1979 results about "Vibration acceleration" patented technology

The invention discloses a pump potential cavitation fault detecting method based on quick spectrum kurtosis analysis. The pump potential cavitation fault detecting method comprises the steps that 1, vibration acceleration signals are collected to be subject to noise reduction to serve as to-be-processed signals; 2, according to the data size of the signals, the resolving order of signal processingis determined; 3, according to the quick spectrum kurtosis algorithm computation result, the optimal carrier frequency and bandwidth are selected; 4, the selected signals within the carrier frequencyand bandwidth are subject to Fourier transformation, and a frequency spectrum envelope diagram is obtained; and 5, an original signal time domain figure, a signal time domain figure treated through quick spectrum kurtosis filtering processing and the frequency spectrum envelope diagram obtained after selected zone Fourier transformation are compared, and cavitation trouble signal time and frequency characteristics are analyzed. By means of the method, more cavitation instant signals can be detected, information in the aspects of time domain and frequency domain is seen more clearly, and the normal state and the cavitation state of a pump can be distinguished obviously.

The invention relates to an optimal damping control algorithm of a continuous-control-type semi-active suspension system, and the optimal damping control algorithm is researched and developed for meeting the taking comfort of people and automobile driving safety requirements well. The control algorithm comprises the following steps of: measuring automobile body vibration acceleration signals, automobile speed signals and rotation angle signals by utilizing a sensor; sensing the automobile driving road condition and suspension system damping ratio according to the signals measured by the sensor; according to the measured automobile body and automobile wheel vibration acceleration, obtaining the automobile body and automobile wheel vertical motion speed and the relative motion speed between an automobile body and automobile wheels; determining the optimal damping coefficient and the damping force of a shock absorber required under the current automobile speed and road condition according to automobile parameters, outputting stepping motor rotation angle control signals through a controller, and controlling and regulating the area of the damping throttling hole of a controllable shock absorber, so that the semi-active suspension system achieves the required optimal damping and damping force. The semi-active suspension optimal damping control algorithm provided by the invention is simple and easy to implement, has low requirements for the dynamic property of an actuating element, and is beneficial to the applications and popularization of the semi-active suspension.

Aspects of this invention include a downhole tool (such as a steering tool) including first and second sensor sets for measuring substantially instantaneous drill string rotation rates. Each of the sensor sets includes at least one accelerometer disposed to measure cross-axial acceleration components. Embodiments of this invention advantageously enable gravitational and tool shock / vibration acceleration components to be cancelled out, thereby improving accuracy. Moreover, exemplary embodiments of this enable stick / slip conditions to be detected and accommodated.

The invention discloses an integrated multi-channel synchronous vibration data acquisition, monitoring and analysis diagnosis device. The data acquisition unit comprises a signal conditioning circuit, an anti-aliasing filtering circuit, a multi-channel synchronous sampling chip, a system control logic unit, a pulse-shaping circuit (16), a phaselocking frequency multiplication chip and a microprocessor CPU, wherein the system control logic unit comprises a channel configuration logic unit, a clock generation logic unit, a rotating speed measurement logic unit, a sampling control logic unit and a frequency divider, and the data acquisition unit (1) is directly connected with a plurality of eddy current vibration displacement sensors, ICP type piezoelectric vibration acceleration sensors or magnetoelectric vibration speed sensors, and has an interface of a single-channel direct-connected eddy-current, photoelectric or magnetoelectric key phase / revolution speed sensor and so on. The CPU core board is connected with a computer or a palm computer through a network and a USB or a serial interface and finishes the data acquisition for monitoring and analysis diagnosis.

