CN114004306A - A device fault assessment system and method based on multi-dimensional data of the Internet of Things - Google Patents

A device fault assessment system and method based on multi-dimensional data of the Internet of Things Download PDF

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CN114004306A
CN114004306A CN202111325074.9A CN202111325074A CN114004306A CN 114004306 A CN114004306 A CN 114004306A CN 202111325074 A CN202111325074 A CN 202111325074A CN 114004306 A CN114004306 A CN 114004306A
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motor
fault
bearing
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vibration
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邓嘉明
侯跃恩
叶忠文
邓文锋
曾军
陈文飞
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Jiaying University
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    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F18/00—Pattern recognition
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Abstract

本发明公开一种基于物联网多维度数据的设备故障评估系统，采集电机工作过程中的混合声音、轴向振动、径向振动以及轴承温度等多维度数据并进行分析，以对电机故障进行综合评估。本发明通过采集电机的噪声、振动以及温度等多维度数据并进行多维度数据的分析，以获得多维度数据展示出的电机继续工作的电机维持危险评估系数，体现了电机故障后继续使用的危险程度，并实现对危险程度的量化展示，便于对电机故障进行综合评估和预测，达到提前预警和维护的作用。The invention discloses an equipment fault evaluation system based on multi-dimensional data of the Internet of Things, which collects and analyzes multi-dimensional data such as mixed sound, axial vibration, radial vibration and bearing temperature during the working process of the motor, so as to synthesize the fault of the motor. Evaluate. The invention collects multi-dimensional data such as noise, vibration and temperature of the motor and analyzes the multi-dimensional data, so as to obtain the motor maintenance risk assessment coefficient of the motor continuing to work displayed by the multi-dimensional data, which reflects the risk of continuous use of the motor after a fault. and realize the quantitative display of the degree of danger, which is convenient for comprehensive evaluation and prediction of motor faults, and achieves the role of early warning and maintenance.

Description

Equipment fault evaluation system and method based on multi-dimensional data of Internet of things
Technical Field
The invention belongs to the technical field of multidimensional data processing, and relates to an equipment fault evaluation system and method based on multidimensional data of the Internet of things.
Background
An electric machine, commonly known as a "motor", refers to an electromagnetic device that converts or transmits electric energy according to the law of electromagnetic induction, and the electric machine mainly functions in a circuit to generate driving torque as a power source for electrical appliances or various machines, and a generator mainly functions in a circuit to convert mechanical energy into electric energy.
At present, only the state of a motor is monitored and managed in the use process of the motor, which is generally represented as recording and storing the operation parameters of the motor, the failure in the operation process of the motor is not predicted, so that the accuracy and the reliability of the failure prediction in the use process of the motor are poor, meanwhile, the motor is interfered by various failures in the use process of the motor, the service life of the motor is influenced, but the judgment of the current motor failure is judged only by manual experience, experimental data are not available, the motor failure cannot be predicted in advance, the motor failure cannot be accurately evaluated, the service life of the motor which continuously works cannot be predicted according to the current failure of the motor, the defects of motor monitoring and failure evaluation are caused, the service life of the motor is shortened, and the motor cannot be maintained in time.
Disclosure of Invention
The invention aims to provide an equipment fault evaluation system based on multi-dimensional data of the Internet of things, which solves the problems in the prior art.
The purpose of the invention can be realized by the following technical scheme:
an equipment fault evaluation system based on multi-dimensional data of the Internet of things comprises a noise source division module, a vibration detection module, a bearing information detection module, a frequency spectrum characteristic classification processing module, a fault chain training interference module, a fault prediction judgment module and a multi-dimensional data evaluation module;
the noise source division module collects mixed sound in the working process of the motor in real time, and performs frequency spectrum analysis on the mixed sound in the working process of the motor by adopting Fourier transform to obtain the frequency spectrum characteristics of the separated bearing noise and the frequency spectrum characteristics of mechanical noise generated when the motor works;
the vibration detection module adopts an eddy current type displacement sensor and respectively collects the axial vibration amplitude and the radial vibration amplitude of the motor in real time; the bearing information detection module adopts a temperature sensor, is arranged on the bearing and is used for collecting the temperature of the surface of the bearing;
the frequency spectrum characteristic classification processing module receives the frequency spectrum characteristics of the bearing noise separated by the noise source distinguishing module and the frequency spectrum characteristics of the mechanical noise generated when the motor works, and compares the frequency spectrum characteristics of the bearing noise and the frequency spectrum characteristics of the mechanical noise with the frequency spectrum characteristics of the bearing noise under different bearing noise fault levels and the frequency spectrum characteristics of the mechanical noise under different mechanical noise fault levels in sequence to screen out the bearing noise fault level corresponding to the separated bearing noise and the mechanical noise fault level corresponding to the mechanical noise;
the method comprises the steps that a fault prediction judging module extracts an axial vibration amplitude and a radial vibration amplitude of a motor, a time domain vibration signal diagram is established according to the obtained axial vibration amplitude and the radial vibration amplitude, time domain vibration signals in the time domain vibration signal diagram are analyzed through Fourier transform, the frequency and the phase of axial vibration and the frequency and the phase of radial vibration are obtained, basic parameters of axial vibration and basic parameters of radial vibration are sequentially analyzed one by one, whether the motor vibration is abnormal or not is preliminarily predicted, the abnormal degree of the motor vibration is judged, the temperature of the surface of a bearing is received, the temperature of the surface of the bearing is drawn into a temperature change curve, the maximum speed of temperature rise of the surface of the bearing is counted, the accumulated time when the temperature of the bearing is larger than a preset temperature W, and the temperature of the surface of the bearing after the temperature of the bearing is larger than the preset temperature W are counted, and the estimated coefficient of a motor analysis shaft is determined through the abnormal degree of the motor vibration and the related parameter information of the temperature of the bearing (ii) a
The multidimensional data evaluation module extracts the bearing noise fault level and the mechanical noise fault level screened by the frequency spectrum characteristic classification processing module, sequentially screens a bearing fault evaluation coefficient corresponding to the bearing noise fault level and a mechanical fault evaluation coefficient corresponding to the mechanical noise fault level which are mapped with the bearing noise fault level and the mechanical noise fault level according to the bearing noise fault level and the mechanical noise fault level, acquires the motor vibration abnormal degree, the motor shaft-holding prediction coefficient and the bearing surface temperature after the bearing temperature is higher than a preset temperature W which are analyzed by the fault prediction judgment module, and predicts a motor maintenance danger evaluation coefficient of the current motor which continuously works by adopting a multidimensional data evaluation model.
