CN109144014B - System and method for detecting operation condition of industrial equipment - Google Patents

System and method for detecting operation condition of industrial equipment Download PDF

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Publication number
CN109144014B
CN109144014B CN201811177989.8A CN201811177989A CN109144014B CN 109144014 B CN109144014 B CN 109144014B CN 201811177989 A CN201811177989 A CN 201811177989A CN 109144014 B CN109144014 B CN 109144014B
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data
fault
equipment
bearing
state
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CN109144014A (en
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朱伟超
杨冬
王洪超
黄雪峰
关山
庞龙
李雪松
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Beijing Jiaotong University
Beijing Sheenline Group Co Ltd
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Beijing Jiaotong University
Beijing Sheenline Group Co Ltd
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
    • G05B19/4183—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by data acquisition, e.g. workpiece identification
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/55—Push-based network services
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/31—From computer integrated manufacturing till monitoring
    • G05B2219/31282—Data acquisition, BDE MDE
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

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  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • General Engineering & Computer Science (AREA)
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  • Quality & Reliability (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Testing And Monitoring For Control Systems (AREA)

Abstract

The invention discloses a system and a method for detecting the operation condition of industrial equipment, which comprises the following steps: the acquisition module is used for acquiring state data of key parts on the industrial equipment and transmitting the state data to the local gateway through the communication link; the local gateway module is used for analyzing the state data through the local gateway to obtain physical data according to the binary data stream, storing the physical data to the local and simultaneously pushing the physical data to the cloud server; the cloud platform module is used for storing the physical data to a preset data storage unit through the cloud server, displaying the physical data on a single-page Web application in a preset chart and/or characters, detecting the current operation state of the industrial equipment according to the physical data, and further identifying the fault type of the industrial equipment when the current operation state is the fault state. The system improves the efficiency of managing the equipment by a factory, reduces the maintenance cost of the equipment by an enterprise, improves the detection accuracy, and is simple and easy to realize.