The invention provides a method and system used for monitoring the vibration states of impellers of wind generating sets. The method includes the steps that the vibration acceleration of a predetermined measuring point on a vane of each wind generating set in at least one direction is detected according to a preset detection period, data of operating parameters of at least one of the wind generating sets are synchronously acquired, and the acquired data of the operating parameters of the set and the detected vibration acceleration data are recorded; data sets of recorded operating parameter data and recorded vibration acceleration data exceeding the preset amount are selected; according to the operating parameters of the wind generating sets, vibration acceleration data detected when the wind generating sets are in abnormal working conditions are removed from the data sets; operating mode analysis is carried out on the vibration acceleration data detected at the measuring points of the vanes, at least one of the inherent frequency, the damping ratio and the vibration mode of each vane in the operating state is extracted, and whether the impellers of the wind generating sets break down or not are determined according to the extracted inherent frequency, damping ratio and vibration mode.

The invention discloses a wind-power gear box fault diagnosis method based on wavelet medium-soft threshold and electronic-magnetic diaphragm (EMD), which includes: A. measuring a wind-power gear box by utilizing an acceleration sensor to obtain vibration accelerator signals; B. performing wavelet analysis on the signals, performing de-noising preprocessing through a wavelet medium-soft threshold method to eliminate interfering noise and obtain de-noising signals; C. performing EMD analysis on the de-noising signals to obtain each intrinsic mode function (IMF) component; D. judging true and false of the IMF components according to the related coefficients between the IMF components and original signals, rejecting false IMF components, and analyzing true IMF components to obtain components containing fault characteristics; and E. obtaining envelope spectrums containing fault characteristic components to draw fault characteristics. The wind-power gear box fault diagnosis method combines wavelet medium-soft threshold de-noising and the EMD and is effective in fault characteristic information drawing.

The invention provides resonant vibratory sensors to render such resonant vibratory sensors more beneficial than conventional MEMS-based and non-MEMS-based resonant vibratory sensors for various usage applications, such as portable applications requiring navigation-grade performance. The resonant vibratory sensors include as examples an oscillator, a vibratory gyroscope and a vibratory accelerometer. In one embodiment, the resonant vibratory sensor is a disk resonator gyroscope. The improved resonant vibratory sensors employ materials having an ultra low thermal expansion coefficient, which provides an improved thermoelastic quality factor.

The invention provides a bearing fault diagnosis system which comprises an intelligent bearing provided with six output ports with a first output port and a second output port outputting vibration acceleration signals, a third output port and a fourth output port speed signals and a fifth output port and a sixth output temperature signals. The system is characterized in that: the six groups of signals output by the intelligent bearing are sent to the input port of an A / D conversion module, six groups of digital signals output by the A / D conversion module are sent to a processor internally provided with a status monitor and a fault recognizer, wherein, the status monitor monitors the status of the bearing according to the vibration acceleration digital signals, the speed digital signals and the temperature digital signals, and the fault recognizer utilizes a signal processing tool to get a fault judgment result. The system of the invention has the advantages of being capable of monitoring the status of the bearing in an on-line manner according to the three types of signals of the intelligent bearing on the basis of the current intelligent bearing provided with a compound sensor so as to judge which part a fault takes place in.

The invention relates to a wind turbine generator bearing fault diagnosis method for a multi-channel deep convolutional neural network. The method comprises the steps of: simultaneously acquiring high-frequency vibration acceleration signals of a drive end and a non-drive end of a test bearing in various states by using a vibration acceleration sensor; analyzing the acquired vibration signals by using a time frequency analysis technology to obtain corresponding time frequency spectra; establishing a deep convolutional neural network diagnosis model, and training the diagnosis model by using the time frequency spectra and the states of the bearing as a training sample; evaluating the diagnosis model, and applying the diagnosis model to a bearing to be monitored. The method can realize automatic feature learning, avoids feature engineering, effectively utilizes multi-channel vibration signals, and has good universality and extensibility.

