3141 results about "Vibration signature" patented technology

A vibrational analysis system diagnosis the health of a mechanical system by reference to vibration signature data from multiple domains. Features are extracted from signature data by reference to pointer locations. The features provide an indication of signature deviation from a baseline signature in the observed domain. Several features applicable to a desired fault are aggregated to provide an indication of the likelihood that the fault has manifested in the observed mechanical system. The system may also be used for trend analysis of the health of the mechanical system.

A vibrational analysis system diagnosis the health of a mechanical system by reference to vibration signature data from multiple domains. Features are extracted from signature data by reference to pointer locations. The features provide an indication of signature deviation from a baseline signature in the observed domain. Several features applicable to a desired fault are aggregated to provide an indication of the likelihood that the fault has manifested in the observed mechanical system. The system may also be used for trend analysis of the health of the mechanical system.

A method for determining a burst pipe that requires immediate feeding-valve closing using acoustic and / or vibration sensors coupled to the pipe lor detecting signals, a microprocessor for analyzing signals from said sensors in real-time, a memory coupled to said microprocessor for storing data, including digital signatures acoustic and / or vibration signatures, as well as an actuator electrically coupled to said microprocessor and mechanically coupled to said feeding-valve. When the sensor signals match stored leak-signatures or when a persistent sensor signal produces no match, the system of the present invention initiates a valve closing. To improve detection, signals from other sensors, i.e. water sensors, smoke detectors, heat sensors or acoustic / vibration sensors mounted on other pipes are also used. Some of the said systems have IP addresses and can communicate over the Internet or wirelessly with others.

In embodiments of the present invention, a system and method of cytometry may include presenting a single sperm cell to at least one laser source configured to deliver light to the sperm cell in order to induce bond vibrations in the sperm cell DNA, and detecting the signature of the bond vibrations. The bond vibration signature is used to calculate a DNA content carried by the sperm cell which is used to identify the sperm cell as carrying an X-chromosome or Y-chromosome. Another system and method may include flowing cells past at least one QCL source one-by-one using a fluid handling system, delivering QCL light to a single cell to induce resonant mid-IR absorption by one or more analytes of the cell, and detecting, using a mid-infrared detection facility, the transmitted mid-infrared wavelength light, wherein the transmitted mid-infrared wavelength light is used to identify a cell characteristic.

A method of measuring and analyzing vibrations of a mechanical system, e.g., for performing gear and bearing fault detection within a system having a reference shaft and a plurality of rotating components. The method may include the steps of tabulating gear ratios of the plurality of rotating components relative to the reference shaft in integer form, establishing a measurement specification and communicating the measurement specification to a digitizer, recording vibration data from the mechanical system during operation, and resampling the recorded vibration data using the tabulated gear ratios to obtain vibration data corresponding to the rotating components and a vibration signature for a component of interest at predetermined angular increments.

The invention discloses a vibration monitoring-based wind generator set automatic fault diagnosis method. The method comprises the following steps: an acceleration sensor collects original vibration signals, a pre-communication subsystem stores and uploads data, and a central monitoring server receives data and stores the data in a database. The method also comprises the step of establishing a fault characteristic frequency library in the database which is used as the basis of fault diagnosis. The method of the invention can automatically realize the fault early warning and fault diagnosis of the key parts of the wind generator set; and the fault characteristic frequency pool can be continuously updated to realize the self-learning function of the method, thus improving the fault diagnosis function continuously.

The invention discloses an establishing method of a failure type prediction model. The establishing method comprises the following steps of: collecting a vibration signal of a fan, and meanwhile, collecting the audio signal of the fan; pretreating the vibration signal and carrying out feature extraction on the vibration signal after pretreating so as to obtain a vibration characteristic parameter, and pretreating the audio signal and carrying out feature extraction on the audio signal after pretreating so as to obtain an audio characteristic parameter; and substituting the vibration characteristic parameter to a failure type vibration template so as to obtain a failure type corresponding to the vibration characteristic parameter, and associating the audio characteristic parameter with the failure type so as to establish a failure type audio template. The invention also discloses an establishing system of the failure type prediction model and an establishing method and system of a malfunction probability prediction model as well as a fan monitoring pre-warning system and method. The fan monitoring early-warning system and method provided by the invention have the advantages that the sensitivity is high, and the failure monitoring is more exact.

