CN108844662B - Method for evaluating state of electrical cabinet of numerical control machine tool - Google Patents

Method for evaluating state of electrical cabinet of numerical control machine tool Download PDF

Info

Publication number
CN108844662B
CN108844662B CN201810955492.8A CN201810955492A CN108844662B CN 108844662 B CN108844662 B CN 108844662B CN 201810955492 A CN201810955492 A CN 201810955492A CN 108844662 B CN108844662 B CN 108844662B
Authority
CN
China
Prior art keywords
electrical cabinet
value
machine tool
characteristic value
layer
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201810955492.8A
Other languages
Chinese (zh)
Other versions
CN108844662A (en
Inventor
汪木兰
季颖
朱晓春
徐开芸
苏健
朱昊
柏月
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing Institute of Technology
Original Assignee
Nanjing Institute of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nanjing Institute of Technology filed Critical Nanjing Institute of Technology
Priority to CN201810955492.8A priority Critical patent/CN108844662B/en
Publication of CN108844662A publication Critical patent/CN108844662A/en
Application granted granted Critical
Publication of CN108844662B publication Critical patent/CN108844662B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
    • G01K13/00—Thermometers specially adapted for specific purposes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/043—Architecture, e.g. interconnection topology based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS]

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • Biophysics (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Fuzzy Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Computational Mathematics (AREA)
  • Biomedical Technology (AREA)
  • Mathematical Analysis (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Automation & Control Theory (AREA)
  • Feedback Control In General (AREA)

Abstract

The invention provides a method for evaluating the state of an electrical cabinet of a numerical control machine tool, which is based on the real-time working condition of the machine tool, monitors the temperature data of the electrical cabinet on line, extracts sensitive characteristics, constructs a fuzzy neural network, and establishes a model for evaluating the state of the electrical cabinet of the numerical control machine tool on line, and has higher real-time performance and practicability. And reasonably evaluating the aging degree of components in the electrical cabinet according to the evaluation result of the working state of the electrical cabinet, finding out the visual maintenance of the performance-degraded components, and if an abnormal condition occurs, sending an alarm signal by the system, and stopping the machine for maintenance.

