The Generalized Spectral Kurtosis Estimator

Due to its conceptual simplicity and its proven effectiveness in real-time detection and removal of radio frequency interference (RFI) from radio astronomy data, the Spectral Kurtosis (SK) estimator is likely to become a standard tool of a new generation of radio telescopes. However, the SK estimator in its original form must be developed from instantaneous power spectral density (PSD) estimates, and hence cannot be employed as an RFI excision tool downstream of the data pipeline in existing instruments where any time averaging is performed. In this letter, we develop a generalized estimator with wider applicability for both instantaneous and averaged spectral data, which extends its practical use to a much larger pool of radio instruments.Comment: 5 pages, 2 figures, MNRAS Letters accepte

Novel spectral kurtosis technology for adaptive vibration condition monitoring of multi-stage gearboxes

In this paper, the novel wavelet spectral kurtosis (WSK) technique is applied for the early diagnosis of gear tooth faults. Two variants of the wavelet spectral kurtosis technique, called variable resolution WSK and constant resolution WSK, are considered for the diagnosis of pitting gear faults. The gear residual signal, obtained by filtering the gear mesh frequencies, is used as the input to the SK algorithm. The advantages of using the wavelet-based SK techniques when compared to classical Fourier transform (FT)-based SK is confirmed by estimating the toothwise Fisher's criterion of diagnostic features. The final diagnosis decision is made by a three-stage decision-making technique based on the weighted majority rule. The probability of the correct diagnosis is estimated for each SK technique for comparison. An experimental study is presented in detail to test the performance of the wavelet spectral kurtosis techniques and the decision-making technique

Spectral kurtosis based methodology for the identification of stationary load signatures in electrical signals from a sustainable building

Producción CientíficaThe increasing use of nonlinear loads in the power grid introduces some unwanted effects, such as harmonic and interharmonic contamination. Since the existence of spectral contamination causes waveform distortion that may be harmful to the loads that are connected to the grid, it is important to identify the frequency components that are related to specific loads in order to determine how relevant their contribution is to the waveform distortion levels. Due to the diversity of frequency components that are merged in an electrical signal, it is a challenging task to discriminate the relevant frequencies from those that are not. Therefore, it is necessary to develop techniques that allow performing this selection in an efficient way. This paper proposes the use of spectral kurtosis for the identification of stationary frequency components in electrical signals along the day in a sustainable building. Then, the behavior of the identified frequencies is analyzed to determine which of the loads connected to the grid are introducing them. Experimentation is performed in a sustainable building where, besides the loads associated with the normal operation of the building, there are several power electronics equipment that is used for the electric generation process from renewable sources. Results prove that using the proposed methodology it is possible to detect the behavior of specific loads, such as office equipment and air conditioning.Universidad de Valladolid y Consejo Mexicano de Ciencia y Tecnología (CONACYT) - (grant 743842)Universidad Autónoma de Querétaro, Fondo para el Desarrollo del Conocimiento (FONDEC-UAQ 2020) - (project FIN202011

Modified weak signal fault diagnosis method forbearing inner ring

In view of the shortcomings of mode mixing in EMD, the excess IMF component in the low frequency band, spectral kurtosis has no adaptive. The method of EMD and spectral kurtosis are proposed. Spectral kurtosis is very sensitive to the fault signal and it can enhance the fault pulse signal effectively. Then combine spectral kurtosis with EMD to overcome the shortcomings of EMD. Improved the efficiency and accuracy of EMD decomposition and the level of fault diagnosis. The method of EMD and spectral kurtosis are applied to the simulation signal and rolling bearing fault diagnosis. The experimental results show that the method of EMD and spectrum kurtosis can accurately judge the rolling bearing weak fault and have a good application prospect

Novel technology based on the spectral kurtosis and wavelet transform for rolling bearing diagnosis

A novel diagnosis technology combining the benefits of spectral kurtosis and wavelet transform is proposed and validated for early defect diagnosis of rolling element bearings. A systematic procedure for feature calculation is proposed and rules for selection of technology parameters are explained. Experimental validation of the proposed method carried out for early detection of the inner race defect. A comparison between frequency band selection through wavelets and spectral kurtosis is also presented. It has been observed that the frequency band selected using spectral kurtosis provide better separation between healthy and defective bearings compared to the frequency band selection using wavelet. In terms of Fisher criterion the use of spectral kurtosis has a gain of 2.75 times compared to the wavelet

Novel adaptation of the spectral kurtosis for vibration diagnosis of gearboxes in non-stationary conditions

In this paper, the adaptation of spectral kurtosis technology is proposed, demonstrated and experimentally validated. Raw data signals were collected from a single-stage gearbox run in different combinations of speed and load, after which time synchronous averaging was used to leave the classical residual signal once meshing harmonics were removed. Each data file is split into many individual realisations based on the time taken for the time synchronous average to converge on stable values, after which the short-time Fourier transform is used to calculate the spectral kurtosis for each realisation. The effects of adapting spectral kurtosis technology parameters such as the resolution and threshold used in creating a Wiener filter are evaluated, showing the effects on the consistent frequency bands identified throughout the realisations. Taking a baseline set of processing parameters, the probability of correct diagnosis was calculated using a three-stage decision-making technique incorporating the k-nearest neighbour and cluster analysis methods. Adaptation of the spectral kurtosis technology is then shown to dramatically improve the probability of correct diagnosis, highlighting that each speed and load case requires different resolution and threshold values to return the optimal result

Identification of multi-fault in rotor-bearing system using spectral kurtosis and EEMD

Condition monitoring and fault diagnosis via vibration signal processing play an important role to avoid serious accidents. Aiming at the complexity of multiple faults in a rotor-bearing system and drawback, the characteristic frequency of relevant fault could not be determined effectively with traditional method. The Spectral Kurtosis (SK) is useful for the bearing fault detection. Nevertheless, the simulation of experiment in this paper shows that the SK is unable to identify multi-fault of rotor-bearing system fully when different faults excite different resonance frequencies. A new multi-fault detection method based on EEMD and spectral kurtosis (SK) is proposed in order to overcoming the shortcoming. The proposed method is applied to multi-faults of rotor imbalance and faulty bearings. The superiority of the proposed method based on spectral kurtosis (SK) and EEMD is demonstrated in extracting fault characteristic information of rotating machinery

Use of spectral kurtosis for improving signal to noise ratio of acoustic emission signal from defective bearings

The use of Acoustic Emission (AE) to monitor the condition of roller bearings in rotating machinery is growing in popularity. This investigation is centred on the application of Spectral Kurtosis (SK) as a denoising tool able to enhance the bearing fault features from an AE signal. This methodology was applied to AE signals acquired from an experimental investigation where different size defects were seeded on a roller bearing. The results suggest that the signal to noise ratio can be significantly improved using SK