CN111766511A - Fault diagnosis and predictive maintenance method for industrial motor - Google Patents
Fault diagnosis and predictive maintenance method for industrial motor Download PDFInfo
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- CN111766511A CN111766511A CN202010034528.6A CN202010034528A CN111766511A CN 111766511 A CN111766511 A CN 111766511A CN 202010034528 A CN202010034528 A CN 202010034528A CN 111766511 A CN111766511 A CN 111766511A
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- 238000003745 diagnosis Methods 0.000 title claims abstract description 28
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- 238000013528 artificial neural network Methods 0.000 claims abstract description 9
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- 230000001050 lubricating effect Effects 0.000 claims description 2
- 238000011161 development Methods 0.000 abstract description 6
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- 238000005516 engineering process Methods 0.000 description 2
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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
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- G—PHYSICS
- G01—MEASURING; TESTING
- 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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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
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- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
- G01M13/02—Gearings; Transmission mechanisms
- G01M13/021—Gearings
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
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- G01M13/028—Acoustic or vibration analysis
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
- G01M13/04—Bearings
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
- G01M13/04—Bearings
- G01M13/045—Acoustic or vibration analysis
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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Abstract
The invention discloses a fault diagnosis and predictive maintenance method of an industrial motor, which belongs to the technical field of industrial motors, and comprises the steps of obtaining motor related performance data through a motor-related sensor and a motor-related component, and respectively carrying out preliminary learning and deep learning on an AI intelligent robot based on a regression algorithm and a neural network algorithm, so that the AI intelligent robot in an initial state, which is unknown to the motor, is changed into a diagnosis expert in the technical field of motors, and meanwhile, data from the AI intelligent robot is based on a cloud server, which is a necessary trend in the existing development, so that a motor data model base is established, corresponding fault diagnosis and predictive maintenance conclusions are carried out, and meanwhile, the AI intelligent robot can also carry out deep learning based on the neural network algorithm in the analysis process, and the motor data model base is continuously perfected.
Description
Technical Field
The invention relates to the technical field of industrial motors, in particular to a fault diagnosis and predictive maintenance method of an industrial motor.
Background
Equipment maintenance is an important work of enterprises, wherein the equipment maintenance mode undergoes the changes of post-repair, preventive maintenance and predictive maintenance, the application of a state monitoring and fault diagnosis technology promotes the innovation of the maintenance mode, the predictive maintenance, also called predictive maintenance or on-demand maintenance, refers to the maintenance of equipment which is carried out when the equipment needs to be maintained according to monitoring and diagnosis, the predictive maintenance is based on the state monitoring and fault diagnosis, the actual running state of the equipment is taken as the basis, a maintenance plan is made according to the comprehensive production needs, and the maintenance is carried out according to the predetermined plan, and the equipment maintenance method has the advantages that: the maintenance cost is relatively lowest; reducing, or even avoiding catastrophic accidents; the shutdown times and time are reduced, and the shutdown loss is reduced; the maintenance period is prolonged, and the product yield is increased; excessive maintenance is avoided, and the service life of equipment and accessories is prolonged; the safety and the performance of equipment are ensured, and the product quality is ensured; maintenance personnel are reasonably configured and used; the stock and the consumption of spare parts are reduced; the safety of a factory is improved, and the environmental influence is improved; ensuring production plan and maintaining market image.
In a predictive maintenance system, the tasks of an internet of things terminal are data acquisition and transmission, and no fault diagnosis, alarm and prediction functions are undertaken, the tasks are undertaken by an industrial internet of things cloud platform, equipment state data acquisition in the maintenance process belongs to the field of industrial monitoring, namely, real-time data of equipment states are acquired, whether equipment has faults or not is judged according to the equipment states, and the time and content of equipment maintenance are determined, the industrial internet tries to balance large data volume transmission and reliability, but because an internet protocol needs to be modified, development cost and market demand cannot be guaranteed, and the high-speed development of a wireless network basically replaces the further development of the industrial internet of things.
Conventional predictive maintenance techniques rely on observing the trend of many key measurements over time, and by carefully analyzing the monitoring results, skilled analysts can find fluctuations that are meaningful to analyze and can be aware of the on-device failures that cause those fluctuations, and analysts are often confused about changes in the measurements, such as changes in rotational speed or load, caused by changes in operation, rather than faults, and the expense of building a system and analyzing the monitoring results is prohibitive to many potential users.