Description

Method for evaluating state of electrical cabinet of numerical control machine tool
Technical Field
The invention relates to the technical field of state evaluation of electrical cabinets, in particular to a state evaluation method for electrical cabinets of a numerical control machine tool.
Background
The electrical cabinet of the numerical control machine tool is provided with various electrical components for strong current control of the machine tool, and the electrical components not only provide a power supply for a weak current control system such as numerical control and servo, and various electrical protections such as short circuit, overload and undervoltage, but also play a role of a bridge between a PLC output interface and electrical execution elements of various auxiliary devices of the machine tool. At present, most machine tool electrical control cabinets are not provided with an online state evaluation system, components in the cabinet are replaced, fault maintenance and the like are mostly carried out after faults occur, and certain hysteresis exists.
Heat-generating faults of electrical equipment include faults external to the equipment (e.g., electrical joint faults) and various faults internal to the equipment housing. The external fault can obtain visual information from the outside of the equipment, and the internal fault obtains the temperature rule of the internal fault appearing outside the equipment by combining the statistical analysis of a field detection example according to the internal structure and the operation working condition of the equipment, so that the nature, the part and the severity of the internal fault of the equipment are judged. The method for evaluating the state of the electrical cabinet of the numerical control machine tool is provided, on the premise of the field working condition of the machine tool, the health state of the electrical cabinet is evaluated in real time based on temperature data measured at different parts in the electrical cabinet, and on the basis, the machine tool is maintained and shut down for maintenance according to the conditions, so that the method has great significance for improving the normal operation and production benefits of the machine tool.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the prior art, a state evaluation method for an electrical cabinet of a numerical control machine tool is provided, and the state evaluation method is used for evaluating the working state of the electrical cabinet of the machine tool.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for evaluating the state of an electric cabinet of a numerical control machine tool comprises the following steps:
step 1: collecting data of each temperature sensor in the electrical cabinet, recording the data of each temperature sensor in the same moment as a sample, and recording the working state of the electrical cabinet corresponding to the sample to obtain a sample set;
step 2: extracting a temperature characteristic value from the sample set to obtain a characteristic value sequence X (k) consisting of the temperature characteristic values, wherein X (k) is (x)1(k),x2(k),...,xn(k) K is the temperature sampling sequence number of the whole sample data sequence, n is the dimension of a single sample, xi(k) Is a temperature characteristic value, i ═ 1, n]；
And step 3: calculating a temperature characteristic value xi(k) Gray correlation coefficient gamma of working state of electric cabinet corresponding to gray correlation coefficient gammaiDistributing corresponding weight omega to the correlation coefficient of each sensor channel and the working state of the machine tooli；
And 4, step 4: according to the respective sample
Figure GDA0002530255490000021
The working states of the electrical cabinet are divided into three types, the first type is as follows: the electrical cabinet works normally, and the components are not abnormal; the second type is: the electrical cabinet basically works normally, performance degradation of components and the like occurs, but the operation of the electrical cabinet is not influenced, and visual maintenance can be realized; in the third category: the regulator cubicle work is unusual, needs to shut down the maintenance. In the first kind of working state
Figure GDA0002530255490000022
Minimum value of (2)
Figure GDA0002530255490000023
In the second type of operating state
Figure GDA0002530255490000024
Minimum value of (2)
Figure GDA0002530255490000025
And 5: calculating new samples
Figure GDA0002530255490000026
If it is not
Figure GDA0002530255490000027
Judging that the electrical cabinet works normally and the components are not abnormal; if it is not
Figure GDA0002530255490000028
Judging that the electrical cabinet basically works normally, performance degradation of components and the like occurs, but the operation of the electrical cabinet is not influenced, and visual maintenance is realized; if it is not
Figure GDA0002530255490000029
The electrical cabinet is judged to be abnormal in work and needs to be shut down for maintenance.
Preferably, in step 2, a linear regression analysis method is used to extract the temperature data characteristic value:
establishing a unary linear regression model expressed as:
y＝as+b
Figure GDA00025302554900000210
Figure GDA00025302554900000211
wherein y is a temperature value, s is a sampling time window sequence number, a is a linear characteristic value of a temperature sequence, b is a linear constant of the temperature sequence, i is a data sampling sequence number in a single sample, and n is a single sample dimension.
Preferably, in step 3, the grey correlation coefficient γ isiAnd weight omegaiThe calculation steps are as follows:
will be at temperatureCharacteristic value xi(k) Processed as reference data xo(k)，
Figure GDA0002530255490000031
Wherein, maxxi(k) Is the maximum value, x, of the sample data sequencemIs the target value of the target eye characteristic; calculating a gray correlation coefficient
Figure GDA0002530255490000032
Wherein, Deltaoi(k)＝|xo(k)-xi(k) L, rho is a resolution coefficient, rho is more than or equal to 0 and less than or equal to 1,
Figure GDA0002530255490000033
Figure GDA0002530255490000034
distributing corresponding weight omega to the correlation coefficient of each sensor channel and the working state of the machine tooli，
Figure GDA0002530255490000035
Figure GDA0002530255490000036
The degree of association is then expressed as:
Figure GDA0002530255490000037
k is a sample data sequence sampling serial number, i is a data sampling serial number in a single sample, and n is a single sample dimension.
Preferably, in step 4
Figure GDA0002530255490000038
The calculation method comprises the following steps:
putting the characteristic value sequence into a fuzzy neural network for network learning, wherein the fuzzy neural network comprises an input layer, a fuzzy layer, an input signal activation layer, a de-fuzzy layer and an output layer; the fuzzy layer is calculated by adopting a Gaussian membership function, and the calculation formula of the central value of the membership function is as follows:
Figure GDA0002530255490000039
the calculation formula of the width value of the membership function is as follows:
Figure GDA00025302554900000310
wherein t is 1, 2, 3; l is the number of iterations; eta is learning efficiency; x is a temperature characteristic value; the calculation formula of the blurring layer is:
Figure GDA0002530255490000041
the calculation formula of the input signal activation layer is as follows:
Figure GDA0002530255490000042
the calculation formula of the deblurring layer is as follows:
ft(x)＝Nt(x)(λtx+μt)，
wherein the content of the first and second substances,
Figure GDA0002530255490000043
Figure GDA0002530255490000044
Figure GDA0002530255490000045
the calculation formula of the output layer is as follows:
Figure GDA0002530255490000046
the concrete network learning steps are as follows:
1) firstly, setting the central value and the width value of the membership function as random numbers tending to 0, and calculating the error between the actual output and the expected output of the network
Figure GDA0002530255490000047
Wherein Z isqAnd Z is the expected output value and the actual output value of the output layer, respectively;
2) when the error E meets the precision requirement, the training is finished, otherwise, the learning efficiency and the iteration number are corrected to continue the training;
the corresponding fuzzy rule is as follows:
If(x is M1)then(f1＝N1(x)(λ1x+μ1))
If(x is M2)then(f2＝N2(x)(λ2x+μ2))
If(x is M3)then(f3＝N3(x)(λ3x+μ3))
f1corresponding to the first type of working state of the electrical cabinet; f. of2Corresponding to the second working state of the electrical cabinet; f. of3Corresponding to the third working state of the electrical cabinet; will f istCorresponding temperature characteristic value as sample data to calculate corresponding
Figure GDA0002530255490000051
f1、f2Corresponding to
Figure GDA0002530255490000052
Respectively is
Figure GDA0002530255490000053
Preferably, the eigenvalue series is compressed and deburred before the eigenvalue series is subjected to fuzzy neural network training.
Has the advantages that: based on the real-time working condition of the machine tool, the temperature data of the electrical cabinet is monitored on line, the sensitive characteristics are extracted, the grey neural network is constructed, the state evaluation model of the electrical cabinet of the numerical control machine tool is established on line, and the real-time performance and the practicability are higher. And reasonably evaluating the aging degree of components in the electrical cabinet according to the evaluation result of the working state of the electrical cabinet, finding out the visual maintenance of the performance-degraded components, and if an abnormal condition occurs, sending an alarm signal by the system, and stopping the machine for maintenance.
Drawings
FIG. 1 is a schematic structural diagram of a numerically-controlled machine tool electrical cabinet state evaluation system;
fig. 2 is a schematic diagram of dividing the working state of the electrical cabinet into 3 stages, where point a is a visual maintenance critical point and point B is a shutdown maintenance critical point;
FIG. 3 is a schematic diagram of the evaluation of the working condition of the electrical cabinet;
fig. 4 is a schematic diagram of a fuzzy neural network structure.
Detailed Description
The invention is further explained below with reference to the drawings.
As shown in fig. 1, a method for evaluating the state of an electrical cabinet of a numerically-controlled machine tool includes the following steps: (1) collecting temperature data of an electrical cabinet of the numerical control machine tool. Temperature sensors are reasonably arranged in the machine tool electrical cabinet, and sensor signals are converted into temperature values after being processed by a data acquisition system, so that original parameters are provided for an electrical cabinet state evaluation system.
(2) And extracting a temperature characteristic value from the collected data. And establishing an unary linear regression model y as + b, selecting a primary term coefficient a as a temperature characteristic value, and selecting an optimal data sampling width, wherein the sampling width is too small to represent the change trend in a short time, and the calculation complexity is increased if the sampling width is too large.