Preferably, the method for determining the abnormal degree of vibration of the motor includes the following steps:
step 1, extracting the amplitude, frequency and phase of axial vibration and radial vibration;
step 2, judging whether the amplitude of the axial vibration is larger than k times of the amplitude of the radial vibration, if so, marking the risk factor of the amplitude of the motor vibration as lambda 1, and obtaining a preliminary numerical value of 1.32 through experiments, otherwise, marking as lambda 2, and obtaining a preliminary numerical value of 0.586 through experiments;
step 3, analyzing the ratio v between the axial vibration frequency f1 and the radial vibration frequency f2, and analyzing the phase difference psi (2 pi f) between the axial vibration phase and the radial vibration phase1T+w1)-(2πf2T + w2), T being time, w1 and w2 being the initial phase of axial vibration and the initial phase of radial vibration, respectively;
step 4, combining the data statistics in the step 2 and the step 3 to count the abnormal vibration coefficient
Figure BDA0003346654850000021
r is λ 1 or λ 2, v ═ f1/f 2.
Preferably, the motor shaft holding prediction coefficient
Figure BDA0003346654850000031
The calculation formula of (a) is as follows:
Figure BDA0003346654850000032
eta 1 represents a proportionality coefficient of motor shaft-holding caused by vibration, eta 2 represents a proportionality coefficient of motor shaft-holding caused by bearing temperature, eta 1+ eta 2 is 1, and beta1Expressed as the associated disturbance coefficient, beta, of the motor vibration anomaly caused by the bearing temperature2Expressed as the associated interference coefficient of the bearing temperature rise caused by the abnormal vibration of the motor, T is expressed as the accumulated time length of the bearing temperature being greater than the preset temperature W, TPreparation ofWhen the preset bearing temperature is higher than the upper limit of the preset temperature WLength, t1 and t2 respectively indicate a time point corresponding to the bearing temperature being equal to the preset temperature W and a time point corresponding to the temperature being greater than the preset temperature W, t2 being greater than t1, smaxExpressed as the maximum speed of temperature rise of the bearing surface, and W' expressed as the bearing surface temperature after the bearing temperature is greater than a preset temperature W.
Preferably, the multidimensional data evaluation module is based on comprehensive evaluation of data acquired by various sensors after processing, and the multidimensional data evaluation model is
Figure BDA0003346654850000033
a1, a2, a3 and a4 are respectively weight coefficients corresponding to bearing noise, mechanical noise, motor shaft seizure and bearing temperature fault types, a1+ a2+ a3+ a4 is 1, X and Y are respectively a bearing fault evaluation coefficient and a mechanical fault evaluation coefficient, E is a motor abnormal vibration coefficient, n is 4, T is T, andpreparation ofFor an upper limit duration for which the predetermined bearing temperature is greater than the predetermined temperature WmaxIs the maximum temperature that can be tolerated by the bearing surface,
Figure BDA0003346654850000034
is the accumulated amount of the bearing surface temperature over time after the bearing temperature is greater than the preset temperature W,
Figure BDA0003346654850000035
for the interference coefficient associated with the aj-th fault category to the ai-th fault category, i is 1,2,3,4, i.e. a1, a2, a3, a4 are bearing noise, mechanical noise, motor shaft seizure and bearing temperature anomaly, respectively, and when i is j,
Figure BDA0003346654850000036
equal to 0.
Preferably, the equipment fault evaluation system further includes a fault chain training interference module, the fault chain training interference module obtains the occurrence frequency of each fault type in the motor fault type set a { a1, a 2.,. ai.,. am } during training time, performs normalization analysis on the occurrence frequency of each fault type, and analyzes the weight of each fault type
Figure BDA0003346654850000037
And counting the interference influence times C among the fault types according to the occurrence sequence of the fault typesai→ajTo count the correlation interference coefficient between each fault category
Figure BDA0003346654850000041
XaiThe number of occurrences of the ai fault category over the training duration.
Preferably, the fault chain training interference module performs clustering processing on the interference influence times among fault types by adopting a clustering analysis method, and analyzes the correlation interference coefficient among fault types, and specifically comprises the following steps;
s1, after each fault type is simulated and trained for K times, the weight of each fault type in the training process is counted, and the weight of each fault type is equal to the ratio of the number of times of the fault type appearing in the K times of training to the sample training number of times K;
s2, primarily screening Z fault categories as clustering centers;
s3, establishing an objective function
Figure BDA0003346654850000042
Z is the number of clustering centers, Z is the number of fault categories,
Figure BDA0003346654850000043
the correlation interference influence degree between the fault class of the sample at the d-th time and the g-th clustering center is delta, which is the sum of the weights corresponding to all fault classes simulated and trained in the step S1, pdgDistance between the d sample fault test and the g cluster center, qdWeighting the fault type corresponding to the d sample fault test;
s4, respectively deducing an associated interference influence matrix and a clustering center iteration formula of the target function by adopting a Lagrange multiplier method:
Figure BDA0003346654850000044
Dgis the g fault speciesClass-corresponding cluster center, RgWeights corresponding to the training sample fault types to be classified;
s5, screening out the correlation interference coefficient between each fault type in the correlation interference influence matrix and the clustering center, and establishing a fault chain for each fault type with the correlation interference coefficient larger than 0.
Preferably, the equipment fault evaluation system further comprises a predictive tracking damage module, wherein the predictive tracking damage module is used for extracting a motor maintenance risk evaluation coefficient in the current motor working state, which is obtained by analysis of the multidimensional data evaluation module, and calculating the motor fault surge acceleration according to the motor maintenance risk evaluation coefficient in the current motor working state and the motor maintenance risk evaluation coefficient in the interval duration t3
Figure BDA0003346654850000045
And tracking and predicting the service life of the motor maintained by the continuous work of the motor according to the current motor fault surge coefficient
Figure BDA0003346654850000046
Gt3Maintaining a risk assessment factor for the motor at time t3, GmaxA risk assessment factor is maintained for the maximum motor allowed for the motor.
Preferably, the equipment fault assessment method based on the multidimensional data of the internet of things comprises the following specific steps:
s1, collecting the bearing temperature and the mixed noise in the motor operation process, and separating the mixed noise to obtain the bearing noise and the mechanical noise;
s2, screening the bearing noise and the mechanical noise to obtain the bearing noise fault level and the mechanical noise fault level;
s3, collecting the axial vibration amplitude and the radial vibration amplitude of the motor to establish a time domain vibration signal diagram, and analyzing the basic parameters of the axial vibration and the basic parameters of the radial vibration through the time domain vibration signal diagram;
s4, extracting basic parameters of axial vibration and radial vibration in the step S3 to judge the abnormal degree of the motor vibration;
s5, processing the temperature of the bearing surface to obtain the maximum speed of the temperature rise of the bearing surface, the accumulated time length of the bearing temperature being greater than the preset temperature W and the temperature of the bearing surface after the bearing temperature is greater than the preset temperature W;
s6, analyzing the seizure prediction coefficient of the motor by combining the data in the step S4 and the step S5, and judging the motor maintenance danger evaluation coefficient under the condition that the motor continues to work by adopting a multi-dimensional data evaluation model.