The application and development of the state monitoring and fault diagnosis technology are closely related to considerable fault loss and equipment maintenance cost, enterprises pursue future greater economic benefits over the years, production scale is continuously enlarged, production devices develop towards large-scale, automatic, continuous and single-series directions, key equipment in the devices is expensive and has no standby machines, once fault shutdown occurs, the whole device stops production, economic loss is huge, the proportion of the equipment maintenance cost in cost is large, and the equipment maintenance cost is a considerable number.
Disclosure of Invention
1. Technical problem to be solved
In view of the problems in the prior art, it is an object of the present invention to provide a fault diagnosis and predictive maintenance method for an industrial motor.
2. Technical scheme
In order to solve the problems, the invention adopts the following technical scheme:
a method for fault diagnosis and predictive maintenance of an industrial motor, comprising the steps of:
s1, motor data acquisition: acquiring motor related performance data through motor related sensors and motor related components;
s2, data analysis: transmitting the motor related performance data to an AI intelligent robot to obtain a fault diagnosis and predictive maintenance conclusion of the motor;
s3, maintenance: a predictive maintenance plan is automatically generated, and a user develops offline maintenance work based on the maintenance plan.
As a preferred aspect of the present invention, the motor performance data includes motor voltage signal data, motor current signal data, motor internal humidity data, motor internal temperature data, motor vibration data, and other motor operation data.
As a preferred aspect of the present invention, the motor-associated sensors include a voltage sensor, a current sensor, a temperature sensor, a humidity sensor, and a vibration sensor.
As a preferred aspect of the present invention, the voltage sensor and the current sensor are installed in a motor control cabinet, the voltage sensor collects a voltage signal of the motor, the current sensor collects a current signal of the motor, the temperature sensor, the humidity sensor and the vibration sensor are installed inside the motor, the temperature sensor collects internal temperature data of the motor, the humidity sensor collects internal humidity data of the motor, the vibration sensor provides vibration data of the motor, and the vibration data includes integral vibration data and a vibration peak value; the change of the frequency and the waveform of the overall vibration data obtained by the vibration sensor can reflect the problems of the shaft, such as unbalance, misalignment or mechanical looseness; this peak data provides a reliable measure of the effect on the machine, which as the peak level increases directly indicates problems to be created including poor lubrication, bearing failure or gear failure, process induced failures detected by increasing the overall vibration and peak data.
In a preferred embodiment of the present invention, the motor-related components include an ultrasonic detector and a thermal imager.
The ultrasonic detector and the thermal imager are arranged in the motor, the ultrasonic detector is used for tracking high-frequency friction, the friction monitoring directly shows the relative health condition of the equipment, the lubricating state of the equipment and the running state information of other mechanical parts can be obtained in time, and the state of the bearing is monitored; the thermal imager is used for receiving infrared rays emitted by any part of the motor, displaying the temperature distribution of the surface of the measured object through a colored picture, and finding out an abnormal point of the temperature according to the small difference of the temperature, thereby playing a role in maintenance.
In step S2, the specific embodiment is as follows:
s21, downloading relevant data information about the motor by the AI intelligent robot based on the cloud server;
s22, the AI intelligent robot preliminarily learns based on a regression algorithm, and establishes a preliminary motor database;
s23, establishing a motor data model base by the AI intelligent robot based on neural network algorithm deep learning;
s24, transmitting the motor related performance data to the AI intelligent robot;
and S25, the AI intelligent robot carries out data analysis on the motor related performance data and the motor data model base to obtain the fault diagnosis and the predictive maintenance conclusion of the motor, and automatically generates a predictive maintenance plan.
3. Advantageous effects
Compared with the prior art, the invention has the advantages that:
according to the scheme, motor related performance data are obtained through a motor related sensor and a motor related component, and preliminary learning and deep learning are respectively carried out on an AI intelligent robot based on a regression algorithm and a neural network algorithm, so that the AI intelligent robot without knowing the motor in an initial state is rapidly changed into a diagnosis expert in the technical field of the motor, meanwhile, data from the AI intelligent robot is based on a cloud server, which is a necessary situation in the existing development, a motor data model base is established, corresponding fault diagnosis and predictive maintenance conclusion are carried out, meanwhile, the AI intelligent robot can also carry out deep learning based on the neural network algorithm in the analysis process, and the motor data model base is continuously perfected.
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. It is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and all other embodiments obtained by those skilled in the art without any inventive work are within the scope of the present invention.