(3) And carrying out compression and deburring processing on the characteristic value array. And partial points which frequently cause data density but do not influence the evaluation state are removed from the temperature characteristic value sequence, so that the sample capacity is reduced, and the calculation workload is reduced. In order to avoid false alarm caused by the phenomenon of 'burr', the square of the characteristic value is taken as a measurement standard, and if the square deviation of the characteristic value at a certain point is slightly larger, the point can be ignored.
(4) Collecting the processed temperature characteristic values a in a data sequence X (k), wherein X (k) is (x)1(k),x2(k),...,xn(k) K is the temperature sampling sequence number of the whole sample data sequence, and i is the temperature sampling sequence number in a single sample. x is the number ofi(k) Is a temperature characteristic value, i ═ 1, n]。
(5) And calculating grey correlation coefficients and correlation degrees of the temperature characteristic values of different positions in the electrical cabinet and the working state of the electrical cabinet. Sampling data x according to the characteristics of the targeti(k) Processed as reference data xo(k)。
Figure GDA0002530255490000061
Wherein, maxxi(k) Is the maximum value, x, of the sample data sequencemFor the target value of the eye characteristic, for simplifying the calculation, x0(k)＝[1 ... 1]. Calculating a gray correlation coefficient
Figure GDA0002530255490000062
Wherein, Deltaoi(k)＝|xo(k)-xi(k) And | and rho are resolution coefficients, rho is more than or equal to 0 and less than or equal to 1, and is generally 0.5.
Figure GDA0002530255490000063
Figure GDA0002530255490000064
The gray correlation ∑ (ω) is obtained according to the gray correlation coefficientiγi) Where n is a single sample dimension, i ═ 1, n]. Distributing corresponding weight omega to the correlation coefficient of each sensor channel and the working state of the machine tooli，
Figure GDA0002530255490000065
Wherein
Figure GDA0002530255490000066
γave-γiIf > 0, get "-", otherwise get "+”。∑(ωiγi) The relationship with the machine tool operating state is shown in fig. 2. Fig. 2 divides the working state of the electrical cabinet into 3 stages, wherein point a is a visual maintenance critical point, and point B is a shutdown maintenance critical point. In stage 1, the electrical cabinet works normally, the components are not abnormal, and parameters are evaluated
Figure GDA0002530255490000067
In stage 2, the electrical cabinet basically works normally, performance degradation of components and parts occurs, and the like, but operation is not influenced, visual maintenance is realized, and parameters are evaluated
Figure GDA0002530255490000071
In stage 3, the electrical cabinet works abnormally and needs to be shut down for maintenance and parameters are evaluated
Figure GDA0002530255490000072
Different failure modes and failure degrees of the electrical cabinet have corresponding evaluation parameters
Figure GDA0002530255490000073
The evaluation diagram is shown in fig. 3.
Considering historical data and field working conditions, putting the temperature characteristic value sequence of the electrical cabinet during the operation of the machine tool into a fuzzy neural network for network learning, setting training times, learning rate and the like, fixing a network structure, and obtaining a critical value of visual maintenance and shutdown maintenance of the electrical cabinet
Figure GDA0002530255490000074
A 5-layer fuzzy neural network of 1 input (temperature characteristic value array), 3 fuzzy rules and 1 output (denoted by Z) is established, and the structure is shown in fig. 4. The 1 st layer is an input layer, the 2 nd layer is a fuzzy layer, and a Gaussian membership function is adopted for calculation. Firstly, setting the central value and the width value of the membership function as random numbers tending to 0. Calculating an error between an actual output and a desired output of a network
Figure GDA0002530255490000075
Wherein Z isqAnd Z is the desired output value of the output layer andthe actual output value. Center value of membership function
Figure GDA0002530255490000076
Width value of membership function
Figure GDA0002530255490000077
Wherein t is 1, 2, 3, l is iteration times, η is learning efficiency, training is finished when the error between the actual output and the ideal output of the network is in a small range, otherwise, the training is continued by correcting parameters, the network is fuzzified after calculating the central value and the width value of the membership function, and the following calculation formula is obtained
Figure GDA0002530255490000078
Layer 3 is the input signal active layer,
Figure GDA0002530255490000079
level 4 is defuzzification, calculating the output of each rule, ft(x)＝Nt(x)(λtx+μt). At layer 5, the sum of all rules,
Figure GDA00025302554900000710
the corresponding fuzzy rule is as follows:
If(x is M1)then(f1＝N1(x)(λ1x+μ1))
If(x is M2)then(f2＝N2(x)(λ2x+μ2))
If(x is M3)then(f3＝N3(x)(λ3x+μ3))
f1、f2、f3corresponding to stage 1, stage 2, stage 3, respectively, in fig. 2. Will f istCorresponding temperature characteristic value as sample data to calculate corresponding
Figure GDA0002530255490000081
f1、f2Stage correspondence
Figure GDA0002530255490000082
Is the value at two critical points
Figure GDA0002530255490000083
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims (4)

