The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Process monitoring of incipient faults, as opposed to abrupt faults, in an industrial process is increasingly becoming more important. These are slowly developing faults that may eventually lead to severe abnormal conditions, and ultimately, failure of a critical component. Data-driven multivariate statistical process monitoring (MSPM) methods are extensively studied and widely used for abrupt fault...
Process monitoring plays a vital role in order to sustain optimal operation and maintenance of the plant in process industry. As an essential stage in process monitoring, datadriven fault detection and diagnosis techniques have evolved quickly owing to the prosperity of multivariate feature extraction methods. In addition to the application of basic feature extraction methods, hybrid algorithms combining...
Planetary gearbox of wind turbine works under changed load and speed and the vibration signal is nonlinear, non-stationary, this make it difficult to extract the weak fault characteristic frequency. In this paper, a new method of fault feature extraction and separation based on empirical mode decomposition (EMD) and resonance demodulation is proposed. The method uses EMD to decompose the vibration...
In the fault diagnosis method of the resonance demodulation of the planetary gear of the wind turbine, there are some shortcomings like artificial difficulties in demodulation band and low demodulation spectral resolution. In this paper, wavelet packet decomposition and reconstruction method are used effectively to extract the resonance frequency components caused by the local fault of gear, which...
Aiming at the problem that the mechanical fault information of the transmission system of the wind turbine is weak in the stator voltage and current signals, and it is difficult to identify and extract, a new method of electrical signal preprocessing-instantaneous power of demodulation method has been proposed. And use the DSP to achieve the portable data acquisition and diagnosis system. By using...
In order to discuss the application of sensorless diagnosis in the fault diagnosis of wind power generator. A method which is different from the traditional model of wind turbine generator system is proposed. A simulation model of wind power generator is established based on MATLAB/SIMULINK, the dynamic simulation model of transmission gear in normal and fault condition is established based on ADAMS...
The problem that the mechanical fault information of wind turbine generator is weak in the voltage and current signals need to be solved, information is not conducive to the identification and extraction. According to the common algorithm, the instantaneous power and other parameters are combined well, and we exploit the characteristic of mechanical fault information which is used in the same phase...
The Principal Component Analysis (PCA) and the Partial Least Squares (PLS) are two commonly used techniques for process monitoring. Both PCA and PLS assume that the data to be analysed are not self-correlated i.e. time-independent. However, most industrial processes are dynamic so that the assumption of time-independence made by the PCA and the PLS is invalid in nature. Dynamic extensions to PCA and...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.