The invention has the beneficial effects that:
the motor maintenance risk assessment coefficient is maintained to the motor that this application was through collecting multidimensional data such as the noise of motor, vibration and temperature and carry out the analysis of multidimensional data to the motor that obtains multidimensional data to show continues to work, has embodied the dangerous degree of continuing to use after the motor trouble, and realizes the quantization show to dangerous degree, is convenient for carry out comprehensive assessment and prediction to the motor trouble, reaches early warning in advance and maintains the effect.
This application is through carrying out integrated processing to motor vibration and bearing temperature to predict out motor vibration and bearing temperature and produce the possibility that the motor embraced the axle, can embrace the axle to the motor in advance and predict and handle, reduce the probability that the motor embraced the axle, and then improve the durable degree of motor, avoid the motor to embrace the damage that the axle caused to the motor.
According to the method and the device, the various fault types causing the motor faults are subjected to cluster analysis to obtain the correlation interference coefficients among the various fault types, the quantitative degree of the correlation degree is provided for the mutual influence judgment among the faults, and then the reliable correlation basis is provided for the motor fault evaluation process.
The motor maintenance risk evaluation coefficients of two different time points are obtained, the motor fault surge acceleration is analyzed according to the motor maintenance risk evaluation coefficients of the two different time points, the service life of the motor maintained according to the current motor fault surge coefficient continuous work is predicted, the service life of the motor in the non-maintenance state is predicted, the accuracy of motor service life prediction is improved, and personnel are promoted to maintain motor faults in time.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
An equipment fault evaluation system based on multi-dimensional data of the Internet of things comprises a noise source division module, a vibration detection module, a bearing information detection module, a frequency spectrum characteristic classification processing module, a fault chain training interference module, a fault prediction judgment module and a multi-dimensional data evaluation module.
The motor is referred to equipment in this application, and the trouble kind of motor is many, and the trouble that the motor took place mainly derives from trouble kinds such as bearing noise, mechanical noise, motor armful of axle and bearing temperature anomaly, consequently this application focuses on the analysis of above four kinds of trouble, can protect the fault situation that the maintainer judged the motor and carry out failure assessment and prediction through the screening to above four kinds of trouble.
The noise source area division module collects mixed sound in the working process of the motor in real time, and performs frequency spectrum analysis on the mixed sound in the working process of the motor by adopting Fourier transform to obtain frequency spectrum characteristics of the separated bearing noise and frequency spectrum characteristics of mechanical noise generated when the motor works, wherein the frequency spectrum characteristics comprise amplitude, power and phase.
The vibration detection module adopts an eddy current type displacement sensor, the axial vibration amplitude and the radial vibration amplitude of the motor are respectively collected in real time, and the collected axial vibration amplitude and the collected radial vibration amplitude are sent to the fault prediction judgment module.
The bearing information detection module adopts a temperature sensor, is arranged on a bearing, collects the temperature of the surface of the bearing and sends the collected temperature of the surface of the bearing to the failure prediction judgment module.
The frequency spectrum characteristic classifying and processing module receives the frequency spectrum characteristic of the bearing noise separated by the noise source distinguishing module and the frequency spectrum characteristic of the mechanical noise generated when the motor works, and compares the frequency spectrum characteristic of the bearing noise and the frequency spectrum characteristic of the mechanical noise with the frequency spectrum characteristic of the bearing noise under different bearing noise fault levels and the frequency spectrum characteristic of the mechanical noise under different mechanical noise fault levels in sequence respectively to screen out the bearing noise fault level corresponding to the separated bearing noise and the mechanical noise fault level corresponding to the mechanical noise, so that the fault level classifying and processing corresponding to the sound in the frequency spectrum characteristic are realized, and the qualitative processing of the fault level is realized.
The method comprises the steps that a fault prediction judging module extracts an axial vibration amplitude and a radial vibration amplitude of a motor, a time domain vibration signal diagram is established according to the obtained axial vibration amplitude and the radial vibration amplitude, time domain vibration signals in the time domain vibration signal diagram are analyzed through Fourier transform, the frequency and the phase of axial vibration and the frequency and the phase of radial vibration are obtained, basic parameters of axial vibration and basic parameters of radial vibration are sequentially analyzed one by one, whether the motor vibration is abnormal or not is preliminarily predicted, the abnormal degree of the motor vibration is judged, the temperature of the surface of a bearing is received, the temperature of the surface of the bearing is drawn into a temperature change curve, the maximum speed of temperature rise of the surface of the bearing is counted, the accumulated time when the temperature of the bearing is larger than a preset temperature W, and the temperature of the surface of the bearing after the temperature of the bearing is larger than the preset temperature W are counted, and the estimated coefficient of a motor analysis shaft is determined through the abnormal degree of the motor vibration and the related parameter information of the temperature of the bearing The motor shaft holding prediction coefficient is used for reflecting the possibility that the motor is held under the common influence of motor vibration and bearing surface temperature received by the motor, the prior art only knows that the motor shaft holding is related to the motor vibration and the bearing temperature, but the motor vibration and the bearing surface temperature cannot be combined to carry out the prediction and evaluation of the motor shaft holding, and the comprehensive prediction judgment that the motor vibration and the bearing temperature are combined is realized, the possibility that the motor shaft holding is held can be relatively accurately analyzed, the prior art is prevented from only depending on manual experience, the motor cannot be predicted in advance, the possibility that the motor shaft holding is carried out is reduced, and the durability of the motor is improved.
When the motor vibrates abnormally, the motor bearing is indirectly caused to break down, so that the motor fault can be intuitively and accurately mastered by quantitatively judging the abnormal degree of the motor vibration.