The invention discloses a rolling bearing fault diagnosis method based on composite multi-scale permutation entropies, and belongs to the technical field of fault diagnosis. The method comprises the following steps: measuring a vibration signal of a faulty object; extracting composite multi-scale permutation entropies from the vibration signal; reducing the dimension of the composite multi-scale permutation entropies with use of a Laplacian score; taking the first multiple composite multi-scale permutation entropies with low scores after dimension reduction as fault feature vectors and dividing the fault feature vectors into multiple training samples and multiple test samples; inputting the multiple training samples into a multi-fault classifier established based on a support vector machine to perform learning so as to classify the test samples; and identifying the working mode and the fault type of the faulty object according to the classifying result. According to the fault diagnosis method disclosed by the invention, feature extraction is highly innovative, and the degree of identification is high in the process of fault mode identification.

A device and method can detect, and also break, an occurrence of gas lock in an electrical submersible pump assembly in a well bore based upon surface or downhole data without the need for operator intervention. To detect an occurrence of gas lock, an instantaneous value is monitored using a sensor. Then a controller compares the instantaneous value to a threshold value over a predetermined duration to thereby detect the occurrence of gas lock in the electrical submersible pump assembly. Sensors can include, for example, a differential pressure gauge, a pressure gage located in a pump stage located toward the inlet, a fluid temperature sensor located toward the discharge, a free gas detector located near the pump discharge, an electrical resistivity gage, a flow meter located within surface production tubing, and a vibration sensor attached to a tubing string to measure a vibration signature.

The invention discloses an online test method for a characteristic parameter of a bearing-rotor system. The online test method comprises the steps of: installing a signal acquisition system on the bearing-rotor system supported by a sliding bearing; regulating the rotation speed of a main shaft, and starting a drive motor and a signal acquisition instrument; acquiring vibration signals of specific positions in real time by an eddy current displacement sensor, and storing the vibration signals by the signal acquisition system; establishing a bearing-rotor system model by adopting a finite element method; and applying a characteristic parameter optimization method of the bearing-rotor system based on combination of machinery dynamics modeling with a genetic algorithm to ensure that a theoretical vibration state obtained through the simulation model is close to an actual measurement value, thereby realizing online solving of the rigidity and damping coefficient of the sliding bearing, the off set of a rotor and the like of the bearing-rotor system. Compared with the traditional method, the online test method disclosed by the invention has the obvious advantages that external excitation or multiple machine start / stop does not need to be performed on the bearing-rotor system, and the online test method is simple and reliable and has the characteristics of high efficiency, high stability and high precision.

The invention discloses a method and device for detecting defects and failures of a high-speed rail based on vibration signals, belonging to the technical field of railway safety monitoring and protection. The invention solves the problem that the operating efficiency of a train is seriously influenced when a current measurement device detects the defects and failures of the high-speed rail. The method comprises the following steps of: 1, acquiring a vibration signal of the high-speed rail by a vibration acceleration transducer, carrying out experience mode decomposition on the vibration signal, and acquiring n IMF (Intrinsic Mode Function) components and one residual error; 2, calculating a correlation coefficient ui (i=1, 2, ......, n) of the n IMF components acquired in the step 1 and the acquired vibration signal, and selecting the IMF component, the correlation coefficient ui of which is larger than a threshold lambda, as defect and failure feature information IMF; 3, respectively calculating power spectral density of the defect and failure feature information IMF acquired in the step 2; and 4, determining defect and failure parts of the high-speed rail according to the power spectral density of the defect and failure feature information IMF acquired in the step 3, and finishing the detection of the defects and failures of the high-speed rail.

An apparatus for analyzing the condition of a machine having a part rotatable with a speed of rotation, includesan input for receiving an analogue measurement signal indicative of a vibration signal signature having a vibration frequency and a repetition frequency;an A / D converter for generating a digital measurement signal dependent on the analogue measurement signal, the digital measurement signal having a first sample rate, which is at least twice the vibration frequency;an enveloper for generating an enveloped signal indicative of the repetition frequency;a decimator for generating a decimated first digital signal dependent on the enveloped signal such that the decimated first digital signal has a reduced sample rate;an enhancer having an input for receiving the decimated signal, wherein the enhancer operates in the time domain to perform discrete autocorrelation for the decimated first digital signal so as to generate an enhancer output signal sequence; andan analyzer for performing a condition monitoring function so as to analyze the condition of the machine dependent on the output signal sequence.

