CN109613428A - It is a kind of can be as system and its application in motor device fault detection method - Google Patents
It is a kind of can be as system and its application in motor device fault detection method Download PDFInfo
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- CN109613428A CN109613428A CN201811519270.8A CN201811519270A CN109613428A CN 109613428 A CN109613428 A CN 109613428A CN 201811519270 A CN201811519270 A CN 201811519270A CN 109613428 A CN109613428 A CN 109613428A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/34—Testing dynamo-electric machines
- G01R31/343—Testing dynamo-electric machines in operation
Abstract
The invention discloses it is a kind of can be as system and its application in motor device fault detection method, this can include several acquisition device ends, several edge calculations equipment and a cloud server as system, and wherein each edge calculations equipment is communicated to connect at least one acquisition device end；Cloud server is connect with each edge of table computing device communication.The motor device fault detection method using it is described can a kind of sparse self-encoding encoder algorithm of improvement as system and based on deep learning equipment fault sorting algorithm.The present invention can accurately predict the incipient fault that motor device may occur in the process of running, reach and prevent early, plan as a whole the gain effect repaired；It is simultaneously that operand is big, calculating process is complicated, traditional cloud computing method demanding to hardware device is replaced by that operand is small, arithmetic speed is fast, the lower edge calculations method of hardware requirement, improve operation efficiency.
Description
Technical field
The present invention relates to fault detection method field, specifically a kind of motor device fault detection method.
Background technique
Core driving device of the induction machine as motor device, plays a crucial role in modern industry.It is special
It is not in factory and enterprises such as large-scale pipeline productions, the driving motor in industrial machinery is most important to production automation.Therefore, it drives
It is that the sound development gone on smoothly with enterprise is particularly necessary and closes for production work that dynamic motor, which keeps normal operating status,
Key.Rotor eccentricity and rotor off-axis are two kinds of main most common failures of induction machine.Rotor is generating centrifugal force
Meanwhile the bias for being harmful to equipment life can be generated.And the limited rotor stick of service life is due to overloading and safeguarding improper be easy
It produces fracture.Once these failures occur, the entire production line will likely break down and even damage, and cause huge economic loss
Even casualties.According to statistics, in China, about 200,000 motors break down every year, and maintenance cost is more than 2,000,000,000 yuan.Just
Really the failure of motor is diagnosed in time, find failure in time before generating more serious damage and is overhauled, is
Essential link in industrial production activities.
Traditional motor device fault diagnosis technology is mainly based upon intrusive sensor to analyze equipment.However
Many common conventional motors equipment do not have onboard data acquisition instrument to obtain new diagnostic data.If upgrading these machines
It will be a challenge very serious, because usually not reserved space adds intrusive sensor inside machine.In addition,
When device fails, intrusive sensor also vulnerable to damage and is detached from, and the interruption of diagnostic data upload will lead to and set
The interruption and erroneous judgement of standby fault diagnosis system operation.In addition, existing most of diagnostic methods are all rule of thumb to believe feature
Number carry out artificial selection classification.When using inappropriate characteristic signal, diagnostic data cannot be fully utilized.Meanwhile especially
It is the different types of motor device for being operated in dynamic operation environment, the versatility of train classification models is also and accuracy
It is also restrained.
Summary of the invention
The present invention to achieve the above object, takes following technical scheme to be achieved:
It is of the present invention to refer to as system in equipment work operating, pass through electric current, voltage, power and the function to equipment
The detection of rate factor electrical energy parameter and the current fluctuation of equipment can detect the operating status of analytical equipment, by one section of equipment
The data modeling of time, can analytical equipment overall operation life cycle.It is described to be used to mention because of factor of equipment failure as system
Preceding early warning is reminded.
It is a kind of can be as system, including several acquisition device ends, several edge calculations equipment and a cloud service
Device, each of several described edge calculations equipment edge calculations equipment all at least one acquisition device end communication link
It connects, the cloud server is connect with each edge of table computing device communication, and the acquisition device end is used for target object
Electrical energy parameter carry out data acquisition and the data of the acquisition be transmitted to edge calculations equipment, the edge calculations equipment is used
In the data for receiving and pre-processing the acquisition device end transmission, and pretreated data are transmitted to the cloud service
Device.
Further, the electrical energy parameter includes electric current, voltage, power and power factor.
