The invention discloses an XLPE cable partial discharge defect type identification method. The method comprises the following steps: obtaining the de-noised XLPE cable ultrahigh frequency PRPS graph; extracting and computing four dimension statistical characteristic parameters and six non-dimension characteristic parameters of the PRPS graph; inputting the characteristic parameters to SOM neural network model; determining the win nerve cell, continuously learning through updating the weight vector of the win nerve cell by the SOM neural network, until the input sample corresponding to the output layer win nerve cell is stable; and outputting the type identification result of the SOM neural network. The XLPE cable partial discharge defect type identification method is capable of overcoming the defects of the traditional method that the convergence speed is slow and the judgment accuracy is bad, satisfying the visual requirement of the fault analysis, and improving the intelligent level of the XLPE cable partial discharge detection system, has the characters of rapid detection speed, simple process and high diagnosis accuracy, and is good for accurately evaluating the running state of the XLPE cable by the maintenance worker.

XLPE cable partial discharge defect type identification method

The invention provides a method for detecting fiber bragg grating acoustic emission of a helicopter bearing fault in order to detect an early-time state of the fault of rolling bearings on a helicopter, enhance the capacity for recognizing the fault of the bearings of the helicopter and prolong the service life of the bearings of the helicopter. The detecting method includes the steps of sensor installation, signal sensing and demodulation, wherein the sensor installation includes pasting-installation of a grating sensor (packaged or non-packaged), the single-fiber-bragg-grating sensing method is adopted for the sensing, and the demodulation includes photoelectric conversion, amplification, acquiring, resonance demodulation processing and the like. The method for detecting fiber bragg grating acoustic emission of the helicopter bearing fault is mainly used for detecting the fault of the bearings of the helicopter and faults of bearings of other rotating machines at the home position and on line. Compared with traditional detecting methods (eye observing, vibration, magnetic powder and the like), the method for detecting fiber bragg grating acoustic emission of the helicopter bearing fault can effectively detect the early-time state of the faults and accurately judge the fault type and the fault severity order, and the method has great significance for on-condition maintenance and service life prolonging of the helicopter.

Method for detecting fiber bragg grating acoustic emission of helicopter bearing fault

The invention discloses a bearing fault diagnosis method based on an adaptive redundancy lifting wavelet packet decomposition tree. The method comprises that an optimal wavelet that matches node feature information is obtained via a decomposition algorithm; problems of frequency band interlacing and frequency aliasing are solved successively; energies of two wavelet packet coefficients are solved, and the wavelet packet coefficient of higher energy serves as a new node signal to prepare for next decomposition; p norm entropies of the decomposed node signal and the two wavelet packet coefficients are solved and determined, if the sum of the entropies of the wavelet packet coefficients is higher than the entropy of the node signal, decomposition continues, and otherwise, decomposition is ended; and according to an optimal decomposition tree structure of the wavelet packet, adaptive single-branch reconstruction algorithm of the node signal is realized, and demodulated envelope spectrum analysis is carried out on the single-branch reconstruction signals. According to technical schemes of the invention, weak feature information of a fault bearing can be extracted from strong background noise effectively, and early faults of the bearing can be discovered.

Bearing fault diagnosis method based on adaptive redundancy lifting wavelet packet decomposition tree

The invention relates to a method for acquiring, extracting and diagnosing early stage fault characteristic signals of a gear case. According to the method, a sound emission sensor is mounted at a monitoring-needing portion of gear case equipment, a gear case bearing seat is generally selected, and the sound emission sensor is used for acquiring sound emission signals of the gear case in operation; noise-containing signals with different signal to noise ratios are selected to carry out singular spectrum slope calculation under different decomposition levels, a singular spectrum slope gradually increases along with increasing of the decomposition level, and the acquired sound emission signals are utilized to select an optimization decomposition level according to a decomposition level optimization realization process; according to the selected optimization decomposition level, the acquired sound emission signals are processed through redundant lifting wavelet analysis to acquire a time domain graph and a frequency domain graph of the signals; an equipment fault can be determined through analyzing the time domain graph and the frequency domain graph. The method is advantaged in that the early stage fault signals of the gear case can be simply and effectively extracted from the complex noise background, an equipment fault type can be further diagnosed through analysis, and long-term equipment operation is further facilitated.