The method for judging the abnormal degree of the motor vibration comprises the following specific steps:
step 1, extracting the amplitude, frequency and phase of axial vibration and radial vibration;
step 2, judging whether the amplitude of the axial vibration is larger than k times of the amplitude of the radial vibration, if so, marking the risk factor of the amplitude of the motor vibration as lambda 1, and obtaining a preliminary numerical value of 1.32 through experiments, otherwise, marking as lambda 2, and obtaining a preliminary numerical value of 0.586 through experiments;
step 3, analyzing the ratio v between the axial vibration frequency f1 and the radial vibration frequency f2, and analyzing the phase difference psi (2 pi f) between the axial vibration phase and the radial vibration phase1T+w1)-(2πf2T + w2), T being time, w1 and w2 being the initial phase of axial vibration and the initial phase of radial vibration, respectively;
step 4, combining the data statistics in the step 2 and the step 3 to count the abnormal vibration coefficient
Figure BDA0003346654850000071
r is lambda 1 or lambda 2, v is f1/f2, the abnormal vibration coefficient reflects the abnormal degree of the motor vibration, and the larger the abnormal vibration coefficient is, the larger the abnormal degree of the motor vibration is, the higher the possibility of axle seizure caused by the abnormal vibration of the motor is.
Wherein, the motor shaft holding pre-estimated coefficient
Figure BDA0003346654850000072
The calculation formula of (a) is as follows:
Figure BDA0003346654850000073
eta 1 represents a proportionality coefficient of motor shaft-holding caused by vibration, eta 2 represents a proportionality coefficient of motor shaft-holding caused by bearing temperature, eta 1+ eta 2 is 1, and beta1Expressed as the associated disturbance coefficient, beta, of the motor vibration anomaly caused by the bearing temperature2Expressed as the associated interference coefficient of the bearing temperature rise caused by the abnormal vibration of the motor, T is expressed as the accumulated time length of the bearing temperature being greater than the preset temperature W, TPreparation ofThe upper limit duration that the preset bearing temperature is greater than the preset temperature W is represented, t1 and t2 represent a time point corresponding to the bearing temperature being equal to the preset temperature W and a time point corresponding to the temperature being greater than the preset temperature W, t2 is greater than t1, and s is greater thanmaxExpressed as the maximum speed of temperature rise of the bearing surface, and W' expressed as the bearing surface temperature after the bearing temperature is greater than a preset temperature W.
The multidimensional data evaluation module extracts the bearing noise fault level and the mechanical noise fault level screened by the frequency spectrum characteristic classification processing module, sequentially screens a bearing fault evaluation coefficient corresponding to the bearing noise fault level and a mechanical fault evaluation coefficient corresponding to the mechanical noise fault level which are mapped with the bearing noise fault level and the mechanical noise fault level according to the bearing noise fault level and the mechanical noise fault level, acquires the motor vibration abnormal degree, the motor shaft holding prediction coefficient and the bearing surface temperature after the bearing temperature is higher than a preset temperature W which are analyzed by the fault prediction judgment module, predicts the current fault hazard degree of the motor by adopting a multidimensional data evaluation model, obtains a motor maintenance hazard evaluation coefficient G for the continuous work of the current motor, reflects the hazard degree of the continuous use of the motor after the motor breaks down so as to quantitatively display the hazard degree, the multidimensional data evaluation module adopts multidimensional detection data to comprehensively detect the motor, so that comprehensive data analysis is carried out by combining faults of bearing temperature, mechanical noise, bearing noise, motor shaft seizure and the like, comprehensive evaluation of motor faults is realized, and the harm degree of continuous work of the motor to the motor under the current motor fault can be optimally displayed.
The bearing noise fault level and the bearing fault evaluation coefficient are mapped with each other, the mechanical noise fault level and the mechanical fault evaluation coefficient are also mapped with each other, and the bearing fault evaluation coefficient and the mechanical fault evaluation coefficient respectively represent the probability of the motor fault under the bearing noise fault level and the probability of the motor fault under the mechanical noise level.
The multidimensional data evaluation module is used for realizing the acquisition and analysis of multidimensional data based on the comprehensive evaluation of the data acquired by various sensors after the data are processed so as to improve the evaluation accuracy of motor faults, and the multidimensional data evaluation model is
Figure BDA0003346654850000081
a1, a2, a3 and a4 are respectively weight coefficients corresponding to bearing noise, mechanical noise, motor shaft seizure and bearing temperature fault types, a1+ a2+ a3+ a4 is 1, X and Y are respectively a bearing fault evaluation coefficient and a mechanical fault evaluation coefficient, E is a motor abnormal vibration coefficient, n is 4, T is T, andpreparation ofFor an upper limit duration for which the predetermined bearing temperature is greater than the predetermined temperature WmaxIs the maximum temperature that can be tolerated by the bearing surface,
Figure BDA0003346654850000082
is the accumulated amount of the bearing surface temperature over time after the bearing temperature is greater than the preset temperature W,
Figure BDA0003346654850000083
for the interference coefficient associated with the aj-th fault category to the ai-th fault category, i is 1,2,3,4, i.e. a1, a2, a3, a4 are bearing noise, mechanical noise, motor shaft seizure and bearing temperature anomaly, respectively, and when i is j,
Figure BDA0003346654850000084
equal to 0.
Example two
And preliminarily analyzing the association degree among the fault types according to the sequence of the occurrence of the fault types, namely designing a fault chain training interference module.
The fault chain training interference module acquires the occurrence frequency of each fault type in a motor fault type set A { a1, a 2.,. ai.,. as, am } under the training duration, performs normalization analysis on the occurrence frequency of each fault type, and analyzes the weight of each fault type
Figure BDA0003346654850000091
And counting the interference influence times C among the fault types according to the occurrence sequence of the fault typesai→ajTo count the correlation interference coefficient between each fault category
Figure BDA0003346654850000092
XaiThe number of occurrences of the ai fault category over the training duration.
EXAMPLE III
Because the related faults are causally related before and after, when a certain fault occurs, another fault which is related mutually also occurs, in order to improve the accuracy of motor fault evaluation, a mean value clustering algorithm is adopted to cluster the interference influence times among fault types to obtain the related interference coefficients among the fault types, and compared with the second embodiment, the statistics of the related interference coefficients among the fault types is higher in accuracy, and artificial subjective factors are eliminated.