Further, the acquisition device end includes measurement module, data processing module, data transmission module, described
Measurement module includes stating current measurement module, voltage measurement module, power measurement module and power-factor measurement module, is used respectively
In measure and acquisition target object electric current, voltage, power and with the time series data of power factor, acquire data frequency be 3 seconds/
It is secondary；The data processing module is used to the collected analog signal data of the measurement module being converted to digital signal data；
The data transmission module has the function by wirelessly or non-wirelessly network mode sending and receiving data.
Further, the edge calculations equipment includes data transmission module, data computation module；The data calculate mould
Block pre-processes the digital signal data from the acquisition device end, and data are transmitted to cloud clothes by treated
Business device；The data transmission module has the function by wirelessly or non-wirelessly network mode sending and receiving data.
The invention also includes using it is above-mentioned can as system carry out motor device fault detection method, the method includes with
Lower step:
S1: the acquisition device end is electrically connected with motor device to be detected；
S2: the acquisition device end acquires the real-time current of the measurement equipment to be checked, voltage, power and power factor
Digital signal data, and it is transmitted through the network to edge calculations equipment；
S3: the edge calculations equipment passes through the digital signal number to the real-time current, voltage, power and power factor
According to calculating separately the statistics feature of real-time current, voltage, power and power factor, and obtained data will be calculated and be transferred to
Cloud server；
S4: the equipment fault discrimination model based on deep learning algorithm is established by cloud server, uses cloud server
The established discrimination model of received data training；
S5: using the discrimination model and cloud server received data in real time to the operating status of motor device into
Row differentiates, when differentiating result is that motor device runs well, does not do subsequent processing；When there may be events for differentiation to motor device
When barrier, fault alarm is issued to manager, arrestment is powered off in time and runs and carry out subsequent maintenance.
Further, the step S3 statistics feature includes temporal signatures: mean value, variance, the coefficient of variation, and frequency domain is special
Sign: low frequency power, high frequency power, low high frequency power ratio, general power, nonlinear characteristic: approximate entropy, fuzzy entropy, Sample Entropy.
Further, the step S4 deep learning algorithm specifically: the equipment for improving sparse self-encoding encoder algorithm (SAE)
Failure modes algorithm.
Compared with prior art, the present invention has following gain effect:
It is of the present invention to be intended to as system by the way that multiple acquisition device ends and edge calculations router is arranged, by operation
Amount is big, calculating process is complicated, traditional cloud computing method demanding to hardware device be replaced by operand is small, arithmetic speed is fast,
The lower edge calculations method of hardware requirement.Meanwhile a variety of electrical energy parameters are acquired to each motor device, to improve conventional method
Accuracy, based on different motor device units operation and electrical energy parameter feature diversity and particularity, with solve due to list
The low problem of accuracy caused by the limitation of one parameter.Acquire simultaneously device end with non-intrusion type, acquisition it is accurate, can
The advantages that connecting internet cloud.The present invention is by using can be as system and the motor device fault detection side based on deep learning
Method can effectively improve the accuracy of prediction, the incipient fault that accurately pre- measurement equipment may occur, and reach and prevent early, plan as a whole to repair
The gain effect copied.
Detailed description of the invention
Fig. 1 is that the present invention can be as the structural schematic block diagram of system；
Specific embodiment
The present invention will be further described with reference to the examples below, but it should be recognized that embodiment is not to this hair
Bright claimed range is construed as limiting.