Example (b):
a method for fault diagnosis and predictive maintenance of an industrial motor, comprising the steps of:
s1, motor data acquisition: the motor related performance data is obtained through a motor related sensor and a motor related component, the motor performance data comprises motor voltage signal data, motor current signal data, motor internal humidity data, motor internal temperature data, motor vibration data and other motor operation data, the motor related sensor comprises a voltage sensor, a current sensor, a temperature sensor, a humidity sensor and a vibration sensor, the voltage sensor collects voltage signals of the motor, the current sensor collects current signals of the motor, the temperature sensor, the humidity sensor and the vibration sensor are arranged in the motor, the temperature sensor collects internal temperature data of the motor, specifically, the temperature data is used for judging whether the motor is normal or not in operation, and the humidity sensor collects internal humidity data of the motor, the humidity data mainly reflects whether the interior of the motor is wet or not, and the vibration sensor provides vibration data of the motor, wherein the vibration data comprise integral vibration data and vibration peak values; the change of the frequency and the waveform of the overall vibration data obtained by the vibration sensor can reflect the problems of the shaft, such as unbalance, misalignment or mechanical looseness; the peak data provides a reliable measure of the impact on the machine, it directly displays the problems to be generated as the peak level increases, the problems include poor lubrication, bearing failure or gear failure, the failure caused by the process is detected by increasing the overall vibration and peak data, the motor-associated components include an ultrasonic detector and a thermal imager, the ultrasonic detector is used to track the high frequency friction, the friction monitoring directly indicates the relative health of the equipment, the lubrication status of the equipment and the operating status information of other mechanical parts (bearing, gear, coupler, pump impeller) can be known in time, the bearing status monitoring; the thermal imager is used for receiving infrared rays emitted by any part of the motor, displaying the temperature distribution of the surface of the measured object through a colored picture, and finding out an abnormal point of the temperature according to the small difference of the temperature, thereby playing a role in maintenance.
S2, data analysis: the motor related performance data is transmitted to the AI intelligent robot, and the fault diagnosis and the predictive maintenance conclusion of the motor are obtained, wherein the specific implementation mode is as follows:
s21, downloading relevant data information about the motor by the AI intelligent robot based on the cloud server;
s22, the AI intelligent robot preliminarily learns based on a regression algorithm, and establishes a preliminary motor database;
s23, establishing a motor data model base by the AI intelligent robot based on neural network algorithm deep learning;
s24, transmitting the motor related performance data to the AI intelligent robot;
and S25, the AI intelligent robot performs data analysis on the motor related performance data and the motor data model base to obtain fault diagnosis and predictive maintenance conclusions of the motor, automatically generates a predictive maintenance plan, and performs deep learning based on a neural network algorithm in the analysis process to continuously perfect the motor data model base.
S3, maintenance: a predictive maintenance plan is automatically generated, and a user develops offline maintenance work based on the maintenance plan.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the equivalent replacement or change according to the technical solution and the modified concept of the present invention should be covered by the scope of the present invention.
Claims (7)
1. A method for fault diagnosis and predictive maintenance of an industrial motor, comprising the steps of:
s1, motor data acquisition: acquiring motor related performance data through motor related sensors and motor related components;
s2, data analysis: transmitting the motor related performance data to an AI intelligent robot to obtain a fault diagnosis and predictive maintenance conclusion of the motor;
s3, maintenance: a predictive maintenance plan is automatically generated, and a user develops offline maintenance work based on the maintenance plan.
2. The method of fault diagnosis and predictive maintenance of an industrial motor according to claim 1, wherein said motor performance data includes motor voltage signal data, motor current signal data, motor internal humidity data, motor internal temperature data, motor vibration data and other motor operating data.
3. The method of fault diagnosis and predictive maintenance of industrial motors of claim 2, wherein the motor-associated sensors include voltage sensors, current sensors, temperature sensors, humidity sensors and vibration sensors.
4. The method of claim 3, wherein the voltage sensor and the current sensor are installed in a motor control cabinet, the voltage sensor collects a voltage signal of the motor, the current sensor collects a current signal of the motor, the temperature sensor, the humidity sensor and the vibration sensor are installed inside the motor, the temperature sensor collects internal temperature data of the motor, the humidity sensor collects internal humidity data of the motor, the vibration sensor provides vibration data of the motor, the vibration data comprises overall vibration data and vibration peak values; the change of the frequency and the waveform of the overall vibration data obtained by the vibration sensor can reflect the problems of the shaft, such as unbalance, misalignment or mechanical looseness; this peak data provides a reliable measure of the effect on the machine, which as the peak level increases directly indicates problems to be created including poor lubrication, bearing failure or gear failure, process induced failures detected by increasing the overall vibration and peak data.