Simulating K sample fault tests for each fault type, and conveniently counting the times of the ai fault type causing the aj fault type of the motor when the ai fault type occurs to the motor so as to obtain the interference influence times C among the fault typesai→aj,Cai →aj≤K。
The fault chain training interference module adopts a clustering analysis method to cluster the interference influence times among fault types and analyzes the correlation interference coefficient among the fault types, and the method specifically comprises the following steps;
s1, after each fault type is simulated and trained for K times, the weight of each fault type in the training process is counted, and the weight of each fault type is equal to the ratio of the number of times of the fault type appearing in the K times of training to the sample training number of times K;
s2, primarily screening Z fault categories as clustering centers;
s3, establishing an objective function
Figure BDA0003346654850000093
Z is the number of cluster centers, Z isThe number of the types of the faults,
Figure BDA0003346654850000094
the correlation interference influence degree between the fault class of the sample at the d-th time and the g-th clustering center is delta, which is the sum of the weights corresponding to all fault classes simulated and trained in the step S1, pdgDistance between the d sample fault test and the g cluster center, qdWeighting the fault type corresponding to the d sample fault test;
s4, respectively deducing an associated interference influence matrix and a clustering center iteration formula of the target function by adopting a Lagrange multiplier method:
Figure BDA0003346654850000101
Dgfor the cluster center corresponding to the g-th fault category, RgWeights corresponding to the training sample fault types to be classified;
s5, screening out the correlation interference coefficient between each fault type in the correlation interference influence matrix and the clustering center, and establishing a fault chain for each fault type with the correlation interference coefficient larger than 0.
The mean value clustering algorithm is adopted to cluster the interference influence times among all fault types, so that the correlation interference degree among all fault types can be accurately analyzed, the quantitative embodiment of the correlation degree is provided for the mutual influence judgment among the faults, reliable correlation interference factors are provided for the motor fault evaluation, and the accuracy of the motor fault evaluation is further improved.
Example four
The prediction tracking damage module is used for extracting a motor maintenance risk evaluation coefficient in the current motor working state obtained by analysis of the multidimensional data evaluation module, and calculating the motor fault surge acceleration according to the motor maintenance risk evaluation coefficient in the current motor state and the motor maintenance risk evaluation coefficient under the interval duration t3
Figure BDA0003346654850000102
And tracking and predicting the service life of the motor maintained by the continuous work of the motor according to the current motor fault surge coefficient
Figure BDA0003346654850000103
Gt3Maintaining a risk assessment factor for the motor at time t3, GmaxAnd maintaining the danger evaluation coefficient for the maximum motor allowed by the motor, wherein the larger the motor maintenance danger evaluation coefficient of the motor in the state of the motor is, the higher the danger possibility of the motor for continuous use is indicated.
EXAMPLE five
An equipment fault assessment method based on multi-dimensional data of the Internet of things comprises the following specific steps:
s1, collecting the bearing temperature and the mixed noise in the motor operation process, and separating the mixed noise to obtain the bearing noise and the mechanical noise;
s2, screening the bearing noise and the mechanical noise to obtain the bearing noise fault level and the mechanical noise fault level;
s3, collecting the axial vibration amplitude and the radial vibration amplitude of the motor to establish a time domain vibration signal diagram, and analyzing the basic parameters of the axial vibration and the basic parameters of the radial vibration through the time domain vibration signal diagram;
s4, extracting basic parameters of axial vibration and radial vibration in the step S3 to judge the abnormal degree of the motor vibration;
s5, processing the temperature of the bearing surface to obtain the maximum speed of the temperature rise of the bearing surface, the accumulated time length of the bearing temperature being greater than the preset temperature W and the temperature of the bearing surface after the bearing temperature is greater than the preset temperature W;
and S6, analyzing the seizing prediction coefficient of the motor by combining the data in the step S4 and the step S5, and judging the motor maintenance danger evaluation coefficient under the continuous work of the current motor by adopting a multi-dimensional data evaluation model so as to reflect the damage to the motor caused by the continuous work of the motor.
The foregoing is merely exemplary and illustrative of the principles of the present invention and various modifications, additions and substitutions of the specific embodiments described herein may be made by those skilled in the art without departing from the principles of the present invention or exceeding the scope of the claims set forth herein.

Claims (8)