The invention discloses a locomotive wheelset bearing fault diagnosis method based on the angular domain-time domain-frequency domain, and belongs to the technical field of bearing testing. The method includes the steps that firstly, collected vibration acceleration signals of locomotive wheelset bearings are analyzed in the time domain and the frequency domain, whether the bearings are in a fault state or not is judged, the specific portions of the faults are judged, angular domain analysis is conducted on vibration acceleration creatively, and quantitative analysis data of the faults are acquired, so that the severity degrees of the faults are grasped, and basic data are provided for further fault monitoring. The analysis method in which the time domain, the frequency domain and the angular domain are combined is adopted, information such as fault occurrence, fault types and fault severity degrees of the bearings can be accurately provided, especially, the angular domain fault diagnosis method is creatively provided, an accurate monitoring mode is provided for the fault development degree, and more abundant reference data are provided for subsequent fault processing.

The invention discloses an identification method of fundamental frequency for detection of the force of the cable of a stayed-cable bridge, which gets a first fundamental frequency by an autopower spectrum module, a second fundamental frequency by a cepstrum module, and then determines whether the quotient of the following two values is less than or equal to the set threshold: 1. the absolute value of the difference of the first fundamental frequency and the second fundamental frequency; 2. a half of the sum of the first fundamental frequency and the second fundamental frequency. In this way, whether the fundamental frequency of the pull cable is a half of the first fundamental frequency and the second fundamental frequency is determined. By adopting the method, the accuracy and the precision of the fundamental frequencies got are significantly improved. In addition, as the vibration acceleration response time interval signal got by an acceleration sensor passes through a signal conditioning module for filtration and smooth processing firstly, the environmental noise in the vibration acceleration response time interval signal is effectively suppressed; the antijamming capability of the stayed-cable bridge is improved so that the fundamental frequency is identified more clearly and accurately.

The invention relates to a multi-parameter compound environment tester comprising a centrifuge for simulating a linear acceleration environment and an environment chamber arranged on the arm of the centrifuge, wherein the environment chamber is used for simulating a vibration acceleration environment, a noise environment, a temperature environment and an air pressure environment; a noise generation mechanism, a temperature control mechanism, a vibration generation mechanism, an air inlet and an air outlet are arranged in the environment chamber; the vibration generation mechanism comprises a test bench, a bench push rod and a corrective push rod fixed on the test bench, a supporting reed and a sensor; the chamber wall of a compound chamber is hermetically connected with the push rods; the bench push rod is connected with a vibration-bench moving coil; and the corrective push rod is connected with a corrective mechanism capable of preventing the vibration generation mechanism and the vibration-bench moving coil from excessively translating. The invention has the advantages of comprehensive simulation of work environment for devices, good test effect and high precision.

The invention provides an engineering nonlinear vibration inspection method. The method adopts a wavelet nonlinear threshold value filtering wave to filter wide-frequency background noise of a three-direction acceleration signal and to remove DC bias so as to get the treated acceleration signals after frequency domain filtering and removal of trend term. Then speed signals and displacement signals are obtained after the acceleration signals is subject to a first time and second time integrals and removal of trend term. The characteristics and types of the nonlinear vibration are determined through a three-dimensional phase space diagram and other calculations of a reconstructed phase space tested by the three-dimensional phase space diagram. The invention adopts three-direction low resistance piezoelectricity acceleration meter to measure the vibration accelerations of three orthogonal directions. The invention has convenient installation, easy usage and reliable analysis, overcomes the problems of drifting, delay and aberration of the actual acceleration signals after passing through analog integration or digital integration. The invention plays an important role in the engineering nonlinear vibration inspection for mechanical mechanisms, bridges, dams and buildings, etc.

A vibration suppressing device and a vibration suppressing method that are capable of obtaining an accurate optimum rotation speed and shortening a time period from generation of chatter vibration to calculation of the optimum rotation speed are provided. The vibration suppressing device 10 includes: vibration sensors 2a to 2c for detecting time-domain vibrational accelerations of a rotary shaft 3 in rotation; and a control device 5 for calculating a chatter frequency and a frequency-domain vibrational acceleration of the rotary shaft 3 at the chatter frequency on the basis of the time-domain vibrational accelerations detected by the vibration sensors 2a to 2c, and when the calculated frequency-domain vibrational acceleration exceeds a predetermined threshold value, calculating an optimum rotation speed on the basis of a predetermined parameter, and rotating the rotary shaft 3 at the calculated optimum rotation speed.