Description

System and method for detecting operation condition of industrial equipment
Technical Field
The invention relates to the technical field of intelligent systems, in particular to a system and a method for detecting the operation condition of industrial equipment.
Background
The stability of the operation of the industrial equipment greatly affects the production efficiency of enterprises, and further affects the benefits of the enterprises, and meanwhile, the safety problem of the operation of the industrial equipment is also an important component in the safety production content of the enterprises. The current industrial equipment maintenance mainly adopts a mode of planning maintenance, and the maintenance system has the following problems:
(1) because the safety overhaul period is unreasonable, the potential safety hazard cannot be found in time, and thus, the major potential safety hazard is caused;
(2) unnecessary planned maintenance causes waste of manpower and material resources, and causes overhigh maintenance cost;
(3) excessive maintenance may damage the equipment and cause additional damage to mechanical equipment;
(4) the traditional maintenance method depends on the technical level and working experience of maintenance personnel, so that the problems of low maintenance efficiency, inaccurate diagnosis result and the like are caused;
with the increasing complexity of the structure and the function of mechanical equipment and the increasing automation degree, the requirements of the industry on the safety and the reliability of the equipment are higher and higher, and the requirements of maintenance and guarantee cannot be met in many fields after maintenance and periodic maintenance. Therefore, the method can acquire the running state information of the equipment at the first time, observe the performance trend of the equipment in real time and intelligently judge the fault type of the equipment, and has important significance on the production safety, the production efficiency and the generation cost of enterprises.
The industrial equipment monitoring and fault diagnosis system is a special system for data acquisition, state monitoring and fault diagnosis by using a computer control system and a data communication technology. At present, the application range of the equipment monitoring system in most factories is limited to local, remote monitoring cannot be carried out, once a problem occurs, technicians far away from the field cannot know the running state of the equipment immediately, preparation cannot be made in advance for specific faults, and debugging and analysis can only be carried out on the site, so that the maintenance process becomes very inconvenient, the efficiency is low, the maintenance period is prolonged, and the maintenance cost is increased.
With continuous operation of mechanical equipment throughout the year, a large amount of historical data capable of reflecting the operation state and trend of the equipment is continuously accumulated, and the production data of the industrial mechanical equipment is precious. However, the current industrial equipment monitoring and fault diagnosis system does not have a data management platform of a set of system to store the data, which causes data waste. Moreover, the conventional equipment fault diagnosis method is difficult to make effective decisions on conditions along with the increasing complexity and automation degree of the structure and function of mechanical equipment based on an expert system, and an advanced data mining theory and an algorithm model are urgently needed for fault diagnosis.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, an object of the present invention is to provide a system for detecting an operation status of an industrial device, which improves efficiency of a factory for device management, reduces maintenance cost of an enterprise for the device, improves accuracy of detection, and is simple and easy to implement.
Another object of the present invention is to provide a method for detecting the operating condition of an industrial device.
In order to achieve the above object, an embodiment of an aspect of the present invention provides a system for detecting an operating condition of an industrial device, including: the system comprises an acquisition module, a local gateway and a communication module, wherein the acquisition module is used for acquiring state data of key parts on industrial equipment and transmitting the state data to the local gateway through a communication link; the local gateway module is used for analyzing the state data through the local gateway so as to obtain physical data according to binary data streams, storing the physical data to the local and simultaneously pushing the physical data to a cloud server; the cloud platform module is used for storing the physical data to a preset data storage unit through the cloud server, displaying the physical data on a single-page Web application in a preset chart and/or characters, detecting the current operation state of the industrial equipment according to the physical data, and further identifying the fault type of the industrial equipment when the current operation state is the fault state.
The system for detecting the operation condition of the industrial equipment, provided by the embodiment of the invention, has the functions of remotely monitoring the operation condition of the factory equipment in real time, judging whether the equipment has a fault and giving out a fault type, namely, remotely monitoring the operation condition of the industrial equipment in real time and diagnosing the fault by combining an algorithm model and utilizing a large data platform, so that the remote real-time monitoring and fault diagnosis of the operation condition of the industrial equipment are realized, the efficiency of the factory on equipment management is improved, the maintenance cost of an enterprise on the equipment is reduced, the detection accuracy is improved, and the system is simple and easy to realize.
In addition, the system for detecting the operating condition of the industrial equipment according to the above embodiment of the present invention may further have the following additional technical features:
further, in an embodiment of the present invention, the key parts include one or more parts of an electrical box, a column, a rotating chassis, a speed reducer, a motor, a speed reducer, an upper bearing, a lower bearing, and a programmable logic controller, and the status data includes one or more of current data, vibration data, rotational speed data, swing data, temperature data, and programmable logic controller data.
Further, in one embodiment of the present invention, the acquisition module includes: a communication link; the sensor unit comprises a current Hall ring, a three-axis acceleration vibration sensor, a rotating speed sensor, a displacement sensor and/or a temperature sensor, wherein the current Hall ring is arranged in the electric box, the three-axis acceleration vibration sensor is arranged on the upright post, the rotating speed sensor is arranged on the rotating chassis, the displacement sensor is arranged on the speed reducer, and a plurality of temperature sensors are respectively arranged on the motor, the speed reducer, the upper bearing and the lower bearing; and the control unit is used for converting the analog signals collected by the sensor into digital signals and storing the digital signals into a register.
Further, in one embodiment of the present invention, the local gateway module includes: the data reading unit is used for reading the state data according to a preset frequency; the data analysis unit is used for analyzing the state data to obtain the physical data; a local storage unit for storing the physical data; and the remote pushing unit is used for pushing the physical data.
Further, in an embodiment of the present invention, the cloud platform module includes: a data storage unit; a data analysis unit; and a data presentation unit.
In order to achieve the above object, another embodiment of the present invention provides a method for detecting an operating condition of an industrial device, including: collecting state data of key parts on industrial equipment, and transmitting the state data to a local gateway through a communication link; analyzing the state data through the local gateway to obtain physical data according to a binary data stream, storing the physical data to the local, and simultaneously pushing the physical data to a cloud server; storing the physical data to a preset data storage unit through the cloud server, displaying the physical data on a single-page Web application in a preset chart and/or characters, detecting the current operation state of the industrial equipment according to the physical data, and further identifying the fault type of the industrial equipment when the current operation state is the fault state.