1.一种基于物联网多维度数据的设备故障评估系统，其特征在于：包括噪音源区分模块、振动检测模块、轴承信息检测模块、频谱特征归类处理模块、故障链训练干扰模块、故障预测判定模块和多维度数据评估模块；1. An equipment fault assessment system based on multi-dimensional data of the Internet of Things, characterized in that: it comprises a noise source discrimination module, a vibration detection module, a bearing information detection module, a spectrum feature classification processing module, a fault chain training interference module, and a fault prediction module. Judgment module and multi-dimensional data evaluation module; 噪音源区分模块实时采集电机工作过程中的混合声音，并采用傅里叶变换对电机工作过程中的混合声音进行频谱分析，得到分离后的轴承噪音的频谱特征和电机工作时产生的机械噪声的频谱特征；The noise source distinguishing module collects the mixed sound in the working process of the motor in real time, and uses the Fourier transform to analyze the spectrum of the mixed sound during the working process of the motor, and obtains the spectral characteristics of the separated bearing noise and the mechanical noise generated when the motor is working. spectral characteristics; 振动检测模块采用涡流式位移传感器，分别实时采集电机轴向振动幅值和径向振动幅值；轴承信息检测模块采用温度传感器，安装在轴承上，对轴承表面的温度进行采集；The vibration detection module adopts an eddy current displacement sensor, which collects the axial vibration amplitude and radial vibration amplitude of the motor in real time respectively; the bearing information detection module adopts a temperature sensor, which is installed on the bearing and collects the temperature of the bearing surface; 频谱特征归类处理模块接收噪音源区分模块分离后的轴承噪音的频谱特征和电机工作时产生的机械噪声的频谱特征，将轴承噪音的频谱特征和机械噪声的频谱特征依次分别与对应的不同轴承噪声故障等级程度下的轴承噪音的频谱特征和不同机械噪声故障等级的机械噪声的频谱特征进行对比，以筛选出分离后的轴承噪音所对应的轴承噪声故障等级和机械噪声所对应的机械噪声故障等级；The spectral feature classification processing module receives the spectral features of the bearing noise separated by the noise source distinguishing module and the spectral features of the mechanical noise generated when the motor is working, and sequentially associates the spectral features of the bearing noise and the mechanical noise with the corresponding different bearings. The spectral characteristics of the bearing noise at the level of the noise failure level are compared with the spectral characteristics of the mechanical noise of different mechanical noise fault levels, so as to screen out the bearing noise fault level corresponding to the separated bearing noise and the mechanical noise fault corresponding to the mechanical noise. grade; 故障预测判定模块提取电机的轴向振动幅值和径向振动幅值，根据获取的轴向振动幅值和径向振动幅值建立时域振动信号图，并采用傅里叶变换对时域振动信号图中的时域振动信号进行分析，得到轴向振动的频率和相位以及径向振动的频率和相位，依次将轴向振动的基本参数与径向振动的基本参数进行逐一相对分析，初步预测出电机振动是否异常以及对电机振动异常程度进行判定，并接收轴承表面的温度，将轴承表面的温度绘制成温度变化曲线，统计轴承表面的温度上升的最大速度、轴承温度大于预设温度W的累计时长以及轴承温度大于预设温度W后的轴承表面温度，通过电机振动异常程度判定量以及轴承温度相关参数信息分析出电机抱轴预估系数；The fault prediction and determination module extracts the axial vibration amplitude and radial vibration amplitude of the motor, establishes a time domain vibration signal map according to the obtained axial vibration amplitude and radial vibration amplitude, and uses Fourier transform to analyze the time domain vibration. The time domain vibration signal in the signal diagram is analyzed to obtain the frequency and phase of the axial vibration and the frequency and phase of the radial vibration. Determine whether the vibration of the motor is abnormal and determine the degree of abnormal vibration of the motor, and receive the temperature of the bearing surface, draw the temperature of the bearing surface into a temperature change curve, and count the maximum speed of the temperature rise of the bearing surface, and the bearing temperature is greater than the preset temperature W. The accumulated time and the bearing surface temperature after the bearing temperature is greater than the preset temperature W, the motor shaft holding prediction coefficient is analyzed through the judgment amount of the abnormal degree of motor vibration and the parameter information related to the bearing temperature; 多维度数据评估模块提取频谱特征归类处理模块筛选出的轴承噪声故障等级和机械噪声故障等级，根据轴承噪声故障等级和机械噪声故障等级依次筛选出与之相互映射的轴承噪声故障等级所对应的轴承故障评定系数以及机械噪声故障等级所对应的机械故障评定系数，并获取故障预测判定模块分析的电机振动异常程度、电机抱轴预估系数以及轴承温度大于预设温度W后的轴承表面温度，采用多维度数据评估模型预测当前电机继续工作的电机维持危险评估系数。The multi-dimensional data evaluation module extracts the bearing noise fault level and the mechanical noise fault level screened out by the frequency spectrum feature classification processing module. The bearing fault evaluation coefficient and the mechanical fault evaluation coefficient corresponding to the mechanical noise fault level are obtained, and the abnormal degree of motor vibration analyzed by the fault prediction and judgment module, the motor shaft holding prediction coefficient, and the bearing surface temperature after the bearing temperature is greater than the preset temperature W are obtained, The multi-dimensional data evaluation model is used to predict the motor maintenance risk assessment coefficient that the current motor continues to work. 2.根据权利要求1所述的一种基于物联网多维度数据的设备故障评估系统，其特征在于：所述电机振动异常程度的判定方法，具体步骤如下所示：2. a kind of equipment fault assessment system based on multi-dimensional data of Internet of Things according to claim 1, is characterized in that: the judging method of described motor vibration abnormality degree, concrete steps are as follows: 步骤1、提取轴向振动和径向振动的幅值、频率和相位；Step 1. Extract the amplitude, frequency and phase of axial vibration and radial vibration; 步骤2、判断轴向振动的幅值是否大于k倍的径向振动的幅值，若大于k倍的径向振动的幅值，则标记电机振动的幅值危险因子为λ1，实验获取的初步数值为1.32，反之，则标记为λ2，实验获取的初步数值为0.586；Step 2. Determine whether the amplitude of axial vibration is greater than k times the amplitude of radial vibration. If it is greater than k times the amplitude of radial vibration, mark the amplitude risk factor of motor vibration as λ1. The value is 1.32, otherwise, it is marked as λ2, and the preliminary value obtained by the experiment is 0.586; 步骤3、分析轴向振动频率f1与径向振动频率f2间的比值v，以及分析轴向振动的相位与径向振动的相位差ψ，ψ＝(2πf1T+w1)-(2πf2T+w2)，T为时间，w1和w2分别为轴向振动的初始相位和径向振动的初始相位；Step 3. Analyze the ratio v between the axial vibration frequency f1 and the radial vibration frequency f2, and analyze the phase difference ψ of the axial vibration and the radial vibration, ψ=(2πf 1 T+w1)-(2πf 2 T +w2), T is time, w1 and w2 are the initial phase of axial vibration and the initial phase of radial vibration, respectively; 步骤4、结合步骤2和步骤3中的数据统计异常振动系数
Figure FDA0003346654840000021
r为λ1或λ2，v＝f1/f2。Step 4. Combine the data in Step 2 and Step 3 to calculate the abnormal vibration coefficient
Figure FDA0003346654840000021
r is λ1 or λ2, and v=f1/f2. 3.根据权利要求2所述的一种基于物联网多维度数据的设备故障评估系统，其特征在于：所述电机抱轴预估系数
Figure FDA0003346654840000022
的计算公式如下：3. The equipment failure assessment system based on multi-dimensional data of the Internet of Things according to claim 2, characterized in that: said motor shaft holding prediction coefficient
Figure FDA0003346654840000022
The calculation formula is as follows:
Figure FDA0003346654840000023
η1表示为振动造成的电机抱轴的比例系数，η2表示为轴承温度造成电机抱轴的比例系数，η1+η2＝1，β1表示为由轴承温度产生电机振动异常的关联干扰系数，β2表示为由电机振动异常造成轴承温度升高的关联干扰系数，T表示为轴承温度大于预设温度W的累计时长，T预表示为预设的轴承温度大于预设温度W的上限时长，t1和t2分别表示为轴承温度等于预设温度W所对应的时间点以及温度大于预设温度W所对应的某一时间点，t2大于t1，smax表示为轴承表面的温度上升的最大速度，W′表示为轴承温度大于预设温度W后的轴承表面温度。
Figure FDA0003346654840000023