The invention provides a bearing fault characteristic extracting method for lifting wavelet transform based on self-adaptive fitting. The bearing fault characteristic extracting method is used for diagnosing faults of a rolling bearing through a vibration acceleration signal, and comprises the following steps of: creating nine wavelets with different characteristics through calculation formulas and lifting algorithms based on data fitting; carrying out redundant lifting wavelet transform on a vibration signal by using the nine created wavelets in sequence; determining the optimum and abandoning the other eight in nine groups of decomposition results according to a normalized 1P norm value; analyzing segmented power spectrums of the initial vibration acceleration signal; selecting the optimum low-frequency approximation signal or a high-frequency detail signal for single reconstruction; subjecting the signal obtained by the single reconstruction to Hilbert demodulation; and judging running state of the rolling bearing according to frequency components in an enveloping spectrum. According to invention, early weak fault characteristic information of the rolling bearing can be extracted more effectively, and evidences are provided for state monitoring and fault diagnosis of the rolling bearing, so that accidents can be avoided as possible.

A method for detecting vibration signatures in a reversible drive is disclosed. The method includes the steps of acquiring digital data representative of vibrations in the reversible drive and identifying and grouping together portions of data in a plurality of groups. The identified portions of data relate to a particular direction of travel of the reversible drive and the plurality of groups relate to travel in different directions of the reversible drive. The method can include the further step of processing at least one of the groups of data using signal processing techniques. A system and computer program product for practicing the method for detecting vibration signatures in a reversible drive are also disclosed.

A system that detects the onset of hard disk drive failure. During operation, the system measures vibrations from the hard disk drive to produce one or more vibration signals. Next, the system generates a vibration signature for the hard disk drive from the measured vibration signals. The system then determines if the vibration signature indicates the onset of hard disk failure by comparing the vibration signature with a reference vibration signature for the hard disk drive. If so, the system generates a warning or takes a remedial action.

An apparatus for monitoring of a device including a moveable part, especially a rotating device, wherein the apparatus includes a control module which receives a measured vibration signal of the device provided by a sensor connected to the device, provides a spectrum of the measured vibration signal, pre-processes the spectrum to determine base frequencies and side frequencies, where the base frequencies are frequencies having peak powers corresponding to eigen frequencies of the device or faulty frequencies and the side frequencies correspond to other frequencies, where the control module additionally processes the base and side frequencies by applying separately a one-class classification on the base and side frequencies, combines the results of the one-class classifications to obtain a classification signal representing a confidence level, and outputs a decision support signal based on the classification signal, where the decision support signal indicates an error status of the monitored device.

Vibration of an electric submersible pump assembly is monitored to produce a vibration spectrum. The vibration spectrum is compared to a known vibration signature for a pump condition that precedes gas lock. The pump condition is at least one of an impeller rotating stall condition, a diffuser stall condition, a pre-surge condition, and percentage of free gas within the wellbore fluid. Operation of the pump is then adjusted in response to the similarity of the vibration spectrum to the vibration signature for the pump condition to prevent impending gas lock.

The invention provides an opening-closing fault diagnosis method for an air circuit breaker based on vibration signals, wherein an acceleration sensor is used to collect machine body vibration signals generated during opening-closing courses of the air circuit breaker. The method comprises the steps that firstly, the acceleration sensor is used to collect the machine body vibration signals generated during opening-closing actions of the air circuit breaker and transform the vibration signals into digital signals, so that initial vibration signals are obtained; secondly, an improved wavelet packet threshold de-noising algorithm is used to process the collected vibration signals; thirdly, a complementary ensemble-average empirical mode decomposition algorithm is used to extract intrinsic mode function components from the de-noising vibration signals; fourthly, the quantity Z of the intrinsic mode function components is determined; fifthly, the intrinsic mode function components of the first Z orders are selected and extracted as sample entropies of a characteristic quantity; sixthly, binary tree multi-classifiers based on a relevance vector machine are established; and seventhly, the binary tree multi-classifiers based on the relevance vector machine obtained at the sixth step are used to establish a fault recognition model of the air circuit breaker.

A distributed optical fiber sensing signal processing method for safety monitoring of underground pipe network, which belongs to infrastructure safety monitoring field, which is aimed to improve the intelligent ability of detection and identification of the existing distributed optical fiber sound / vibration sensing system under complex application conditions. The present invention utilizes the distributed optical fiber sound / vibration sensing system to pick up the sound or vibration signal of the whole line along the detection cable; and the customized short time feature and long time feature are respectively extracted from the relative quantity of the sound or the vibration signal at each spatial point in the whole monitoring range. The Bayesian identification and classification network at each spatial point is constructed and trained based on the prior knowledge of the collected signal features and their different background noises.

