40 results about "Order tracking" patented technology

The invention relates to a gear fault positioning system and method, and belongs to the field of a fault diagnosis technology and a signal processing analysis technology. The gear fault positioning system comprises a signal acquisition module, a signal processing module and a feature extraction module. By use of an order tracking method, frequency fuzziness caused by a phenomenon can be effectively overcome, by use of an angle domain synchronization average technology, random interference components such as a bearing and the like in a gear case can be eliminated, and the purpose of noise reduction is achieved; by use of spectral line editing, the disadvantage of incapability of completely filtering unnecessary frequency components due to existence of a transition zone in a conventional band pass filter can be overcome, and accordingly, a frequency band of interest is accurately filtered; influences exerted by such external factors as noise, rotation speeds, load fluctuations and the like on a phase demodulation effect can be eliminated, abrupt change of signals after demodulation at gear fault positions is more obvious, and gear faults are quite accurately positioned; and the method is easy to master and use, the algorithm meets the real-time performance requirement of an industrial field, and through actual application tests, the algorithm is accurate and reliable.

The invention relates to a tachometer-free rotating machinery fault feature extraction method under a variable rotating speed. The method comprises the steps of firstly adopting a narrow-band filtering method to eliminate an interference frequency component and most random noise in the vibration data; then dividing the filtered vibration data into a plurality of sections by adopting a short-time sliding window method, further extracting the rotation frequency or the rotation frequency harmonic frequency of the vibration data in each short time period by utilizing a matching pursuit method, andfinally synthesizing the instantaneous rotation frequency or the instantaneous rotation frequency harmonic frequency of the vibration data in the whole time period; and performing order tracking analysis on the original vibration data according to the extracted instantaneous rotation frequency or instantaneous rotation frequency harmonic frequency to realize the fault feature extraction of a variable rotating speed machine without a tachometer. The instantaneous rotation frequency or instantaneous rotation frequency harmonic frequency extracted by the method designed by the invention is highin precision, and under the condition of small random noise interference, the precision of the extracted instantaneous rotation frequency or instantaneous rotation frequency harmonic frequency is close to the measurement precision of the tachometer, so that the accurate extraction of the variable rotating speed rotating machine fault features can be realized.

The invention discloses a rolling bearing fault detection method based on an actual measurement signal. The rolling bearing fault detection method comprises the steps of: firstly, converting a rollingbearing fault time domain vibration signal to an angle domain through employing an order tracking technology; secondly, carrying out parameter optimization on variational mode decomposition through adpopting a longicorn beard search algorithm, and decomposing all state vibration signals of a rolling bearing to obtain a series of intrinsic mode functions, wherein frequency band energy in differentintrinsic mode functions can change when different faults happen to the bearing; thirdly, extracting Renyi entropy features from modal components containing main fault information, and constructing afeature subset; and finally, using normal state vibration signals easy to obtain for training, extracting fault characteristic quantities, establishing fault data samples and incremental learning data samples, acquiring a fault recognition model through training by adopting a single-class support vector machine incremental learning algorithm, judging whether the rolling bearing breaks down or notaccurately, and achieving fault early warning.

The invention discloses an intelligent fault diagnosis method for a rotating machine at a time-varying rotating speed. Angular domain resampling is carried out on a time-domain vibration signal of a rolling bearing and a non-stable time-domain signal is converted into a stable angular domain signal, to so that the impact on the analysis of the vibration signal from the change of the rotating speedis eliminated; gabor transformation capable of well describing transient characteristics of drastically changing signals is used for a rotating speed estimation method based on time-frequency spectrum ridge line fitting; a keyless phase order tracking method without installing a rotating speed sensor is suitable for occasions where the rotating speed sensor cannot be installed. An LSTM model capable of adaptively extracting time sequence features without expert experience and domain knowledge is adopted, and due to the existence of a BN generalization layer, convergence of the model can be accelerated, over-fitting is prevented, the generalization ability of the model is improved, and adaptive intelligent fault diagnosis and identification of the rotating machine under the variable-rotating-speed working condition are realized.

A method (400) of determining blade tip deflection characteristics is applied to moving rotor blades (R1, R2) in a turbomachine (10) comprising a housing and rotor including a shaft with the rotor blades attached thereto and at least one proximity probe (202). The method (400) includes measuring ((402) a proximity signal caused by a presence of a proximate tip of a moving rotor blade (R1) and calculating (404) by a control module (212) a shaft Instantaneous Angular Position (IAP) as a function of time, and performing (410) an order tracking process which includes expressing (412) the measuredproximity signal in the angular domain and resampling (414) the expressed proximity signal to render it equidistant in the angular domain. The method (400) includes performing (416) a pulse localisation process which includes filtering (418) the proximity signal yielding a complex-valued response, expressing (420) the complex-valued response in terms of a local amplitude and phase, and calculating(422) local phase shifts between each expressed signal and a reference signal.

