Vibration signal fusion using improved empirical wavelet transform and variance contribution rate for weak fault detection of hydraulic pumps

Yu, He; Li, Hongru; Li, Yaolong

doi:10.1016/j.isatra.2020.07.025

Cited by 72 publications

(29 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many of them may be found in the industry. The majority of them applies the Fourier transform [ 30 , 31 , 32 , 33 ] or wavelet transform [ 34 , 35 , 36 ] of the original signal as a preprocessing step. However, the above mentioned techniques require continuous data acquisition and analysis.…”

Section: Related Workmentioning

confidence: 99%

Sensor-Based Predictive Maintenance with Reduction of False Alarms—A Case Study in Heavy Industry

Hermansa

Kozielski

Michalak

et al. 2021

Sensors

View full text Add to dashboard Cite

In this paper, the problem of the identification of undesirable events is discussed. Such events can be poorly represented in the historical data, and it is predominantly impossible to learn from past examples. The discussed issue is considered in the work in the context of two use cases in which vibration and temperature measurements collected by wireless sensors are analysed. These use cases include crushers at a coal-fired power plant and gantries in a steelworks converter. The awareness, resulting from the cooperation with industry, of the need for a system that works in cold start conditions and does not flood the machine operator with alarms was the motivation for proposing a new predictive maintenance method. The proposed solution is based on the methods of outlier identification. These methods are applied to the collected data that was transformed into a multidimensional feature vector. The novelty of the proposed solution stems from the creation of a methodology for the reduction of false positive alarms, which was applied to a system identifying undesirable events. This methodology is based on the adaptation of the system to the analysed data, the interaction with the dispatcher, and the use of the XAI (eXplainable Artificial Intelligence) method. The experiments performed on several data sets showed that the proposed method reduced false alarms by 90.25% on average in relation to the performance of the stand-alone outlier detection method. The obtained results allowed for the implementation of the developed method to a system operating in a real industrial facility. The conducted research may be valuable for systems with a cold start problem where frequent alarms can lead to discouragement and disregard for the system by the user.

show abstract

Section: Related Workmentioning

confidence: 99%

Sensor-Based Predictive Maintenance with Reduction of False Alarms—A Case Study in Heavy Industry

Hermansa

Kozielski

Michalak

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Wavelet transform is a typical time-frequency analysis method, which can decompose vibration signal into some wavelets and provide some significant local information. However, it is a nonadaptive signal analysis method as wavelet basis functions are determined in advance [7][8][9][10][11]. An empirical mode decomposition (EMD) method was developed by Huang et al [12].…”

Section: Introductionmentioning

confidence: 99%

A Novel Parameter‐Adaptive VMD Method Based on Grey Wolf Optimization with Minimum Average Mutual Information for Incipient Fault Detection

Wang

2021

Shock and Vibration

View full text Add to dashboard Cite

Recently, variational mode decomposition (VMD) has attracted wide attention on mechanical vibration signal analysis. However, there are still some dilemmas in the application of VMD, such as the determination of the number of mode decomposition K and quadratic penalty term α. In order to acquire appropriate parameters of VMD, an improved parameter-adaptive VMD method based on grey wolf optimizer (GWO) is developed by taking the minimum average mutual information into consideration (GWOMI). Firstly, the parameters (K, α) are adaptively determined through GWOMI. Then, the vibration signal is decomposed by the developed method and effective modes are extracted according to the maximum kurtosis. Finally, the extracted modes are processed by Hilbert envelope analysis to acquire the incipient fault features. With the simulation and experimental analysis, it is clearly found that the developed method is effective and performs better than some existing ones.

show abstract

“…It is highly favorable for the processing and interpretation of non-stationary and complex data. Therefore, EWT demonstrates outstanding performance in various applications of machine fault diagnosis, seismic data analysis, image processing, medical disease diagnosis, and so on [19][20][21][22][23]. The vibration responses of engineering structures are usually complex due to the structural scale and intricate interactions between structures and dynamic loadings.…”

Section: Introductionmentioning

confidence: 99%

Modal Parameters Identification of Bridge Structures from GNSS Data Using the Improved Empirical Wavelet Transform

Fang

Meng

2021

Remote Sensing

View full text Add to dashboard Cite

It is difficult to accurately identify the dynamic deformation of bridges from Global Navigation Satellite System (GNSS) due to the influence of the multipath effect and random errors, etc. To solve this problem, an improved empirical wavelet transform (EWT)-based procedure was proposed to denoise GNSS data and identify the modal parameters of bridge structures. Firstly, the Yule–Walker algorithm-based auto-power spectrum and Fourier spectrum were jointly adopted to segment the frequency bands of structural dynamic response data. Secondly, the improved EWT algorithm was used to decompose and reconstruct the dynamic response data according to a correlation coefficient-based criterion. Finally, Natural Excitation Technique (NExT) and Hilbert Transform (HT) were applied to identify the modal parameters of structures from the decomposed efficient components. Two groups of simulation data were used to validate the feasibility and reliability of the proposed method, which consisted of the vibration responses of a four-storey steel frame model, and the acceleration response data of a suspension bridge. Moreover, field experiments were carried out on the Wilford suspension bridge in Nottingham, UK, with GNSS and an accelerometer. The fundamental frequency (1.6707 Hz), the damping ratio (0.82%), as well as the maximum dynamic displacements (10.10 mm) of the Wilford suspension bridge were detected by using this proposed method from the GNSS measurements, which were consistent with the accelerometer results. In conclusion, the analysis revealed that the improved EWT-based method was capable of accurately identifying the low-order, closely spaced modal parameters of bridge structures under operational conditions.

show abstract

Vibration signal fusion using improved empirical wavelet transform and variance contribution rate for weak fault detection of hydraulic pumps

Cited by 72 publications

References 35 publications

Sensor-Based Predictive Maintenance with Reduction of False Alarms—A Case Study in Heavy Industry

Sensor-Based Predictive Maintenance with Reduction of False Alarms—A Case Study in Heavy Industry

A Novel Parameter‐Adaptive VMD Method Based on Grey Wolf Optimization with Minimum Average Mutual Information for Incipient Fault Detection

Modal Parameters Identification of Bridge Structures from GNSS Data Using the Improved Empirical Wavelet Transform

Contact Info

Product

Resources

About