The invention provides a method for diagnosing and predicating a rolling bearing based on a grey support vector machine. The method is characterized in that the rolling bearing is used as a key part of a mechanical device, and the advantages and disadvantages of the operation state influence the operation performances of the whole device. The method is the method for diagnosing and predicating the rolling bearing based on GM (1, 1)-SVM. The method comprises the steps of extracting a vibration signal time domain and frequency domain feature values of the rolling bearing under various fault and normal states; selecting important feature parameters to build a predicating model, namely, grey model; predicating the feature value; training a binary tree supporting vector machine according to various fault feature values and normal state feature values of the bearing; creating a rolling bearing decision making tree for determining the fault as well as classifying the fault type to diagnosis the fault of the bearing; then predicating the fault according to the predicating value and the trained supporting vector machine.

The invention discloses a method for predicting a bearing fault based on Gaussian process regression. The method comprises the following five steps of: step 1, setting prediction system parameters, initializing a Gaussian process regression model; step 2, collecting a bearing vibration signal regularly, extracting characteristics of a vibration signal to obtain time domain characteristic parameters of the bearing vibration signal, and carrying out fault symptom judgment; step 3, judging whether a fault symptom exists; step 4, calculating and storing the characteristic parameters, and carrying out dynamic updating of the Gaussian process regression model; and step 5, predicting the fault of a bearing. According to an actual use condition of a product, small amount of data is collected, time that the product possibly has the fault is given quantificationally, a calculation speed and prediction accuracy are improved by using the Gaussian process regression, a whole life cycle of the bearing is divided into three time ranges, such as a health time range, a sub-health time range and a fault time range by use of an idea of health management, fault prediction is carried out in the sub-health state, usage management capacity of the bearing is improved.

The invention discloses a milling cutter wear prediction method and a state recognition method. The wear prediction method comprises the following steps that firstly, wavelet noise reduction processing is performed on milling vibration data, feature extraction is performed on vibration signals from three aspects including time domain, frequency domain and time domain, after an initial feature vector set is obtained, a correlation coefficient method is used for calculating the correlation between feature vectors and wear amount, and an optimal feature vector set is obtained by screening; then,an average relative error predicted by a least squares support vector machine is defined as a fitness function of an adaptive step size cuckoo search algorithm, and by searching for a nest position, input parameters of the least squares support vector machine are optimized; finally, the wear amount is predicted by using the optimal least square support vector machine. Through comparison with two other hybrid intelligent algorithms, the superiority of an ASCS- LSSVR algorithm is verified.

The invention relates to a method for diagnosing faults of a planetary gear system of a wind driven generator under variable rotating speed. The method for diagnosing the faults of the planetary gear system of the wind driven generator under the variable rotating speed aims to overcome defects in a method for diagnosing variable rotating speed faults in the prior art. According to the method for diagnosing the faults of the planetary gear system of the wind driven generator under the variable rotating speed, a method that order data envelopment analysis is carried out on signals is adopted, namely, input (output) shaft rotating speed signals n(t) are obtained through a rotating speed sensor, then a fitting function (please see the formula in the specification) is obtained through fitting, a function (please see the formula in the specification) of changes of rotating angles along with time is obtained through integration, a time sequence tk of equal angle resampling is obtained through an interpolation method, interpolation is carried out on accelerated speed vibration signals Y(t) through the time sequence tk to obtain equal angle resampling signals (please see the symbol in the specification), then FTP is carried out on the envelop of the equal angle resampling signals (please see the symbol in the specification), and thus an order envelop spectrum can be obtained. The problem brought by the variable rotating speed can be well solved through the order analysis, interference of noise can be well restrained through envelop analysis, and thus the order envelop method can be well applied to fault diagnosis of the planetary gear system of the wind driven generator.

The invention relates to a device for testing dynamic characteristic parameters of a fixed joint surface of a single-degree-of-freedom system. The device comprises a base, a lower experimental plate, an experimental sample wafer, an upper experimental plate, a piezoelectric acceleration transducer, a bolt, a bracket, a vibration exciter, an impedance head, a charge amplifier, a digital analyzer, a power amplifier, an electronic computer, a normal coupling bolt hole, a lower experimental plate coupling bolt, a tangential coupling bolt hole and a flexible rope. Compared with the prior art, the device has the remarkable advantages of compact structure and clear testing principle; the dynamic characteristic parameters of a normal contact surface and a tangential contact surface can be simultaneously measured; the dynamic characteristic parameters of the contact surfaces in different load states can be measured by adjusting the pre-tightening force of the coupling bolt of the upper experimental plate and the lower experimental plate; the excitation force passes through the center of gravity of the upper experimental plate during measurement; vibration signals of the upper experimental plate and the lower experimental plate are simultaneously measured; the influence of the vibration signal of the base is eliminated during the calculation of a frequency response function of the system; and the device has the characteristics of high efficiency, stability and accuracy.