The invention discloses a wide-area order tracking method for monitoring a train running gear. The method comprises the following steps: obtaining a time-domain vibration signal sequence f (t) collected by a vibration sensor, and obtaining a key-phase vibration signal sequence f (theta) collected by a key-phase sensor at each key-phase phase point; according to the relationship between the key phase theta and the time t in f (theta) and f (t), adopting a spline interpolation method to construct a function relationship between any phase and the time t, and according to the relationship between the function relationship and the time domain vibration signal sequence f (t), adopting the spline interpolation method to construct a relationship between any phase and the vibration signal; obtaining a phase-vibration signal sequence, performing equal-angle resampling on the phase vibration signal sequence, and obtaining a resampled phase vibration signal sequence; and performing Fourier transform on the resampled phase vibration signal sequence to obtain the frequency and amplitude of the vibration signal. According to the invention, accurate order tracking is carried out on the characteristics of the train during running in a wide speed range of the train.

The invention discloses a generalized autocorrelation method for bearing fault feature extraction under a variable-speed working condition, which is combined with a period estimation method to realize balance between accumulative circulation and strong background noise and is used for bearing fault diagnosis under the variable-speed working condition. The method comprises the steps that an order tracking processing method is adopted, an original vibration signal is resampled in an order domain through instantaneous phase information, and the frequency modulation phenomenon is greatly weakened; a generalized autocorrelation method is adopted, correlation of a plurality of adjacent fragments is considered, and background noise is further weakened; on the basis that the original NRC method considers the correlation among all signal segments and cannot eliminate the influence of the accumulative periodic disturbance, only the correlation of a plurality of adjacent signal segments is considered, and the accumulation of the periodic disturbance is controlled. Compared with the traditional method, the method has the advantages that the difficulty caused by mutually restricted signal characteristics is overcome, and the effect is better.

The invention provides a fault diagnosis method and device for a wind generating set. The method comprises the following steps of: acquiring a current signal and vibration data of a wind generating set; determining the angular displacement of a generator of the wind generating set according to the current signal; determining a first-order spectrum of the wind generating set according to the angular displacement and the vibration data, a first coordinate axis of the first-order spectrum being used for representing the angular displacement, a second coordinate axis of the first-order spectrum being used for representing the vibration data, and the angular displacement comprising a plurality of unit angular displacements; carrying out interpolation processing on the first-order spectrum to enable the number of the vibration data corresponding to each unit angular displacement to meet a preset condition, so as to obtain a second-order spectrum; and analyzing the second-order spectrum by adopting a fault diagnosis algorithm. The non-rotating-speed order tracking mode is more accurate and stable, the system cost is reduced, and the precision and definition of fault diagnosis are improved through interpolation processing.

According to a bearing fault diagnosis method under the variable-speed working condition based on mobile equipment, carrier waves are emitted through a loudspeaker of the mobile equipment, amplitude demodulation is carried out on sound signals collected by a microphone, then instantaneous phase signals of a rotating shaft where a bearing is located are restored through maximum search and least square polynomial fitting, a spectral kurtosis method is used to extract a fault impact envelope to perform order tracking, and a bearing fault is judged through envelope order spectrum analysis. According to the method, test hardware outside mobile equipment does not need to be used, the test cost is reduced, the fault order of the bearing is irrelevant to the rotating speed, compared with a traditional envelope spectrum, the envelope order spectrum is easier to judge the occurrence and the type of the fault, the method is suitable for bearing diagnosis under the working condition of a fixed rotating speed, and a mobile phone program application form is beneficial to technical popularization and field personnel use.

The invention discloses a gear health monitoring index construction method based on an order sideband product spectrum, and the method comprises the steps: carrying out the angle domain resampling of a measured vibration acceleration signal through employing a calculation order tracking technology, and carrying out the Fourier transform of a resampling signal, and obtaining an order spectrum; calculating an order sideband product spectrum by using an order spectrum according to the order resolution and the tooth number information, designing a rectangular window by taking the gear tooth fault feature order as a center, and capturing corresponding gear tooth fault information in the order sideband product spectrum; finally, health monitoring indexes are calculated, and an alarm threshold value self-learning method is established by means of a statistical learning method based on data driving; according to the method, the influence of in-band noise on monitoring index construction is effectively inhibited through the order sideband product spectrum technology, the proposed alarm threshold self-learning method is simple and universal, the dependence on professional technicians is avoided, and gear transmission real-time health monitoring and threshold self-setting functions are realized.