Papers by Keyword: Bispectrum

Abstract: This paper proposes a new method of motor fault detection by applying the eliminated-signal as data sources for motor fault analysis. Bi-spectrum is used as a key method for processing the signal. The expectation is that the eliminated-signal may contain information for fault analysis. The spectrum and bi-spectrum of the signal are applied as signal processing methods to analyze the motor faults. The method is tested on 3 different motor conditions: healthy, stator fault, and rotor fault motor at full load condition. Based on experiments, the method can differentiate conditions clearly. They seem also to be able to measure fault severity levels by observing the change in among harmonic amplitudes.

Abstract: Based on the study of the characteristics of load current signal, this article develops a method to extract features that can be use to distinguish the different working status of machine tools in real-time manner. The features are extracted from wavelet packet energy spectrum and bispectrum of the load current signal, and thus can take advantages of both wavelet packet transforms and bispectrum in signal analysis. Experimental results show that, compared with the features extracted from wavelet packet energy spectrum or bispectrum alone, the features extracted by applying the proposed method can provide better performance in term of identifying the machine working status.

Abstract: Block forming machine, as a kind of automatic equipments, can quickly compact blocks. Higher-order spectrum analysis emerges as a new effective method in signal processing, which can describe nonlinear coupling, restrain Gaussian noise and reserve phase components. In the paper, a hydraulic exciter applying to block forming machine will be introduced. Then block forming machine’s random vibration signals during the compacting process would be collected, in order to make use of the sample data to build up a time series autoregressive model and bispectrum of three-order accumulation, to analyze AR bispectrum characteristics of the machine’s vibrate signals under different work conditions.

Abstract: As the improve of voltage class in power system and the aggravation of industrial pollution, flashover of contaminated insulators in HV and UHV system is growing, imperiling the security of power system severely. The linchpin of enhancing the security of system is excavating ways of monitoring status of insulator’s surface by some eigenvalues. In this paper, distinctive spectrum features of leakage current(LC) are computed, including power spectrum and bispectrum. The results show that bispectrum depicts the information of arc discharge in LC more valid than power spectrum, which is a good eigenvalue for flashover identification and risk predictions.

Abstract: Fault feature extraction and application is the key technology of fault diagnosis. In this paper, a fault diagnosis method using bispectrum and bispectrum entropy as the fault feature parameters is put forward. Bispectrum entropy as the information entropy in bispectrum domain can reflect the complexity of information energy. When the structure is failed, the distribution of bispectrum will be changed. bispectrum entropy can reflect this change and achieve good separation of the different types of fault. Vibration signal in different bearing states of a secondary drive gearbox is compared and analyzed, bispectrum energy spetrum and bispectrum entropy are extracted. Feature vector is set up via bispectrum entropy for the fault pattern recognition and diagnosis by BP neural network. The analysis result proves that bispectrum entropy is more sensitive to fault characteristic and can separate the fault of bearing.

Abstract: Performing bispectrum analysis on the actual measured vibration signals of the roller bearing with different failure modes, it developed that the spectrum distribution regions are similar among the same failure modes, and distinguishable among the different failure modes, thus this character can be used to classify fault types. The binary images extracted from the bispectra are taken as the feature vectors. The fuzzy clustering analysis based on objective function is applied for pattern recognition, which makes use of the binary image to construct a core and a domain representing the common and scope of bispectrum distribution, respectively, then constructs the objective function as the classify to achieve pattern recognition and diagnosis. The roller bearing fault diagnosis is performed as an example to verify the feasibility of the proposed method.

Abstract: The induction motor is the most common driver in industry and has been previously proposed as a means of inferring the condition of an entire equipment train, predominantly through the measurement and processing of power supply parameters. This has obvious advantages in terms of being non-intrusive or remote, less costly to apply and improved safety. This paper describes the use of the induction motor current to identify and quantify a number of common faults seeded on a two-stage reciprocating compressor. An analysis of the compressor working cycle leads to current signal the components that are sensitive to the common faults seeded to compressor system, and second- and third-order signal processing tools are used to analyse the current signals. It is shown that the developed diagnostic features: the bispectral peak value from the amplitude modulation bispectrum and the kurtosis from the current gives rise to reliable fault classification results. The low feature values can differentiate the belt looseness from other fault cases and valve leakage and inter-cooler leakage can be separated easily using two linear classifiers. This work provides a novel approach to the analysis stator current data for the diagnosis of motor drive faults.

Abstract: This paper proposes an advanced signal processing technique for the precise estimation of a nonlinear ultrasonic parameter, based on power spectral and bispectral analysis. The power spectrum and bispectrum estimation of the pulse-like ultrasonic signal used in the commercial SAM (scanning acoustic microscopy) equipment is especially considered in this study. The usefulness of the proposed estimation is confirmed by experiments for a Newton ring with a continuous air gap and a real semiconductor sample with local delaminations. The results show that the nonlinear parameter obtained by the proposed method had a good correlation with the delamination.

Abstract: Wavelet analysis and bispectrum was applied to Acoustic Emission (AE) signals from scratch tests on corroded hot-dip galvanized samples in order to achieve the detection of corrosion products in pieces non reachable by visual inspection. AE signals were correlated with the fracture mechanisms occurring during scratch tests, while the contact force increased. Results were corroborated by Scanning Electron Microscopy (SEM), Energy Dispersive X-ray spectroscopy (EDX) and X-Ray Diffraction (XRD).

Abstract: Bispectrum is a powerful tool for non-Gaussian signal processing and nonlinearity detection. However, it is difficult to use in practical applications due to that it is a 2-dimensional function. Bispectral slices are widely used reduction methods, and they can only represent a small part of the whole bispectral information. Integrated bispectrum contains more signal features than that of the bispectral slices, whereas the integration will lose the focus of some signal features. To overcome these problems, a new approach called maximal bispectrum is proposed to extract signal features. Maximal bispectrum is obtained by selecting the maximal values of every row of the magnitude bispectrum in the whole bispectral plane and it is a 1-dimensional function. Feature extraction based on maximal bispectrum is investigated and the maximal bispectrum is used to extract features of gear fault. Experimental results indicate that the maximal bispectrum is effective for diagnosing gear crack fault.