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

Abstract

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.

Smart Sensor-Based Monitoring Technology for Machinery Fault Detection

“…The VMD in Figure 9b performed better than the HHT in Figure 9a with the recognized second harmonic (60 Hz), due to its more efficient IMF selection and processing than the HHT. Based on Equation (18), the fault indicator values are 3.75, 4.22, and 5.09 for the HHT, VMD, and MVMD techniques, respectively.…”

“…The variational mode decomposition (VMD) method decomposes the vibration signal into several signatures with different center frequencies by using a set of adaptive Wiener filters [ 16 ]. Although VMD has been used in the extraction of nonlinear features and machine fault detection [ 17 , 18 ], it still has some limitations. For example, it requires pre-choosing the number of modes and the bandwidth control parameters.…”

Aerodynamic noise characteristics of a centrifugal fan in high-altitude environments

“…Tonal noise, or discrete noise, is generated when the fan volute and impeller output air flow interact [ 7 , 8 ]. The generation mechanism of fan aerodynamic noise has gathered considerable research attention, in addition to the development of noise numerical prediction methods and noise reduction design [ 9 , 10 ].…”

Acoustic detection and localization of gas pipeline leak based on residual connection and one-dimensional separable convolutional neural network

“…Traditional intelligent diagnosis methods mainly include two processes: feature extraction and pattern classification. Feature extraction usually uses a variety of signal processing methods such as mode decomposition (Vashishtha et al, 2022a(Vashishtha et al, , 2022b(Vashishtha et al, , and 2022cVashishtha and Kumar, 2021), spectral analysis (Vashishtha and Kumar, 2022), wavelet transform and statistical features (Meng et al, 2019;Zadkarami et al, 2017) to extract time-domain, frequency-domain and time-frequency domain features from original data. The extracted features are then input into support vector machine (SVM), particle swarm optimization (PSO), and artificial neural network (ANN), and Bayesian network algorithms are used to classify the faults.…”