η1 is the proportional coefficient of the motor holding the shaft caused by vibration, η2 is the proportional coefficient of the motor holding the shaft caused by the bearing temperature, η1+η2=1, β 1 is the correlation interference coefficient of abnormal motor vibration caused by the bearing temperature, β 2 It is expressed as the related interference coefficient of the bearing temperature rise caused by abnormal vibration of the motor, T is the cumulative time period when the bearing temperature is greater than the preset temperature W, T is pre- expressed as the upper limit time when the preset bearing temperature is greater than the preset temperature W, t1 and t2 is respectively expressed as the time point corresponding to the bearing temperature equal to the preset temperature W and a certain time point corresponding to the temperature greater than the preset temperature W, t2 is greater than t1, smax is expressed as the maximum speed of the temperature rise of the bearing surface, W′ It is expressed as the bearing surface temperature after the bearing temperature is greater than the preset temperature W. 4.根据权利要求3所述的一种基于物联网多维度数据的设备故障评估系统，其特征在于：所述多维度数据评估模块是基于对多种传感器采集的数据进行处理后的数据的综合评估，所述多维度数据评估模型为
Figure FDA0003346654840000024
a1,a2,a3,a4分别为轴承噪声、机械噪声、电机抱轴以及轴承温度故障种类所对应的权重系数，a1+a2+a3+a4＝1，X和Y分别为轴承故障评定系数、机械故障评定系数，E为电机异常振动系数，n为4，T预为为预设的轴承温度大于预设温度W的上限时长，Wmax为轴承表面可允许的最大温度，
Figure FDA0003346654840000031
为轴承温度大于预设温度W后的轴承表面温度随时间的累计量，
Figure FDA0003346654840000032
为第aj个故障种类对第ai个故障种类的关联干扰系数，i＝1,2,3,4,即a1,a2,a3,a4分别为轴承噪声、机械噪声、电机抱轴以及轴承温度异常，当i＝j时，
Figure FDA0003346654840000033
等于0。4. A device failure evaluation system based on multi-dimensional data of the Internet of Things according to claim 3, wherein the multi-dimensional data evaluation module is based on the synthesis of data after processing the data collected by various sensors Evaluation, the multi-dimensional data evaluation model is
Figure FDA0003346654840000024
a1, a2, a3, a4 are the weight coefficients corresponding to the bearing noise, mechanical noise, motor shaft holding and bearing temperature fault types, respectively, a1+a2+a3+a4=1, X and Y are the bearing fault evaluation coefficient, mechanical The fault evaluation coefficient, E is the abnormal vibration coefficient of the motor, n is 4, T is the preset bearing temperature is greater than the upper limit of the preset temperature W, W max is the maximum allowable temperature of the bearing surface,
Figure FDA0003346654840000031
is the cumulative amount of bearing surface temperature over time after the bearing temperature is greater than the preset temperature W,
Figure FDA0003346654840000032
is the correlation interference coefficient of the ajth fault type to the aith fault type, i=1, 2, 3, 4, that is, a1, a2, a3, and a4 are the bearing noise, mechanical noise, motor shaft holding and abnormal bearing temperature, respectively , when i=j,
Figure FDA0003346654840000033
equal to 0. 5.根据权利要求4所述的一种基于物联网多维度数据的设备故障评估系统，其特征在于：所述设备故障评估系统还包括故障链训练干扰模块，故障链训练干扰模块获取训练时长下电机故障种类集A{a1,a2,...,ai,...,am}中各故障种类出现的次数，对各故障类型出现的次数进行归一化分析，分析出各故障种类的权值
Figure FDA0003346654840000034
并根据各故障种类出现的先后顺序统计各故障种类间的干扰影响次数Cai→aj，以统计出各故障种类间的关联干扰系数
Figure FDA0003346654840000035
Xai为ai故障种类在训练时长下出现的次数。5. A device failure assessment system based on multi-dimensional data of the Internet of Things according to claim 4, characterized in that: the device failure assessment system further comprises a failure chain training interference module, and the failure chain training interference module obtains the training duration The number of occurrences of each fault type in the motor fault type set A{a1,a2,...,ai,...,am}, normalize the occurrence times of each fault type, and analyze the weight of each fault type. value
Figure FDA0003346654840000034
And according to the order of occurrence of each type of fault, the number of interference effects C ai→aj among various types of faults is counted, so as to count the correlation interference coefficient between various types of faults
Figure FDA0003346654840000035
X ai is the number of times ai fault types appear under the training time. 6.根据权利要求4所述的一种基于物联网多维度数据的设备故障评估系统，其特征在于：所述故障链训练干扰模块采用聚类分析方法对各故障类型间的干扰影响次数进行聚类处理，分析出各故障种类间的关联干扰系数，具体包括以下步骤；6 . The equipment fault assessment system based on multi-dimensional data of the Internet of Things according to claim 4 , wherein the fault chain training interference module adopts a cluster analysis method to cluster the number of interference effects between the fault types. 7 . Class processing, analyzing the correlation interference coefficient between various fault types, including the following steps; S1、对每个故障种类模拟训练K次后，统计各故障种类在本次训练过程中的权重，各故障种类的权重等于在K次训练中出现该故障种类的次数与样本训练次数K的比值；S1. After simulating training K times for each fault type, count the weight of each fault type in this training process, and the weight of each fault type is equal to the ratio of the number of times the fault type occurs in the K times of training to the number of sample training K ; S2、初步筛选出Z个故障种类作为聚类中心；S2. Preliminarily screen out Z fault types as cluster centers; S3、建立目标函数
Figure FDA0003346654840000036
Z为聚类中心数目，Z为故障种类数量，
Figure FDA0003346654840000037
为第d次样本故障种类与第g个聚类中心的关联干扰影响度，δ为步骤S1中模拟训练的所有故障种类对应的权重和，pdg为第d次样本故障试验与第g个聚类中心的距离，qd为第d个样本故障试验所对应的故障种类的权重；S3, establish the objective function
Figure FDA0003346654840000036
Z is the number of cluster centers, Z is the number of fault types,
Figure FDA0003346654840000037
is the correlation interference influence degree between the d-th sample fault type and the g-th cluster center, δ is the weight sum corresponding to all fault types simulated and trained in step S1, p dg is the d-th sample fault test and the g-th cluster center The distance from the class center, q d is the weight of the fault type corresponding to the d-th sample fault test; S4、采用拉格朗日乘子法分别推导出目标函数的关联干扰影响矩阵和聚类中心迭代公式：
Figure FDA0003346654840000041
Dg为第g个故障种类对应的聚类中心，Rg为待分类的训练样本故障种类所对应的权重；S4. Use the Lagrange multiplier method to deduce the correlation interference matrix and cluster center iteration formula of the objective function respectively:
Figure FDA0003346654840000041
D g is the cluster center corresponding to the g-th fault type, and R g is the weight corresponding to the fault type of the training sample to be classified; S5、筛选出关联干扰影响矩阵中各故障种类与聚类中心的关联干扰系数，并将关联干扰系数大于0的各故障种类建立故障链。S5. Screen out the correlation interference coefficients of each fault type and the cluster center in the correlation interference influence matrix, and establish a fault chain for each fault type whose correlation interference coefficient is greater than 0. 7.根据权利要求5或6所述的一种基于物联网多维度数据的设备故障评估系统，其特征在于：所述设备故障评估系统还包括预测追踪损坏模块，预测追踪损坏模块用于提取多维度数据评估模块分析获得的当前电机工作状态下的电机维持危险评估系数，根据当前电机状态下的电机维持危险评估系数以及间隔时长t3下的电机维持危险评估系数计算电机故障激增加速度
