Time-frequency ridge estimation: An effective tool for gear and bearing fault diagnosis at time-varying speeds

Li, Yifan; Zhang, Xin; Chen, Zaigang; Yang, Yunyun; Geng, Changqing; Zuo, Ming J.

doi:10.1016/j.ymssp.2023.110108

Cited by 75 publications

(17 citation statements)

References 35 publications

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“…Thus, the extracted ridge with the lowest frequency can be temporarily regarded as an ISRF. Based on this, the average ratio of curve-to-ISRF is used to detect bearing fault in [23,25]. Fourier mode is also utilized to alleviate the adverse influence of distortion curves on the average ratio.…”

Section: Bearing Diagnosis Using Probability Density Distributionmentioning

confidence: 99%

“…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%

“…(2) In order to cope with the issue of ridge jumps caused by scattering peaks, a peak map updating method is proposed to explore the underlying trend of the ridge around the smearing region. (3) Compared to the strategy [23][24][25] of calculating FCC using the average of the curve-tocurve ratios, the proposed FCC-estimated method based on the probability density function (PDF) can address the influence of outliers.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Bearing Diagnosis Using Probability Density Distributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Typically, these damage identification techniques require several operations including [ 34 , 35 ]: (i) data acquisition, (ii) feature extraction, (iii) feature normalization, (iv) feature fusion, and (v) feature classification. Other innovative techniques for wheel flat fault detection include a time–frequency ridge estimation method [ 36 ] or a multiscale morphology analysis [ 29 ] that can effectively extract the influential features of signals from strong background noise and under variable speed conditions.…”

Section: Introductionmentioning

confidence: 99%

Early Identification of Unbalanced Freight Traffic Loads Based on Wayside Monitoring and Artificial Intelligence

Silva

Guedes

Ribeiro

et al. 2023

Sensors

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The identification of instability problems in freight trains circulation such as unbalanced loads is of particular importance for railways management companies and operators. The early detection of unbalanced loads prevents significant damages that may cause service interruptions or derailments with high financial costs. This study aims to develop a methodology capable of automatically identifying unbalanced vertical loads considering the limits proposed by the reference guidelines. The research relies on a 3D numerical simulation of the train–track dynamic response to the presence of longitudinal and transverse scenarios of unbalanced vertical loads and resorting to a virtual wayside monitoring system. This methodology is based on measured data from accelerometers and strain gauges installed on the rail and involves the following steps: (i) feature extraction, (ii) features normalization based on a latent variable method, (iii) data fusion, and (iv) feature discrimination based on an outlier and a cluster analysis. Regarding feature extraction, the performance of ARX and PCA models is compared. The results prove that the methodology is able to accurately detect and classify longitudinal and transverse unbalanced loads with a reduced number of sensors.

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“…For these approaches, the entire data set is used for computing specific metrics, such as entropies, energies, forms, and moments. With this information, the novelty means alterations in these values compared with normal data sets, such as time–frequency ridge estimation (TFRE) [ 20 , 21 ], degree of cyclostationarity (DCS) demodulation [ 22 ], and entropy measure-based methods (EMBM) [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Power Disturbance Monitoring through Techniques for Novelty Detection on Wind Power and Photovoltaic Generation

Gonzalez-Abreu

Osornio-Ríos

Elvira-Ortiz

et al. 2023

Sensors

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Novelty detection is a statistical method that verifies new or unknown data, determines whether these data are inliers (within the norm) or outliers (outside the norm), and can be used, for example, in developing classification strategies in machine learning systems for industrial applications. To this end, two types of energy that have evolved over time are solar photovoltaic and wind power generation. Some organizations around the world have developed energy quality standards to avoid known electric disturbances; however, their detection is still a challenge. In this work, several techniques for novelty detection are implemented to detect different electric anomalies (disturbances), which are k-nearest neighbors, Gaussian mixture models, one-class support vector machines, self-organizing maps, stacked autoencoders, and isolation forests. These techniques are applied to signals from real power quality environments of renewable energy systems such as solar photovoltaic and wind power generation. The power disturbances that will be analyzed are considered in the standard IEEE-1159, such as sag, oscillatory transient, flicker, and a condition outside the standard attributed to meteorological conditions. The contribution of the work consists of the development of a methodology based on six techniques for novelty detection of power disturbances, under known and unknown conditions, over real signals in the power quality assessment. The merit of the methodology is a set of techniques that allow to obtain the best performance of each one under different conditions, which constitutes an important contribution to the renewable energy systems.

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Time-frequency ridge estimation: An effective tool for gear and bearing fault diagnosis at time-varying speeds

Cited by 75 publications

References 35 publications

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

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

Early Identification of Unbalanced Freight Traffic Loads Based on Wayside Monitoring and Artificial Intelligence

Power Disturbance Monitoring through Techniques for Novelty Detection on Wind Power and Photovoltaic Generation

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