Method for acquiring, extracting and diagnosing early stage fault characteristic signals of gear case

The invention relates to a mechanical rubbing fault feature extraction method based on multi-wavelet kernel-support vector regression (SVR). The method comprises the following steps: 1) a vibration signal of a mechanical key component is measured and taken, and the signal is subjected to redundant lifting scheme wavelet (RLSW) decomposition; 2) each layer of detail signals and the last layer of approximation signals obtained by decomposition are subjected to adaptive singular value decomposition (ASVD) noise reduction; 3) a signal is reconstructed by redundant lifting wavelets, and a noise reduction signal is acquired; 4) with two interval three-time Hermite spline multi-wavelet scale functions as interpolation basis functions, a novel multi-wavelet kernel function is constructed; and 5) the vibration signals after noise reduction are divided into a training set and a prediction set, the training set is used to construct a prediction model, the prediction set is predicted, and a weak periodic impact component hidden in the original vibration signal is extracted by the prediction residual. Thus, the mechanical rubbing fault features can be extracted.

Mechanical rubbing fault diagnosis method based on multi-wavelet kernel-support vector regression (SVR)

The invention relates to an adaptive redundant lifting wavelet noise reduction analysis-based bearing failure recognition method, belonging to the technical field of failure recognition of a rolling bearing. The technical scheme is that the method comprises the following steps of: (1) performing adaptive redundant lifting wavelet transformation of a bearing vibration signal; (2) performing variable-size threshold noise reduction processing on a high-frequency detail signal obtained by each decomposition process; (3) performing complete reverse reconstruction on a low-frequency approximation signal obtained by final decomposition and a high-frequency detail signal subjected to wavelet threshold noise reduction; and (4) performing Hilbert demodulation processing on a reconstructed signal to obtain an envelope spectrogram of an initial vibration signal, extracting and recognizing a frequency component in the spectrogram, and judging that a bearing fails if frequency conversion or failure characteristic frequency and even corresponding frequency multiplication occurs. The adaptive redundant lifting wavelet noise reduction analysis-based bearing failure recognition method has the beneficial effects that a threshold can be flexibly selected according to the characteristic of change of noise in a wavelet region, so that noise can be filtered better, and meanwhile, the completeness of a real signal can also be guaranteed as much as possible.

Adaptive redundant lifting wavelet noise reduction analysis-based bearing failure recognition method

The invention discloses a vibration and acoustic transmission signal regulation instrument for detecting mechanical faults, belongs to the technical field of mechanical equipment fault detection instruments and is used for detecting mechanical equipment faults. The vibration and acoustic transmission signal regulation instrument consists of a resonance modulation part and a normal signal processing part, wherein the resonance modulation part comprises a program controlled amplifying circuit, a high Q-factor band-pass filter, a secondary resonance circuit, an envelop detection circuit and anti-aliasing circuits; the normal signal processing part comprises a channel selection circuit and an anti-aliasing circuit; a sensor for detecting faults is connected with the program controlled amplifying circuit; the program controlled amplifying circuit is connected with the high Q-factor band-pass filter and the channel selection circuit respectively; the high Q-factor band-pass filter is connected with the secondary resonance circuit, the envelop detection circuit and the anti-aliasing circuit; the channel selection circuit is connected with the anti-aliasing circuits; and the anti-aliasing circuits are both connected with an A/D (analogue-to-digital) acquisition card. By using a vibration and acoustic transmission signal regulation technique, the vibration and acoustic transmission signal regulation instrument is good in effect of extracting fault signals of mechanical equipment and is precise in fault diagnosing.

Vibration and acoustic transmission signal regulation instrument for detecting mechanical faults

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Adaptive redundant lifting wavelet noise reduction analysis-based bearing failure recognition method

Vibration and acoustic transmission signal regulation instrument for detecting mechanical faults