5. The method of fault diagnosis and predictive maintenance of industrial motors of claim 2, wherein the motor associated components include an ultrasonic detector and a thermal imager.
6. The method for fault diagnosis and predictive maintenance of industrial motors of claim 5, wherein the ultrasonic detector and the thermal imager are installed inside the motor, the ultrasonic detector is used for tracking the high-frequency friction, the friction monitoring directly shows the relative health condition of the equipment, the lubricating state of the equipment and the running state information of other mechanical parts can be timely obtained, and the bearing state is monitored;
the thermal imager is used for receiving infrared rays emitted by any part of the motor, displaying the temperature distribution of the surface of the measured object through a colored picture, and finding out an abnormal point of the temperature according to the small difference of the temperature, thereby playing a role in maintenance.
7. The method for fault diagnosis and predictive maintenance of an industrial motor according to claim 1, wherein in step S2, the specific implementation is as follows:
s21, downloading relevant data information about the motor by the AI intelligent robot based on the cloud server;
s22, the AI intelligent robot preliminarily learns based on a regression algorithm, and establishes a preliminary motor database;
s23, establishing a motor data model base by the AI intelligent robot based on neural network algorithm deep learning;
s24, transmitting the motor related performance data to the AI intelligent robot;
and S25, the AI intelligent robot performs data analysis on the motor related performance data and the motor data model base to obtain fault diagnosis and predictive maintenance conclusions of the motor, automatically generates a predictive maintenance plan, and performs deep learning based on a neural network algorithm in the analysis process to continuously perfect the motor data model base.
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| CN202010034528.6A CN111766511A (en) | 2020-01-14 | 2020-01-14 | Fault diagnosis and predictive maintenance method for industrial motor |
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| CN202010034528.6A CN111766511A (en) | 2020-01-14 | 2020-01-14 | Fault diagnosis and predictive maintenance method for industrial motor |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112396226A (en) * | 2020-11-16 | 2021-02-23 | 中国第一汽车股份有限公司 | Intelligent predictive equipment maintenance method and device based on mobile communication |
| CN113074940A (en) * | 2021-03-18 | 2021-07-06 | 昆明理工大学 | Rolling bearing health state estimation system and method based on OS-ELM |
| CN114442571A (en) * | 2021-12-28 | 2022-05-06 | 上海繁易信息科技股份有限公司 | Production equipment management method, controller and system based on industrial Internet of things |
| CN114764111A (en) * | 2021-01-14 | 2022-07-19 | 广州中国科学院先进技术研究所 | Non-access type machine fault prediction system |
| CN114955346A (en) * | 2022-06-18 | 2022-08-30 | 河南中烟工业有限责任公司 | Health detection system for piece cigarette warehousing sorting electromechanical equipment |
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| CN107645685A (en) * | 2017-09-22 | 2018-01-30 | 广东欧珀移动通信有限公司 | Information processing method, device, terminal device and storage medium |
| CN108921303A (en) * | 2018-05-29 | 2018-11-30 | 青岛鹏海软件有限公司 | The Fault diagnosis and forecast maintaining method of industrial motor |
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- 2020-01-14 CN CN202010034528.6A patent/CN111766511A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107645685A (en) * | 2017-09-22 | 2018-01-30 | 广东欧珀移动通信有限公司 | Information processing method, device, terminal device and storage medium |
| CN108921303A (en) * | 2018-05-29 | 2018-11-30 | 青岛鹏海软件有限公司 | The Fault diagnosis and forecast maintaining method of industrial motor |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112396226A (en) * | 2020-11-16 | 2021-02-23 | 中国第一汽车股份有限公司 | Intelligent predictive equipment maintenance method and device based on mobile communication |
| CN114764111A (en) * | 2021-01-14 | 2022-07-19 | 广州中国科学院先进技术研究所 | Non-access type machine fault prediction system |
| CN113074940A (en) * | 2021-03-18 | 2021-07-06 | 昆明理工大学 | Rolling bearing health state estimation system and method based on OS-ELM |
| CN114442571A (en) * | 2021-12-28 | 2022-05-06 | 上海繁易信息科技股份有限公司 | Production equipment management method, controller and system based on industrial Internet of things |
| CN114955346A (en) * | 2022-06-18 | 2022-08-30 | 河南中烟工业有限责任公司 | Health detection system for piece cigarette warehousing sorting electromechanical equipment |
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Application publication date: 20201013 |