1. A method for evaluating the state of an electric cabinet of a numerical control machine tool is characterized by comprising the following steps:
step 1: collecting data of each temperature sensor in the electrical cabinet, recording the data of each temperature sensor in the same moment as a sample, and recording the working state of the electrical cabinet corresponding to the sample to obtain a sample set;
step 2: extracting a temperature characteristic value from the sample set to obtain a characteristic value sequence X (k) consisting of the temperature characteristic values, wherein X (k) is (x)1(k),x2(k),...,xn(k) K is the temperature sampling sequence number of the whole sample data sequence, n is the dimension of a single sample, xi(k) Is a temperature characteristic value, i ═ 1, n]；
And step 3: calculating a temperature characteristic value xi(k) Gray correlation coefficient gamma of working state of electric cabinet corresponding to gray correlation coefficient gammaiAnd distributing corresponding weight omega to the correlation coefficient of each sensor channel and the working state of the machine tooli；
And 4, step 4: according to each sample
Figure FDA0002481489920000011
The working states of the electrical cabinet are divided into three types, the first type is as follows: the electrical cabinet works normally, and the components are not abnormal; the second type is: the electrical cabinet basically works normally, and the performance of components is degraded without influencing the operation of the electrical cabinet; in the third category: the electrical cabinet works abnormally and needs to be shut down for maintenance; in the first kind of working state
Figure FDA0002481489920000012
Minimum value of (2)
Figure FDA0002481489920000013
In the second type of operating state
Figure FDA0002481489920000014
Minimum value of (2)
Figure FDA0002481489920000015
And 5: calculating new samples
Figure FDA0002481489920000016
If it is not
Figure FDA0002481489920000017
Judging that the electrical cabinet works normally and the components are not abnormal; if it is not
Figure FDA0002481489920000018
Judging that the electrical cabinet basically works normally, and the performance of the components is degraded without influencing the operation of the electrical cabinet; if it is not
Figure FDA0002481489920000019
Judging the working abnormity of the electrical cabinet, and needing to be shut down for maintenance;
in step 4
Figure FDA00024814899200000110
The calculation method comprises the following steps:
putting the characteristic value sequence into a fuzzy neural network for network learning, wherein the fuzzy neural network comprises an input layer, a fuzzy layer, an input signal activation layer, a de-fuzzy layer and an output layer; the fuzzy layer is calculated by adopting a Gaussian membership function, and the calculation formula of the central value of the membership function is as follows:
Figure FDA0002481489920000021
the calculation formula of the width value of the membership function is as follows:
Figure FDA0002481489920000022
wherein t is 1, 2, 3, l is iteration times, η is learning efficiency, and E is the error between the actual output and the expected output;
Figure FDA0002481489920000023
to obtain a partial derivative about E for the central value of the membership function,
Figure FDA0002481489920000024
calculating a partial derivative about E for the width value of the membership function; x is a temperature characteristic value; the calculation formula of the blurring layer is:
Figure FDA0002481489920000025
the calculation formula of the input signal activation layer is as follows:
Figure FDA0002481489920000026
the calculation formula of the deblurring layer is as follows:
ft(x)＝Nt(x)(λtx+μt)，
wherein the content of the first and second substances,
Figure FDA0002481489920000027
Figure FDA0002481489920000028
Figure FDA0002481489920000029
t0as an initial value, the calculation formula of the output layer is:
Figure FDA00024814899200000210
the concrete network learning steps are as follows:
1) firstly, setting the central value and the width value of the membership function as random numbers tending to 0, and calculating the error between the actual output and the expected output of the network
Figure FDA0002481489920000031
Wherein Z isqAnd Z is the expected output value and the actual output value of the output layer, respectively;
2) when the error E meets the precision requirement, the training is finished, otherwise, the learning efficiency and the iteration number are corrected to continue the training;
the corresponding fuzzy rule is as follows:
If(x is M1)then(f1＝N1(x)(λ1x+μ1))
If(x is M2)then(f2＝N2(x)(λ2x+μ2))
If(x is M3)then(f3＝N3(x)(λ3x+μ3))
f1corresponding to the first type of working state of the electrical cabinet; f. of2Corresponding to the second working state of the electrical cabinet; f. of3Corresponding to the third working state of the electrical cabinet; will f istCorresponding temperature characteristic value as sample data to calculate corresponding
Figure FDA0002481489920000032
f1、f2Corresponding to
Figure FDA0002481489920000033
Minimum value of (2)I.e. each is
Figure FDA0002481489920000034
2. The method for evaluating the state of the electrical cabinet of the numerical control machine tool according to claim 1, wherein in the step 2, a linear regression analysis method is adopted to extract the temperature characteristic value:
establishing a unary linear regression model expressed as:
y＝as+b
Figure FDA0002481489920000035
Figure FDA0002481489920000036
wherein y is a temperature value, s is a sampling time window sequence number, a is a linear characteristic value of a temperature sequence, b is a linear constant of the temperature sequence, i is a data sampling sequence number in a single sample, and n is a single sample dimension.
3. The method for evaluating the state of the electrical cabinet of the numerically-controlled machine tool according to claim 1, wherein in step 3, a grey correlation coefficient γiAnd weight omegaiThe calculation steps are as follows:
the temperature characteristic value xi(k) Processed as reference data xo(k)，
Figure FDA0002481489920000041
Wherein, maxxi(k) Is the maximum value, x, of the sample data sequencemIs the target value of the target eye characteristic; calculating a gray correlation coefficient
Figure FDA0002481489920000042
Wherein, Deltaoi(k)＝|xo(k)-xi(k) Rho is a resolution coefficient, rho is more than or equal to 0 and less than or equal to 1,
Figure FDA0002481489920000043
Figure FDA0002481489920000044
distributing corresponding weight omega to the correlation coefficient of each sensor channel and the working state of the machine tooli，
Figure FDA0002481489920000045
Figure FDA0002481489920000046
The degree of association is then expressed as:
Figure FDA0002481489920000047
k is a sample data sequence sampling serial number, i is a data sampling serial number in a single sample, and n is a single sample dimension.
4. The method for evaluating the state of the electrical cabinet of the numerical control machine tool according to claim 1, wherein before the fuzzy neural network training is performed on the characteristic value number series, the characteristic value number series is compressed and deburred.
CN201810955492.8A 2018-08-21 2018-08-21 Method for evaluating state of electrical cabinet of numerical control machine tool Active CN108844662B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810955492.8A CN108844662B (en) 2018-08-21 2018-08-21 Method for evaluating state of electrical cabinet of numerical control machine tool