In the present embodiment, it is a kind of can be as system, including several acquisition device ends, several edge calculations equipment, one
Platform cloud server and several motor devices；Each of several edge calculations equipment edge calculations equipment all with
At least one acquisition device end is connected by WIFI network, and wherein each acquisition device end is electrically connected with a motor device
It connects；The cloud server calculates equipment with each edge of table and is connect by WIFI network.It can be as system and with deep using this
Spend the motor device fault detection method of learning algorithm are as follows: acquisition device end is to the motor device to be detected being electrically connected
Real-time current, voltage, power and power factor electrical energy parameter are acquired, and obtain the digital signal of electrical energy parameter, and will be digital
Signal is transmitted to edge calculations equipment by WIFI network, edge calculations equipment by received from being attached thereto
The digital signal of acquisition device end calculates the statistics feature of every group of signal, including temporal signatures: mean value, variance, variation lines
Number, frequency domain character: low frequency power, high frequency power, low high frequency power ratio, general power, nonlinear characteristic: approximate entropy, fuzzy entropy, sample
This entropy, and result is transferred to cloud server by WIFI network in real time.It is established by cloud server dilute based on improving
The equipment fault discrimination model for dredging the equipment fault sorting algorithm of self-encoding encoder algorithm (SAE), with cloud server, institute is received
The established discrimination model of data training, is finally passed through using the discrimination model and cloud server received data in real time
The operating status of motor device is differentiated, when differentiating result is that equipment runs well, does not do subsequent processing；When differentiation is arrived
Equipment issues fault alarm to manager, powers off arrestment in time and run and carry out subsequent maintenance there may be when failure.Base
It is constructed in the equipment fault sorting algorithm for improving sparse self-encoding encoder algorithm (SAE) and training deep neural network includes following step
It is rapid:
S1: building deep-neural-network, including input layer, hidden layer and output layer；Deep-neural-network is carried out initial
Change setting, comprising: setting input layer, hidden layer, the number of plies of each layer of output layer and neuromere points select iterative algorithm for gradient
Descent algorithm, and the cost function of iterative algorithm is set, the initial value of step-up error limit value and maximum number of iterations, setting test
Accuracy threshold values；
The cost function formula isM is sample size in formula, and footmark i is indicated
Sample serial number, parameter θ are the weight vector generated at random of being counted according to every layer of neuromere, hθ(x(i)) indicate pre- with parameter θ and x
The prediction y value come is measured, y indicates the standard y value of former training sample；
S2: electrical energy parameter data can be run as the equipment that system acquisition device end collects according to described, comprising setting
It is standby to operate normally and multiple classifications of equipment different faults, each classification include multiple and different samples, data sample according to
The ratio of 1:1 is divided into training group and test group；
S3: by the set according to stamping behavior class label value and being sent into deep neural network, cost function J is calculated
The numerical value of (θ) constantly updates the value of weight vector θ, value of the iteration up to cost function J (θ) by gradient descent algorithm
Reach maximum number of iterations less than error limit described in step S1 or the number of iterations, iteration is completed；
S4: testing deep neural network obtained in step S3 using the test group data, is obtaining classification just
True sample number calculates test accuracy, its calculation formula is: the correct sample number/total number of samples of test accuracy=classification；
S5: if test accuracy is less than the test accuracy threshold values, the error limit and greatest iteration time are adjusted
Number, repeats step S3 to S5；Otherwise training is completed.
Specifically, error limit described in step S1 is set as 10-4, error limit can adjusted again according to test result
Whole, the maximum number of iterations is arranged 400 times, and the test accuracy threshold values is set as 95%, the operation of equipment described in step S2
Classification includes: normal operation, rotor eccentricity failure and rotor off-axis failure；
In the present embodiment, database described in S2 can be run using described as the equipment that system acquisition device end collects
Electrical energy parameter data set category attribute, rotor off-axis and rotor eccentricity are defined as equipment fault, no rotor off-axis or rotor
Bias is defined as equipment normal operation.Building and initialization deep-neural-network, sample different classes of in uploaded data point
For training group and test group data, ratio 1:1, cost function formula is
With set according to being trained by the method for above-mentioned steps S3 using to deep neural network, as cost function J
The value of (θ) is less than 10-4Or the number of iterations, when reaching 400 times, iteration is completed, these three equipment states are arrived in deep neural network study
Classification；Deep neural network is tested with test group data again, tests its standard to the identification of this three kinds of equipment state classifications
Exactness trains test sample if deep neural network can correctly identify and test accuracy is more than or equal to 95%
It completes；Otherwise, weight vector θ and error limit are modified, continues to be trained by step S3, until training is completed.In this system,
Collected new equipment data and the statistics feature being calculated periodically can be uploaded to cloud by the edge calculations equipment
Server differentiates that network is detected and sentenced in real time to equipment state by trained depth nerve for cloud server
It is disconnected.If cloud server detects equipment, there may be failures, sound an alarm notify manager immediately, and in time to event
Hinder the operation of device powers down arrestment, to carry out subsequent maintenance.
In conclusion the present invention can in real time be monitored the operating status of equipment and health status, and can it is accurate,
The promptly incipient fault that discovering device is likely to occur, and sounded an alarm in time to manager, to device powers down, after facilitating progress
Continuous overhaul of the equipments.Meanwhile acquisition device end has many advantages, such as that non-intrusion type, acquisition is accurate, can connect internet cloud.We
Method combination cloud server and edge calculations, while constructing equipment fault discrimination model using deep neural network and can effectively mention
The accuracy of height prediction, the failure that accurately pre- measurement equipment may potentially occur reach and prevent early, plan as a whole the gain repaired
Effect.
It is provided for the embodiments of the invention technical solution above to be described in detail, specific case used herein
The principle and embodiment of the embodiment of the present invention are expounded, the explanation of above embodiments is only applicable to help to understand this
The principle of inventive embodiments；At the same time, for those skilled in the art, according to an embodiment of the present invention, in specific embodiment party
There will be changes in formula and application range, in conclusion the contents of this specification are not to be construed as limiting the invention.
Claims (7)
1. a kind of can be as system, it is characterised in that: including several acquisition device ends, several edge calculations equipment and one
Cloud server, each of several described edge calculations equipment edge calculations equipment are all whole at least one acquisition equipment
End communication connection, the cloud server connect with each edge of table computing device communication, and the acquisition device end is used for pair
The electrical energy parameter of target object carries out data acquisition and the data of the acquisition is transmitted to edge calculations equipment, the edge meter
The data that equipment is used to receive and pre-process the acquisition device end transmission are calculated, and pretreated data are transmitted to the cloud
Hold server.
2. according to claim 1 can be as system, it is characterised in that: the electrical energy parameter include electric current, voltage, power and
Power factor.
3. according to claim 1 can be as system, it is characterised in that: the acquisition device end includes measurement module, number
According to processing module, data transmission module；The measurement module includes stating current measurement module, voltage measurement module, power measurement
Module and power-factor measurement module, be respectively used to measure and acquire target object electric current, voltage, power and with power factor
Time series data, acquisition data frequency are 3 seconds/time；The data processing module is used for the collected simulation of the measurement module
Signal data is converted to digital signal data；The data transmission module, which has, passes through wirelessly or non-wirelessly network mode sending and receiving data
Function.
4. according to it is according to claim 1 can be as system, it is characterised in that: the edge calculations equipment include data transmission
Module, data computation module；The data computation module carries out the digital signal data from the acquisition device end pre-
Processing, and data are transmitted to cloud server by treated；The data transmission module has through wirelessly or non-wirelessly network
The function of mode sending and receiving data.
5. a kind of can exist using claim 1-4 is described in any item as the motor device fault detection method of system, feature
In: it the described method comprises the following steps:
S1: the acquisition device end is electrically connected with motor device to be detected；
S2: the acquisition device end acquires the number of the real-time current of the measurement equipment to be checked, voltage, power and power factor
Signal data, and it is transmitted through the network to edge calculations equipment；
S3: the edge calculations equipment passes through the digital signal data to the real-time current, voltage, power and power factor point
Not Ji Suan real-time current, voltage, power and power factor statistics feature, and obtained data will be calculated and be transferred to cloud
Server；
S4: the equipment fault discrimination model based on deep learning algorithm is established by cloud server, is connect with cloud server
The established discrimination model of data training of receipts；
S5: the operating status of motor device is sentenced in real time using the discrimination model and cloud server received data
Not, when differentiating result is that motor device runs well, subsequent processing is not done；When there may be failures for differentiation to motor device
When, fault alarm is issued to manager, arrestment is powered off in time and runs and carry out subsequent maintenance.
6. motor device fault detection method according to claim 5, it is characterised in that: the step S4 statistics feature
Including temporal signatures: mean value, variance, the coefficient of variation, frequency domain character: low frequency power, high frequency power, low high frequency power ratio, total work
Rate, nonlinear characteristic: approximate entropy, fuzzy entropy, Sample Entropy.
7. motor device fault detection method according to claim 5, it is characterised in that: the step S6 deep learning is calculated
Method specifically: improve the equipment fault sorting algorithm of sparse self-encoding encoder algorithm (SAE).
