Centrifugal pump impeller defect identification by the improved adaptive variational mode decomposition through vibration signals

Vashishtha, Govind; Kumar, Rajesh

doi:10.1088/2631-8695/ac23b5

Cited by 26 publications

(7 citation statements)

References 42 publications

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“…Fault diagnosis for centrifugal pumps is most commonly performed with vibration analysis [19][20][21][22][23][24]. Vibration analysis is the process of monitoring vibration signals within a machine to detect abnormal operating levels or patterns to evaluate its condition.…”

Section: Pump Fault Mechanismsmentioning

confidence: 99%

Integrating Survival Analysis with Bayesian Statistics to Forecast the Remaining Useful Life of a Centrifugal Pump Conditional to Multiple Fault Types

Kapuria

Cole

2023

Energies

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To improve the viability of nuclear power plants, there is a need to reduce their operational costs. Operational costs account for a significant portion of a plant’s yearly budget, due to their scheduled-based maintenance approach. In order to reduce these costs, proactive methods are required that estimate and forecast the state of a machine in real time to optimize maintenance schedules. In this research, we use Bayesian networks to develop a framework that can forecast the remaining useful life of a centrifugal pump. To do so, we integrate survival analysis with Bayesian statistics to forecast the health of the pump conditional to its current state. We complete our research by successfully using the Bayesian network on a case study. This solution provides an informed probabilistic viewpoint of the pumping system for the purpose of predictive maintenance.

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Section: Pump Fault Mechanismsmentioning

confidence: 99%

Integrating Survival Analysis with Bayesian Statistics to Forecast the Remaining Useful Life of a Centrifugal Pump Conditional to Multiple Fault Types

Kapuria

Cole

2023

Energies

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“…Initial assumptions are made about how long these procedures will take to compute. However, by looking at the opposing solution [48][49][50], this computation time can be lowered. The best option is then chosen by examining both the random solution and its opposing solution.…”

Section: Opposition Based Learningmentioning

confidence: 99%

An ameliorated African vulture optimization algorithm to diagnose the rolling bearing defects

Vashishtha

Chauhan

Kumar

et al. 2022

Meas. Sci. Technol.

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In this work, a novel bearing fault identification scheme making use of deep learning has been proposed. Initially, the raw vibration signal is passed through a time-varying filter based empirical mode decomposition (TVF-EMD) to obtain different modes. Filter parameters of TVF-EMD are optimized by a newly developed optimization algorithm i.e., ameliorated African vulture optimization algorithm (A-AVOA). The Kernel estimate for mutual information (KEMI) has been considered as the fitness index for the developed optimization algorithm. The mode having the least value of fitness index is known as a prominent mode from which sensitive features representing different bearing conditions are extracted. These extracted features help in preparing the data matrix which is further utilised to build fuzzy-based classification models. The results obtained revealed that the linguistic hedge neuro-fuzzy classifier obtained maximum performance with the least computational time. The comparison of the developed method has also been done with other classification models viz., KNN, SVM, ELM and random forest that revealed the superiority of the developed method.

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“…k The modes obtained from VMD synchronizes themselves according to the central frequency w k [25,48]. The following steps are followed for decomposing the signal into modes using VMD: (1) To obtain frequency spectrum, the Hilbert transform is applied to each mode; (2) The frequency spectrum of each mode synchronizes with the central frequency;…”

Section: Theoretical Background 21 Variational Mode Decomposition (Vmd)mentioning

confidence: 99%

Adaptive MOMEDA model based variational mode decomposition for Pelton wheel fault detection

Vashishtha¹,

Chauhan²,

Yadav³

et al. 2022

Eng. Res. Express

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A critical step in fault diagnosis is determining the frequency of faults. Variational mode decomposition (VMD) is extensively employed for this purpose since it can describe the signal in the time-frequency domain. On the other hand, the VMD frequently fails to analyse non-stationary data containing low-frequency disturbances/noises. A multipoint optimal minimal entropy deconvolution adjusted (MOMEDA) is used with VMD in this research to improve defect detection performance in the presence of low-frequency disturbances. The filter length has a strong influence on the output of MOMEDA thus choosing the right one is a critical step in recovering a periodic pulse in the event of a weak defective signal. Improved grey wolf optimization (GWO) adaptively selects the appropriate filter length using the autocorrelation energy as its fitness function. The GWO is improved by introducing a gaussian mutation strategy which maintains the proper balance between the exploration and exploitation process. The proposed method has been applied to investigate the bucket defects of the Pelton wheel. The raw vibration signal is first decomposed into a series of modes using VMD. Second, the MOMEDA model is used to purify each mode by reducing low-frequency noise interference. The modes processed by MOMEDA are reconstructed again into a pure signal. Finally, the Hilbert envelop spectrum of the pure signal is obtained to determine the fault frequency. The same is verified from the theoretical fault feature frequency of the turbine bucket. The performance of the proposed method in extracting fault frequency accurately is also compared with other two models: a) MOMEDA model with ensemble empirical mode decomposition (EEMD) and b) MOMEDA model with complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The comparison results proved the efficacy and superiority of the proposed method.

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Centrifugal pump impeller defect identification by the improved adaptive variational mode decomposition through vibration signals

Cited by 26 publications

References 42 publications

Integrating Survival Analysis with Bayesian Statistics to Forecast the Remaining Useful Life of a Centrifugal Pump Conditional to Multiple Fault Types

Integrating Survival Analysis with Bayesian Statistics to Forecast the Remaining Useful Life of a Centrifugal Pump Conditional to Multiple Fault Types

An ameliorated African vulture optimization algorithm to diagnose the rolling bearing defects

Adaptive MOMEDA model based variational mode decomposition for Pelton wheel fault detection

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