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810955492.8A CN108844662B (en) 2018-08-21 2018-08-21 Method for evaluating state of electrical cabinet of numerical control machine tool

Publications (2)

Publication Number Publication Date
CN108844662A CN108844662A (en) 2018-11-20
CN108844662B true CN108844662B (en) 2020-08-14

Family

ID=64189811

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810955492.8A Active CN108844662B (en) 2018-08-21 2018-08-21 Method for evaluating state of electrical cabinet of numerical control machine tool

Country Status (1)

Country Link
CN (1) CN108844662B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114371658B (en) * 2021-12-27 2024-06-07 上海滨捷机电有限公司 Control method and system for numerical control machine integrated electric cabinet, electronic equipment and readable storage medium

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103678765B (en) * 2013-10-31 2016-04-27 上海交通大学 Based on the running state of transformer comprehensive estimation method of on-line monitoring
CN104076233B (en) * 2014-07-18 2017-03-15 彭浩明 Degree of aging detection method and detection means
CN107289998B (en) * 2017-05-12 2019-08-23 淮阴工学院 CAN bus based pig house environment temperature intelligent monitoring system

Also Published As

Publication number Publication date
CN108844662A (en) 2018-11-20

Similar Documents

Publication Publication Date Title
WO2021004154A1 (en) Method for predicting remaining life of numerical control machine tool
Demetriou et al. Incipient fault diagnosis of dynamical systems using online approximators
DE102017108169B4 (en) Production system that defines a determination value of a variable in relation to a product deviation
Wang et al. On line tool wear monitoring based on auto associative neural network
CN108363836B (en) Multi-working-condition self-adaptive industrial robot health degree assessment method and system
CN112629585A (en) Equipment on-line monitoring method and device based on multi-dimensional parameter estimation
CN108535572B (en) Metering system secondary circuit monitoring method and device based on fundamental wave zero sequence characteristics
CN115828170B (en) Fault detection method based on electronic control data of tunneling machine
CN111506049A (en) Multiple fault diagnosis method for aero-engine control system based on AANN network system
CN111340367B (en) Oil-shortage equipment state evaluation method and system based on multi-dimensional relevance data analysis
CN111368428A (en) Sensor precision reduction fault detection method based on monitoring second-order statistics
CN112668105A (en) Helicopter transmission shaft abnormity judgment method based on SAE and Mahalanobis distance
Bejaoui et al. A data-driven prognostics technique and rul prediction of rotating machines using an exponential degradation model
CN116086537A (en) Equipment state monitoring method, device, equipment and storage medium
CN108844662B (en) Method for evaluating state of electrical cabinet of numerical control machine tool
Wei et al. Remaining useful life estimation based on gamma process considered with measurement error
CN110751217A (en) Equipment energy consumption ratio early warning analysis method based on principal component analysis
CN112989587B (en) Online analysis method and system for degradation cause of capacitive voltage transformer
CN116627116B (en) Process industry fault positioning method and system and electronic equipment
CN111618655B (en) Quantitative evaluation method for health degree of ball screw of numerical control machine tool
CN112067289A (en) Motor shaft and transmission shaft abnormal vibration early warning algorithm based on neural network
CN115081798A (en) PLS algorithm-based plate and strip hot finish rolling process sub-frame combined monitoring and diagnosis method
CN110991841B (en) Analysis method for nonstandard behaviors in bidding process based on AI technology
CN116150666B (en) Energy storage system fault detection method and device and intelligent terminal
CN110161382B (en) Method and device for judging whether transformer needs power failure test or not

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant