mode decomposition

Bearing fault diagnosis under variable speed conditions on adaptive time frequency extraction mode decomposition

Application of Time-Frequency Analysis in Rotating Machinery Fault Diagnosis

An improved tracking method for bearing characteristic frequencies in the time-frequency representation of vibration signal

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