Denoising the hob vibration signal using improved complete ensemble empirical mode decomposition with adaptive noise and noise quantization strategies

Zhou, Han; Yan, Ping; Yuan, Yanfei; Wu, Dayuan; Huang, Qin

doi:10.1016/j.isatra.2022.05.017

Cited by 15 publications

(10 citation statements)

References 35 publications

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“…To improve the low reconstruction accuracy of EEMD, Niu et al [25] proposed a vibration signal noise reduction method based on complementary ensemble empirical mode decomposition (CEEMD) and bilateral filtering. Considering that statistical metrics such as correlation coefficient and kurtosis are invalid when containing non-Gaussian noise, Zhou et al [26] proposed a noise reduction method combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a noise quantization strategy. The intrinsic time-scale decomposition (ITD) method has good time-frequency aggregation.…”

Section: Vibration Signal Noise Reductionmentioning

confidence: 99%

Research Progress and Development Trend of Prognostics and Health Management Key Technologies for Equipment Diesel Engine

Liu

Zhang²,

Dong

et al. 2023

Processes

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The diesel engine, as the main power source of equipment, faces practical problems in the maintenance process, such as difficulty in fault location and a lack of preventive maintenance techniques. Currently, breakdown maintenance and cyclical preventive maintenance are the main means of maintenance support after a diesel engine failure, but these methods require professional maintenance personnel to carry out manual fault diagnosis, which is time-consuming. Prognostics and health management (PHM), as a new technology in the field of equipment maintenance support, has significant advantages in improving equipment reliability and safety, enhancing equipment maintenance support capability, and reducing maintenance support costs. In view of this, when introducing PHM into diesel engine maintenance support, the research progress and development trend of the key technologies of PHM for diesel engines are carried out with the objective of achieving precise maintenance and scientific management of diesel engines, and the key technologies demand traction. Firstly, the development history of PHM technology is reviewed, and its basic concept and main functions are introduced. Secondly, the system architecture of PHM for diesel engines is constructed, and its key technologies are summarized. Then, the research progress in the field of PHM for diesel engines is reviewed from four aspects: data acquisition, data processing, fault diagnosis, and health status assessment. Finally, the challenges faced by diesel engine PHM in engineering applications are analyzed, effective solutions to address these challenges are explored, and the future development trend is foreseen.

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Section: Vibration Signal Noise Reductionmentioning

confidence: 99%

Research Progress and Development Trend of Prognostics and Health Management Key Technologies for Equipment Diesel Engine

Liu

Zhang²,

Dong

et al. 2023

Processes

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“…It was concluded that the signal-to-noise ratio was increased by 48.03% and the root mean square error and total number of iterations were reduced by 38.77% and 33.34%, respectively, compared to EEMD. Furthermore, Zhou et al [33] introduced a denoising method that combines CEEMDAN with noise quantization strategies, offering an efficient means of enhancing the effective components and eliminating noise. In this study, the comparison with several state-of-theart approaches and the analysis of ablation experiments show that this method achieves better performance for enhancing the gear hobbing signatures.…”

Section: Introductionmentioning

confidence: 99%

A Novel Wind Turbine Rolling Element Bearing Fault Diagnosis Method Based on CEEMDAN and Improved TFR Demodulation Analysis

Zhang,

Wang,

Jiang

et al. 2024

Energies

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Among renewable energy sources, wind energy is regarded as one of the fastest-growing segments, which plays a key role in enhancing environmental quality. Wind turbines are generally located in remote and harsh environments. Bearings are a crucial component in wind turbines, and their failure is one of the most frequent reasons for system breakdown. Wind turbine bearing faults are usually very localized during their early stages which is precisely when they need to be detected. Hence, the early diagnosis of bearing faults holds paramount practical significance. In order to solve the problem of weak pulses being masked by noise in early failure signals of rolling element bearings, a novel fault diagnosis method is proposed based on the combination of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and an improved TFR demodulation method. Initially, the decomposition of vibration signals using CEEMDAN is carried out to obtain several intrinsic mode functions (IMFs). Subsequently, a novel KC indicator that combines kurtosis and the correlation function is designed to select the effective components for signal reconstruction. Finally, an innovative approach based on the continuous wavelet transform (CWT) for multi-scale demodulation analysis in the domain of time–frequency representation (TFR) is also introduced to extract the envelope spectrum. Further fault diagnosis can be achieved by the identification of the fault characteristic frequency (FCF). This study focuses on the theoretical exploration of bearing faults diagnosis algorithms, employing modeling and simulation techniques. The effectiveness and feasibility of the proposed method are validated through the analysis of simulated signals and experimental signals provided by the Center for Intelligent Maintenance Systems (IMS) of the University of Cincinnati and the Case Western Reserve University (CWRU) Bearing Data Center. The method demonstrates the capability to identify various types of bearing faults, including outer race and inner race faults, with a high degree of computational efficiency. Comparative analysis indicates a significant enhancement in fault diagnostic performance when compared to existing methods. This research contributes to the advancement of effective bearing fault diagnosis methodologies for wind turbines, thereby ensuring their reliable operation.

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“…Xie et al used the complementary EEMD (CEEMD) to retrieve the intrinsic mode functions (IMFs) with the highest correlation from the original signal of the train bearing [18]. Zhou et al proposed a novel denoising method by combining complete EEMD with adaptive noise and effectively extracting the effective components in the vibration signals with strong noise interference [19].…”

Section: Introductionmentioning

confidence: 99%

Optimised LightGBM-based health condition evaluation method for the functional components in CNC machine tools under strong noise background

Jia,

Jialong,

Wanghao

et al. 2024

Meas. Sci. Technol.

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The accurate health condition evaluation of the functional components in computer numerical control (CNC) machine tools is an important prerequisite for predictive maintenance and fault warning. The vibration signals of the functional components in CNC machine tools often contain substantial noise, impeding the extraction of relevant health condition information from the vibration signals. This work presents an approach that leverages the variational mode decomposition (VMD) enhanced by the Artificial Hummingbird Algorithm (AHA) alongside the Light Gradient Boosting Machine (LightGBM) optimised through particle swarm optimisation (PSO) to evaluate the health condition of the functional components in CNC machine tools amidst pervasive noise. Initially, the AHA optimised the penalty factor (α) and the decomposition layer (K) within the VMD. This optimised VMD was subsequently applied to denoise the original vibration signals. After this denoising process, PSO was employed to optimise the learning rate and maximum tree depth within LightGBM. Health condition evaluation experiments were executed on the feed system and spindle of the CNC machine tool to validate the proposed methodology. Comparative analysis indicates that the proposed method attains paramount accuracy and computational efficiency, which are crucial for accurately evaluating the health condition of the functional components in CNC machine tools.

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Denoising the hob vibration signal using improved complete ensemble empirical mode decomposition with adaptive noise and noise quantization strategies

Cited by 15 publications

References 35 publications

Research Progress and Development Trend of Prognostics and Health Management Key Technologies for Equipment Diesel Engine

Research Progress and Development Trend of Prognostics and Health Management Key Technologies for Equipment Diesel Engine

A Novel Wind Turbine Rolling Element Bearing Fault Diagnosis Method Based on CEEMDAN and Improved TFR Demodulation Analysis

Optimised LightGBM-based health condition evaluation method for the functional components in CNC machine tools under strong noise background

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