Figure FDA0003346654840000042
并追踪预测电机按照当前电机故障激增系数持续工作所维持的寿命
Figure FDA0003346654840000043
Gt3为t3时间点下的电机维持危险评估系数，Gmax为电机所允许的最大电机维持危险评估系数。7. The equipment failure assessment system based on the multi-dimensional data of the Internet of Things according to claim 5 or 6, characterized in that: the equipment failure assessment system further comprises a prediction and tracking damage module, and the prediction and tracking damage module is used to extract the multidimensional data. The dimensional data evaluation module analyzes and obtains the motor maintenance risk assessment coefficient under the current motor working state, and calculates the motor fault surge acceleration according to the motor maintenance risk assessment coefficient in the current motor state and the motor maintenance risk assessment coefficient under the interval time t3
Figure FDA0003346654840000042
And track and predict the life of the motor that continues to work according to the current motor fault surge factor
Figure FDA0003346654840000043
G t3 is the motor maintenance risk assessment coefficient at the time point t3, and G max is the maximum motor maintenance risk assessment coefficient allowed by the motor. 8.一种基于物联网多维度数据的设备故障评估方法，其特征在于：具体步骤如下：8. A device fault assessment method based on multi-dimensional data of the Internet of Things, characterized in that: the specific steps are as follows: S1、对电机运行过程中的轴承温度和混合噪声进行采集，对混合噪声进行分离，获得轴承噪声和机械噪声；S1. Collect the bearing temperature and mixed noise during the operation of the motor, and separate the mixed noise to obtain bearing noise and mechanical noise; S2、对轴承噪声和机械噪声进行故障等级的筛查，获得轴承噪声故障等级和机械噪声故障等级；S2. Screen the fault level of bearing noise and mechanical noise to obtain the bearing noise fault level and mechanical noise fault level; S3、采集电机轴向振动幅值和径向振动幅值，以建立时域振动信号图，并通过时域振动信号图分析轴向振动的基本参数以及径向振动的基本参数；S3. Collect the axial vibration amplitude and radial vibration amplitude of the motor to establish a time-domain vibration signal graph, and analyze the basic parameters of axial vibration and radial vibration through the time-domain vibration signal graph; S4、提取步骤S3中的轴向振动和径向振动的基本参数进行电机振动异常程度判定量；S4, extracting the basic parameters of axial vibration and radial vibration in step S3 to determine the abnormal degree of motor vibration; S5、对轴承表面温度进行处理，获得轴承表面的温度上升的最大速度、轴承温度大于预设温度W的累计时长以及轴承温度大于预设温度W后的轴承表面温度；S5, processing the bearing surface temperature to obtain the maximum speed of the temperature rise of the bearing surface, the cumulative time period for which the bearing temperature is greater than the preset temperature W, and the bearing surface temperature after the bearing temperature is greater than the preset temperature W; S6、结合步骤S4和步骤S5中的数据分析电机的抱轴预估系数，并采用多维度数据评估模型判断当前电机继续工作下的电机维持危险评估系数。S6. Combine the data in steps S4 and S5 to analyze the shaft-holding prediction coefficient of the motor, and use a multi-dimensional data evaluation model to determine the motor maintenance risk assessment coefficient when the current motor continues to work.
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CN115982552A (en) * 2022-12-19 2023-04-18 萍乡学院 Electronic signal processing method and system
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CN117890096A (en) * 2024-01-16 2024-04-16 杭州腾励传动科技股份有限公司 Abnormal test method for driving shaft running state
CN117928950A (en) * 2024-01-29 2024-04-26 陕西重构智信科技有限公司 A Fault Diagnosis Method and System Based on Rolling Bearing Vibration
CN118503926A (en) * 2024-07-12 2024-08-16 无锡学院 Intelligent electromechanical comprehensive diagnosis and monitoring method and system based on industrial Internet
CN118503882A (en) * 2024-07-10 2024-08-16 无锡学院 Method for collecting operating state parameters and diagnosing faults of three-phase horizontal decanter centrifuge
CN118583498A (en) * 2024-08-02 2024-09-03 常熟理工学院 A method and system for predicting the remaining service life of a rolling bearing
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CN113777488A (en) * 2021-09-14 2021-12-10 中国南方电网有限责任公司超高压输电公司昆明局 State evaluation method and device for valve cooling main pump motor and computer equipment
CN114500235A (en) * 2022-04-06 2022-05-13 深圳粤讯通信科技有限公司 Communication equipment safety management system based on Internet of things
CN114920122A (en) * 2022-05-05 2022-08-19 广州广日电梯工业有限公司 Escalator fault early warning method and system based on transfer learning
CN114810513A (en) * 2022-06-24 2022-07-29 江苏奥派电气科技有限公司 Wind power generator bearing vibration fault intelligent monitoring system based on 5G communication
CN115453267A (en) * 2022-09-15 2022-12-09 北京京能清洁能源电力股份有限公司北京分公司 Fault diagnosis system for electric power information system
CN115982552B (en) * 2022-12-19 2023-10-20 萍乡学院 Electronic signal processing method and system
CN115982552A (en) * 2022-12-19 2023-04-18 萍乡学院 Electronic signal processing method and system
CN116781864A (en) * 2023-06-21 2023-09-19 浙江宏远智能科技有限公司 Factory operation state remote monitoring system and method based on Internet of things data acquisition
CN116781864B (en) * 2023-06-21 2024-02-23 浙江宏远智能科技有限公司 Factory operation state remote monitoring system and method based on Internet of things data acquisition
CN117890096A (en) * 2024-01-16 2024-04-16 杭州腾励传动科技股份有限公司 Abnormal test method for driving shaft running state
CN117928950A (en) * 2024-01-29 2024-04-26 陕西重构智信科技有限公司 A Fault Diagnosis Method and System Based on Rolling Bearing Vibration
CN118503882A (en) * 2024-07-10 2024-08-16 无锡学院 Method for collecting operating state parameters and diagnosing faults of three-phase horizontal decanter centrifuge
CN118503882B (en) * 2024-07-10 2024-11-22 无锡学院 The method of operating status parameter collection and fault diagnosis of three-phase horizontal screw centrifuge
CN118503926A (en) * 2024-07-12 2024-08-16 无锡学院 Intelligent electromechanical comprehensive diagnosis and monitoring method and system based on industrial Internet
CN118503926B (en) * 2024-07-12 2024-11-05 无锡学院 Intelligent electromechanical comprehensive diagnosis, monitoring method and system based on industrial Internet
CN118583498A (en) * 2024-08-02 2024-09-03 常熟理工学院 A method and system for predicting the remaining service life of a rolling bearing
CN119199529A (en) * 2024-11-30 2024-12-27 浙江华章科技有限公司 A motor detection system for digital papermaking integrated control system

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