Vascular conditions are detected non-invasively in the human body using a collection of acoustic information from small local regions of the vasculature. An array of accelerometers or other sensors are attached to the head or other points of interest of a patient and blood flow sounds are recorded. Vibration signatures of vessel structures such as branches, aneurysms, stenosis, etc. using random, periodic, band limited or transient analysis provides a library for further processing. The signature library is used to localize the origin of the recognized vascular feature, and the localized feature is presented to the physician in a clinically relevant manner.

The invention discloses a sparse representation and dictionary learning-based bearing fault classification method. The method is characterized by comprising the following steps of: preprocessing acquired history bearing vibration signals to obtain vibration signals under different working states, respectively constructing corresponding sub-dictionaries and combining the sub-dictionaries into a redundant dictionary; and online acquiring a bearing vibration signal by using a sensor, solving a sparse coefficient of the signal under the redundant dictionary by using a generalized orthogonal matching pursuit algorithm, and classifying the vibration signal through a reconstruction error so as to identify the bearing working state. According to the method, relatively good classification effect can be obtained, the dictionary training process can be accelerated and the ability, of being adapted to target signals, of the dictionary is improved, so that the method is capable of realizing the sparse decomposition of complex vibration signals more efficiently and is used for fault identification.

The invention discloses a system and method for judging interior looseness of a power transformer. The system for judging and initially positioning interior looseness of the power transformer comprises a signal conditioning circuit, a data acquisition instrument, a data analysis module and three vibration sensors, wherein the signal conditioning circuit, the data acquisition instrument and the data analysis module are sequentially connected. The three vibration sensors are connected with the data acquisition instrument through the signal conditioning circuit and measure vibration signals of three phases of the power transformer correspondingly, the vibration signals are amplified, filtered and denoised by the signal conditioning circuit and then transmitted to the data acquisition instrument, the data acquisition instrument transmits the acquired vibration signals to the data analysis module, and the data analysis module stores and processes the vibration signals and carries out fault judgment. The looseness fault of the transformer is judged through 100 Hz signals in the vibration signals according to the characteristic of change of the vibration signals along with the looseness degree.

The invention discloses a time-frequency diagram processing method and system for mechanical equipment monitoring vibration signals. The time-frequency diagram processing method comprises the steps that firstly, linear or bilinear time-frequency transformation is carried out on collected mechanical equipment monitoring vibration signals to obtain a time-frequency diagram TFRs, and average threshold processing is carried out on the time-frequency diagram TFRs to obtain a sparse time-frequency diagram STFRs; secondly, time-frequency compression sampling is carried out on the STFRs in a random sampling mode to obtain a matrix M<STFR > with dimensionality being k times the dimensionality of an original matrix; thirdly, the parallel class FISTA proximal decomposition method is used for reconstructing an STFRs* through iterative computation. The time-frequency diagram processing system comprises a monitoring front-end machine and a server connected with the monitoring front-end machine. The monitoring front-end machine comprises an A / D data collection module, a time-frequency transformation module, an average thresholding module and a time-frequency compression sampling module and is connected with a vibration sensor. The server comprises an on-line data storage module and a reconstruction algorithm module. According to the time-frequency diagram processing method and system, the dimensionality of the reconstructed time-frequency diagram is greatly reduced, data storage and transmission are facilitated, and time-frequency analysis technology can be widely applied for mechanical equipment fault real-time quantitative analysis and diagnosis.

Vibrations at harmonic frequencies are reduced by injecting harmonic balancing signals into the armature of a linear motor / alternator coupled to a Stirling machine. The vibrations are sensed to provide a signal representing the mechanical vibrations. A harmonic balancing signal is generated for selected harmonics of the operating frequency by processing the sensed vibration signal with adaptive filter algorithms of adaptive filters for each harmonic. Reference inputs for each harmonic are applied to the adaptive filter algorithms at the frequency of the selected harmonic. The harmonic balancing signals for all of the harmonics are summed with a principal control signal. The harmonic balancing signals modify the principal electrical drive voltage and drive the motor / alternator with a drive voltage component in opposition to the vibration at each harmonic.