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| CN110197128A (en) * | 2019-05-08 | 2019-09-03 | 华南理工大学 | The recognition of face architecture design method planned as a whole based on edge calculations and cloud |
| CN110262421A (en) * | 2019-06-21 | 2019-09-20 | 深圳市美兆环境股份有限公司 | The control method and device of production equipment |
| CN110388964A (en) * | 2019-08-16 | 2019-10-29 | 深圳江行联加智能科技有限公司 | A kind of methods, devices and systems of tunnel cable data acquisition |
| CN110737732A (en) * | 2019-10-25 | 2020-01-31 | 广西交通科学研究院有限公司 | electromechanical equipment fault early warning method |
| CN110749462A (en) * | 2019-07-19 | 2020-02-04 | 华瑞新智科技（北京）有限公司 | Industrial equipment fault detection method and system based on edge calculation |
| CN111507489A (en) * | 2020-04-20 | 2020-08-07 | 电子科技大学中山学院 | Cloud-edge-coordinated amusement equipment fault prediction and health management system and method |
| CN111784077A (en) * | 2020-07-23 | 2020-10-16 | 国网浙江省电力有限公司检修分公司 | Method and device for predicting state of power equipment based on edge side |
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| CN112015153A (en) * | 2020-09-09 | 2020-12-01 | 江南大学 | System and method for detecting abnormity of sterile filling production line |
| CN112984953A (en) * | 2021-03-24 | 2021-06-18 | 苏州可米可酷食品有限公司 | Intelligent refrigeration industry process optimization method based on Rete algorithm |
| CN113064075A (en) * | 2021-03-16 | 2021-07-02 | 电子科技大学成都学院 | Motor life estimation method based on edge calculation and deep learning |
| CN114543863A (en) * | 2022-02-10 | 2022-05-27 | 德闻仪器仪表(上海)有限公司 | Method for correcting zero drift of ultrasonic transducer |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110008898A (en) * | 2019-04-02 | 2019-07-12 | 中国计量大学 | Industrial equipment data edges processing method based on symbol and convolutional neural networks |
| CN110197128A (en) * | 2019-05-08 | 2019-09-03 | 华南理工大学 | The recognition of face architecture design method planned as a whole based on edge calculations and cloud |
| CN110262421A (en) * | 2019-06-21 | 2019-09-20 | 深圳市美兆环境股份有限公司 | The control method and device of production equipment |
| CN110749462B (en) * | 2019-07-19 | 2021-05-07 | 华瑞新智科技（北京）有限公司 | Industrial equipment fault detection method and system based on edge calculation |
| CN110749462A (en) * | 2019-07-19 | 2020-02-04 | 华瑞新智科技（北京）有限公司 | Industrial equipment fault detection method and system based on edge calculation |
| CN110388964A (en) * | 2019-08-16 | 2019-10-29 | 深圳江行联加智能科技有限公司 | A kind of methods, devices and systems of tunnel cable data acquisition |
| CN110737732A (en) * | 2019-10-25 | 2020-01-31 | 广西交通科学研究院有限公司 | electromechanical equipment fault early warning method |
| CN111507489A (en) * | 2020-04-20 | 2020-08-07 | 电子科技大学中山学院 | Cloud-edge-coordinated amusement equipment fault prediction and health management system and method |
| CN111507489B (en) * | 2020-04-20 | 2023-04-18 | 电子科技大学中山学院 | Cloud-edge-coordinated amusement equipment fault prediction and health management system and method |
| CN111784077A (en) * | 2020-07-23 | 2020-10-16 | 国网浙江省电力有限公司检修分公司 | Method and device for predicting state of power equipment based on edge side |
| CN111970342A (en) * | 2020-08-03 | 2020-11-20 | 江苏方天电力技术有限公司 | Edge computing system of heterogeneous network |
| CN111970342B (en) * | 2020-08-03 | 2024-01-30 | 江苏方天电力技术有限公司 | Edge computing system of heterogeneous network |
| CN112015153B (en) * | 2020-09-09 | 2021-06-22 | 江南大学 | System and method for detecting abnormity of sterile filling production line |
| WO2022052510A1 (en) * | 2020-09-09 | 2022-03-17 | 江南大学 | Anomaly detection system and method for sterile filling production line |
| CN112015153A (en) * | 2020-09-09 | 2020-12-01 | 江南大学 | System and method for detecting abnormity of sterile filling production line |
| CN113064075A (en) * | 2021-03-16 | 2021-07-02 | 电子科技大学成都学院 | Motor life estimation method based on edge calculation and deep learning |
| CN112984953A (en) * | 2021-03-24 | 2021-06-18 | 苏州可米可酷食品有限公司 | Intelligent refrigeration industry process optimization method based on Rete algorithm |
| CN114543863A (en) * | 2022-02-10 | 2022-05-27 | 德闻仪器仪表(上海)有限公司 | Method for correcting zero drift of ultrasonic transducer |
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