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.

The invention aims to provide a method for analyzing and evaluating measure point precision of a gearbox in a vibration acceleration sensor, aiming at analyzing a plurality of sets of collected sample data at different measure point positions and a certain fixed position under various working conditions. The method comprises the following steps of: performing synchronous data collection of a vibration signal and a rotation speed signal of the gearbox by taking the rotation speed signal as the main control through the vibration acceleration sensor; extracting time domain characteristic values,analyzing and the evaluating the time domain characteristic values, and drawing an RMS (root mean square)-Peak picture and a box plot; calculating an order time spectrum and analyzing and evaluating the order time spectrum; and analyzing the result through time domain analysis and frequency domain analysis methods to form an analysis and evaluation result of the measure point precision of the gearbox. According to the method disclosed by the invention, the order time analysis on the vibration signal of a gearbox shell can be realized; optical positions better representing various fault characteristics can be obtained by analyzing the time domain and frequency domain characteristics; the measurement error is reduced; the installation position of the vibration sensor is determined; and an assurance is provided for the accuracy of the fault diagnosis.

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.

The invention discloses a rolling bearing failure diagnosis method base on vibration temporal frequency analysis. The method comprises the following steps: utilizing a vibration acceleration sensor to collect vibration signals of the rolling bearing under a normal condition and a failure condition; utilizing a modified inherent time scale resolving method to resolve the collected vibration signals, and generating a plurality of inherent time scale components and residual signals; calculating relativity of the time scale components and the vibration signals, selecting the inherent time scale components of which the relativity is ranked top 5 as related components, and rejecting noise signals and false components; calculating Wigner distribution of the related components respectively, and conducting linear stack to obtain the Wigner temporal frequency figure of the original signal; extracting difference fractal box dimensionality of the Wigner temporal frequency figure and the image entropy as failure characteristics; utilizing mahalanobis distance to build mapping relation of the failure characteristics and failure types to realize failure diagnosis. According to the invention, interference of Wigner distribution cross terms is avoided; two kinds of representative failure characteristics of the difference fractal box dimensionality and the image entropy are confirmed.

A relaxation apparatus which comprises a reclining chair for supporting thereon a whole body of a person who desires relaxation. The person resting on the reclining chair is cyclically vibrated at a frequency not higher than 25 Hz. A control is provided for controlling the vibrating device. The maximum absolute value of acceleration of the vibration produced by the vibrating device to vibrate the person supported on the reclining chair is not greater than 0.1 G. The control controls the acceleration in dependence on the frequency of vibrations outputted by the vibrating device such that the acceleration is small when the frequency of vibrations outputted by the vibrating device is low while the acceleration is large when the frequency of vibrations is high.

The invention discloses a reliability acceleration test method of electronic products, and belongs to the field of reliability designing and testing. The method comprises steps of firstly presetting an acceleration test temperature change curve boundary according to working stress limit obtained via a routine test for reliability of an electronic product, carrying out simulating calculation of temperature acceleration factors, and modifying and determining initial acceleration test temperature stress change curves; and secondly, calculating vibration acceleration factors according to the initial acceleration test temperature stress change curves and a test profile of the routine test for the reliability of the original electronic product, calculating acceleration test vibration stress change curves according to the vibration acceleration factors, and according to the acceleration test vibration stress change curves, determining the final temperature stress change curves and determining acceleration test time. According to the invention, by following the thought of testing and examining working stress of a product and properly increasing test stress level, test time is shortened; a reliability test of the electronic product can be accelerated; and diversity and controllability of the acceleration factors are achieved.

