Use of vibration signal to estimate instantaneous angular frequency under strong nonstationary regimes

Li, Yifan; Geng, Changqing; Zuo, Ming J.; Liang, Xihui

doi:10.1016/j.ymssp.2023.110571

Cited by 15 publications

(6 citation statements)

References 40 publications

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“…The process of extracting time-frequency ridges is a two-step procedure: (1) One-dimensional signals are transformed into the time-frequency plane through the use of TFRs; (2) Tracing and extracting the target ridges in the time-frequency plane by defining some criteria [26]. Once the suitable time-frequency ridges are obtained, the corresponding shaft rotational speeds can be determined.…”

Section: Ridge Extraction Methods Based On Automatic Peak Search and ...mentioning

confidence: 99%

Extraction of time-frequency ridge line based on automatic peak search and curve fitting

Hu,

Wang,

Wang

et al. 2024

Eng. Res. Express

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Instantaneous rotational frequency (IRF) plays a crucial role in diagnosing bearing faults under variable speed conditions. However, accurately extracting the rotational frequency becomes challenging due to significant noise and strong harmonic interference. To address this issue, this paper proposes an approach for extracting Time-frequency ridge line through automatic peak search and curve fitting with a minimal number of parameters. The approach eliminates disturbance from harmonics and noise by locating peak coordinates within specified areas. Moreover, these peak coordinates were fitted as interpolated nodes to prevent sudden changes in the curve due to noise interference. It also enhances the accuracy of the extracted ridge. The efficacy of the proposed technique is confirmed through simulations and specially designed test bench data. Results show that IRF-estimation errors of our method fall by about 20% compared with those of any traditional method, approaching TKEO and Phase Demodulation without using too many parameters.

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Section: Ridge Extraction Methods Based On Automatic Peak Search and ...mentioning

confidence: 99%

Extraction of time-frequency ridge line based on automatic peak search and curve fitting

Hu,

Wang,

Wang

et al. 2024

Eng. Res. Express

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“…The cumulative sum of each order over time is calculated and normalised to establish the order amplitude weight distribution for each component: 7) and (8).…”

Section: Time-order Weight Distributionmentioning

confidence: 99%

“…Without accurate rotational speed data, processing vibration signals can be significantly hampered. A new instantaneous frequency estimation method was proposed in [8], which consisting of time-frequency ridge extraction and an improved multi-order probabilistic approach. An enhanced instantaneous angular speed estimation approach for wind turbine tacholess order tracking was proposed in [9], which can estimate instantaneous frequency effectively under conditions of large speed variation and background noise.…”

Section: Introductionmentioning

confidence: 99%

Multicomponent collaborative time-frequency state-space model for vibration signal decomposition under nonstationary conditions

Huang,

Chen,

et al. 2024

Meas. Sci. Technol.

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The implementation of Prognostics and Health Management (PHM) strategies is essential for enhancing the safety and maintenance of rotating equipment in chemical plants. The examination of vibration signal behaviours under variable-speed conditions and the development of signal decomposition methods in such contexts are of substantial theoretical and practical relevance. This paper proposes a novel multicomponent collaborative time-frequency state-space method for the decomposition of vibration signals. The method employs a multi-component proportional model to accurately describe the synchronisation of high-frequency components with the rotational frequency component. Based on this framework, the instantaneous frequency change curves of multiple components are input into the Vold-Kalman filter algorithm for precise decomposition of multicomponent vibration signals under variable-speed conditions. Experimental results demonstrate the effectiveness of the proposed method in achieving accurate instantaneous frequency tracking and signal decomposition, showing clear advantages over traditional methods.

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“…The cantilever roadheader consists of a mechanical system, a hydraulic system, and an electrical system. The fault diagnosis of mechanical systems focuses on gear and bearing [5,6] faults in the cutting transmission system, while hydraulic system faults mainly revolve around pressure fluctuations, unstable cylinder action, high oil temperature, and other faults. The occurrence of these fault phenomena is caused by a single factor or the coupling of multiple factors [7,8].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Optimization Method of Knowledge Graph Entity Relations for Smart Maintenance of Cantilever Roadheaders

Wang,

Liu,

Ding

et al. 2023

Mathematics

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The fault maintenance scenario in coal-mine equipment intelligence is composed of videos, images, signals, and repair process records. Text data are not the primary data that reflect the fault phenomenon, but rather the secondary processing based on operation experience. Focusing on the difficulty of extracting fault knowledge from the limited textual maintenance process records, a forward static full-connected topology network modeling method based on domain knowledge from four dimensions of physical structure, internal association, condition monitoring, and fault maintenance, is proposed to increase the efficiency of constructing a fault-maintenance knowledge graph. Accurately identifying the intrinsic correlation between the equipment anomalies and the faults’ causes through only domain knowledge and loosely coupled data is difficult. Based on the static full-connected knowledge graph of the cantilever roadheader, the information entropy and density-based DBSCAN clustering algorithm is used to process and analyze many condition-monitoring historical datasets to optimize the entity relationships between the fault phenomena and causes. The improved DBSCAN algorithm consists of three stages: firstly, extracting entity data related to fault information from the static fully connected graph; secondly, calculating the information entropy based on the real dataset describing the fault information and the historical operating condition, respectively; and thirdly, comparing the entropy values of the entities and analyzing the intrinsic relationship between the fault phenomenon, the operating condition data, and the fault causes. Based on the static full-connected topology storage in the Neo4j database, the information entropy and density-based DBSCAN algorithm is computed by using Python to identify the relationship weights and dynamically display optimized knowledge graph topology. Finally, an example of EBZ200-type cantilever roadheader for smart maintenance is studied to analyze and evaluate the forward and four-mainlines knowledge graph modeling method and the dynamic entity relations optimization method for static full-connected knowledge graph.

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Use of vibration signal to estimate instantaneous angular frequency under strong nonstationary regimes

Cited by 15 publications

References 40 publications

Extraction of time-frequency ridge line based on automatic peak search and curve fitting

Extraction of time-frequency ridge line based on automatic peak search and curve fitting

Multicomponent collaborative time-frequency state-space model for vibration signal decomposition under nonstationary conditions

Dynamic Optimization Method of Knowledge Graph Entity Relations for Smart Maintenance of Cantilever Roadheaders

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