The reciprocal compressor is the key power equipment in petrochemical industry, but due to its complex structure and motion nature, the failure rate of reciprocal compressor in production is high. According the structural characteristics and monitoring targets of reciprocal compressor, the power-showing hole is equipped with a special pressure sensor which is high temperature resistant, high pressure resistant and fatigue resistant; the three directions of cross slideway box wall, cylinder side wall and cylinder cover wall are both equipped with vibration acceleration sensor; the piston rod box body is in-equipped with displacement sensor; the air intake pipe and the air discharge pipe are equipped with platinum resistance temperature sensor on themselves; the rotary axle is equipped with a key phase sensor. The signals of various sensors are transmitted into underlying computer to be stored after pass through amplifier and A / D converter. The amplifier, A / D converter and the underlying computer is laid in the explosion-proof tank on production site. The signals of underlying computer are inputted into up-lying computer through network to analysis and process data and diagnose faults. The present invention is characterized by establishing a set of practical reciprocal compressor state online motion and fault diagnosis system which on-site use is convenient and express, diagnosis accurate rate is high. As far as now, the system in same type has not been used on site, so it has very good application prospect.

The invention discloses a fiber grating vibrating sensor with temperature insensitivity, tunable damping and high precision. The fiber grating vibrating sensor comprises a cantilever beam. A permanent magnet is connected at the end portion of the cantilever beam. The upper side and the lower side of the permanent magnet are respectively provided with a sealing coil, wherein the sealing coils are the same in diameter and number of wire turns. The upper surface and the lower surface of the cantilever beam are respectively provided with a sensing grating, wherein the sensing gratings are symmetric. The sensing gratings are led out through transmission optical fibers. The gratings are stuck to the upper side and the lower side of the central axis of the cantilever beam with the constant strength respectively. Due to the fact that the cantilever beam can be stretched or compressed by vibration, the central wavelengths of the two sensing gratings can drift in the negative direction, the measurement of vibration acceleration can be achieved by demodulating the changes of the central wavelengths of the two sensing gratings, the signal measurement accuracy can be effectively improved by the adoption of the double-grating structure, the influences, caused by temperature changes, on the measurement result are eliminated, and the sensor has the temperature insensitivity.

A vibration suppressing device and a vibration suppressing method that are capable of obtaining an accurate optimum rotation speed and shortening a time period from generation of chatter vibration to calculation of the optimum rotation speed are provided. The vibration suppressing device 10 includes: vibration sensors 2a to 2c for detecting time-domain vibrational accelerations of a rotary shaft 3 in rotation; and a control device 5 for calculating a chatter frequency and a frequency-domain vibrational acceleration of the rotary shaft 3 at the chatter frequency on the basis of the time-domain vibrational accelerations detected by the vibration sensors 2a to 2c, and when the calculated frequency-domain vibrational acceleration exceeds a predetermined threshold value, calculating an optimum rotation speed on the basis of a predetermined parameter, and rotating the rotary shaft 3 at the calculated optimum rotation speed.

The invention provides a method for diagnosing faults of bearings on the basis of extreme learning machines, and belongs to the field of technologies for diagnosing mechanical faults. The method includes carrying out variation mode decomposition on vibration acceleration signals to obtain IMF (intrinsic mode function) mode components; acquiring singular values of the IMF mode components by the aid of singular value decomposition (SVD) algorithms; dividing the singular values of each IMF mode component into a training sample and a test sample; utilizing the singular values of the training samples as input values of extreme learning machine (ELM) neural network models and determining input connection weights, offset values and optimal output connection weights of the ELM neural network models; utilizing the singular values of the test samples as input values of the ELM neural network models with the determined input connection weights, the determined offset values and the determined optimal output connection weights and acquiring output results which are bearing fault diagnosis results. The method has the advantages that the signals can be accurately effectively separated, component signal modes are fast in convergence and high in robustness, and the method is high in fault recognition speed and accuracy; model building can be omitted, professional requirements can be lowered, and accordingly the method is suitable for industrial application.

The invention provides a gear failure keyless phase angle domain average computing order analysis method. The method comprises the steps that low-pass phase-protection filtering is conducted on collected accelerated speed signals, and time-frequency distribution of the signals is calculated through smooth pseudo Wigner-Ville distribution; the instantaneous frequency of a gear case rotary shaft is estimated according to a Viterbi optimal path search algorithm, key phase signals are used for estimating a model to conduct point-by-point integration on the instantaneous frequency, and an estimated key phase signal is obtained, calculation order analysis is conducted on the vibration accelerated speed signals through equal angle resampling and the angular domain average technology, and the order spectrum based on instantaneous frequency estimation is obtained. The order spectrogram can effectively reflect the feature information of gear case failures. The gear failure keyless phase angle domain average computing order analysis method integrates the advantages of the smooth pseudo Wigner-Ville distribution, the Viterbi optimal path search algorithm, the angular domain average technology and the calculation order analysis, and can effectively conduct fault diagnosis on a gear case working on the working station of variable speeds.

The invention provides a rotating machine fault diagnosis method based on Fast ICA-spectrum kurtosis-envelope spectrum analysis. The method includes the steps that (1), an acceleration sensor is used for obtaining vibration acceleration test signals of a rotating machine; (2), the test signals are separated in a decoupling mode by the adoption of a Fast ICA method with negentropy maximized; (3), the spectrum kurtosis of the separated signals is calculated, and the separated signals capable of representing fault information best are screened out; (4), Hilbert envelope spectrum analysis is performed on the selected separated signals; (5), the frequency corresponding to the peak value of an envelope spectrum is compared with the fault character frequency of a bearing, so that a concrete fault is diagnosed. The effectiveness of the method is verified well by the inner ring fault diagnosis of a rolling bearing of the rotating machine, the fault information is obviously increased, the precision of the fault diagnosis is greatly improved, the fault diagnosis method is easy to achieve and good in real-time performance, and it is illustrated that the rotating machine fault diagnosis method has good application prospects.

The invention is a random no-wear power detection technology about the stress state of anchor arm and there is installing a exciting force fasten instrument on the open end of the anchor fastened on the rock mass (coal) anchor arm anchor wire and on the open end of the anchor arm anchor wire installs an acceleration transducer connecting with the intelligent dynamic measurement equipment and make the anchor arm generated a lateral vibration by forcing to the exciting force fasten instrument and adopts the acceleration signal by the acceleration transducer and then transfer to the intelligent dynamic measurement equipment and the intelligent dynamic measurement equipment transfers the acceleration signal into the digital signal and stores it at last inputs the adopted data to the computer for computing and treating so it completes the detection of the inherent stress at the time of completed the initial installment and the detection of load at the sated of work and the monitor of forcing whole process, non-breakage, exhaustive, it can make no-wear power detection for random one anchor arm and the operation is easy and the use is convenience and speed and it is easy to carry and the effect is good and it has the extensive practicability.

The invention provides a vibration and strain signal on-line data acquisition and analysis device of a wind generating set. The device consists of an eight-channel synchronous acquisition circuit and an advanced reduced instruction-set computer (RISC) machine (ARM) main control module, wherein the eight-channel synchronous acquisition circuit and the ARM main control module are communicated with each other through an RS485 interface, can be used for performing dynamic signal acquisition on signals output by a vibration acceleration sensor and a strain sensor of the wind generating set, extracting characteristic parameters of strain data and vibration data and automatically recognizing the running state of a machine according to the configuration information of the device and can be used for performing storage management on the operating data of the wind generating set according to a strategy preset by a configuration, so that the integrity of data information during the abnormal running of the wind generating set is ensured and the mechanical running state of the wind generating set can be automatically monitored and analyzed; simultaneously, the ARM main control module is used for performing data transmission through a network interface and an external monitoring computer. The device has a small size, can be directly arranged on the wind generating set and can effectively improve the running maintainability of the wind generating set.

The invention relates to a rolling bearing error auto diagnose system. It takes testing analysis and kurtosis analysis to the bearing vibration, and calculates the kurtosis by virtual device and DSP, analyzes and compares the spectral configuration to accurately measure the acceleration and speed of bearing vibration, and achieve accurate analysis to automatically judge the place of the error.