The method for detecting the operation condition of the industrial equipment in the embodiment of the invention has the advantages that the operation condition of the industrial equipment is remotely monitored in real time, whether the equipment has a fault or not is judged, and the fault type is given, namely, the operation condition of the industrial equipment is remotely monitored in real time and is diagnosed by utilizing a large data platform in combination with an algorithm model, so that the remote real-time monitoring and fault diagnosis of the operation condition of the industrial equipment are realized, the efficiency of the equipment management of a factory is improved, the maintenance cost of an enterprise on the equipment is reduced, the detection accuracy is improved, and the method is simple and easy to realize.
In addition, the method for detecting the operating condition of the industrial equipment according to the above embodiment of the present invention may further have the following additional technical features:
further, in an embodiment of the present invention, the method further includes: generating an alarm signal according to the fault type; and sending the alarm signal to a preset terminal through a mail and/or a short message.
Further, in one embodiment of the invention, the fault type is one or more of a motor fault, a reducer fault, or a bearing fault.
Further, in one embodiment of the present invention, the key parts include one or more of an electric box, a column, a rotating chassis, a speed reducer, a motor, a speed reducer, an upper bearing and a lower bearing.
Further, in one embodiment of the invention, the status data comprises one or more of current data, vibration data, rotational speed data, swing data, temperature data, and programmable logic controller data.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic block diagram of a system for detecting the operational condition of an industrial plant according to one embodiment of the present invention;
FIG. 2 is a schematic block diagram of a system for detecting the operational condition of an industrial plant according to an embodiment of the present invention;
FIG. 3 is a block diagram of a design of a local gateway module of a system for detecting the operational status of an industrial device according to one embodiment of the present invention;
FIG. 4 is a diagram of a big data platform architecture for a system for detecting operational conditions of an industrial plant according to one embodiment of the present invention;
FIG. 5 is a flow diagram of a method for presenting industrial equipment status data according to one embodiment of the present invention;
FIG. 6 is a flow chart of a method of detecting an operating condition of an industrial device according to one embodiment of the present invention;
FIG. 7 is a flow diagram of a method for fault diagnosis of industrial equipment according to one embodiment of the present invention;
FIG. 8 is a schematic diagram of a fault diagnosis data characteristic table format according to an embodiment of the invention;
FIG. 9 is a display diagram of real-time data collected from sensors, according to one embodiment of the invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The following describes a system and a method for detecting an operating condition of an industrial device according to an embodiment of the present invention with reference to the drawings, and first, a system for detecting an operating condition of an industrial device according to an embodiment of the present invention will be described with reference to the drawings.
Fig. 1 is a schematic configuration diagram of a system for detecting an operating condition of an industrial device according to an embodiment of the present invention.
As shown in fig. 1, the system 10 for detecting the operating condition of the industrial equipment includes: the system comprises an acquisition module 100, a local gateway module 200 and a cloud platform module 300.
The acquisition module 100 is configured to acquire status data of a key portion on the industrial device, and transmit the status data to the local gateway through the communication link. The local gateway module 200 is configured to parse the state data through the local gateway to obtain physical data according to the binary data stream, store the physical data locally, and push the physical data to the cloud server. The cloud platform module 300 is configured to store physical data in a preset data storage unit through a cloud server, present the physical data on a single-page Web application in a preset chart and/or characters, detect a current operation state of the industrial device according to the physical data, and further identify a fault type of the industrial device when the current operation state is a fault state. The device 10 of the embodiment of the invention has the functions of remotely monitoring the operation condition of the factory equipment in real time, judging whether the equipment has a fault and giving the fault type, thereby improving the efficiency of the factory to manage the equipment, reducing the maintenance cost of enterprises to the equipment, improving the accuracy of detection and being simple and easy to realize.
Specifically, as shown in fig. 2, the data collection module 100 is used to collect data of each important component on the industrial equipment through various sensors, and then transmit the data to the local gateway module through the communication link. The local gateway module 200 is configured to read data and parse the data, and perform local storage and push the parsed data to the cloud. The cloud platform module 300 is configured to perform distributed data storage of big data and analyze data, further perform fault diagnosis on the device, and simultaneously present the acquired real-time data and data analysis results on the cloud platform.
The system 10 for detecting the operating condition of an industrial plant will be described in detail with reference to the following embodiments.
Further, in one embodiment of the present invention, the acquisition module 100 comprises: a communication link, a sensor unit and a control unit.
Wherein the communication link; the sensor unit comprises a current Hall ring, a three-axis acceleration vibration sensor, a rotating speed sensor, a displacement sensor and/or a temperature sensor, wherein the current Hall ring is arranged in the electric box, the three-axis acceleration vibration sensor is arranged on the stand column, the rotating speed sensor is arranged on the rotating chassis, the displacement sensor is arranged on the speed reducer, and the temperature sensors are respectively arranged on the motor, the speed reducer, the upper bearing and the lower bearing; and the control unit is used for converting the analog signals collected by the sensor into digital signals and storing the digital signals into the register.
Optionally, in an embodiment of the present invention, the key component includes one or more of an electrical box, a column, a rotating chassis, a speed reducer, a motor, a speed reducer, an upper bearing, a lower bearing, and a programmable logic controller, and the status data includes one or more of current data, vibration data, rotational speed data, swing data, temperature data, and programmable logic controller data.
Specifically, as shown in fig. 2, the data acquisition module further includes three units, i.e., a sensor unit, a control unit and a communication link unit. The data acquisition module is used for acquiring data of each important part on the industrial equipment through various sensors and then transmitting the acquired data to the local gateway module through the communication link. Important parts on the industrial equipment comprise an electric box, a motor, a speed reducer, a rotating chassis, an upright post, an upper bearing, a lower bearing and a programmable logic controller.
The sensor unit is the source of data, and required sensor has hall ring, triaxial acceleration vibration sensor, speed sensor, displacement sensor and temperature sensor, wherein hall ring is used for gathering A, B, C three-phase current's in the collection box data, triaxial acceleration vibration sensor is used for gathering the vibration data of stand, speed sensor is used for gathering the rotational speed data of rotating the chassis, displacement sensor is used for gathering the displacement data that the speed reducer rocked about during operation, temperature sensor is used for gathering the temperature data on motor, speed reducer, upper bearing and the lower bearing. Data in the programmable logic controller can be directly read by Socket communication without any sensor equipment. And finally obtaining six types of data including current data, vibration data, rotating speed data, swing amplitude data, temperature data and programmable logic controller data, wherein the purpose of obtaining the programmable logic controller data is to learn whether the equipment is in a working state at present.
The embodiment of the invention collects the data of five sensors and the state data of one device, thereby not only being capable of acquiring the running state of the device from multiple parts and multiple dimensions in real time, but also being convenient for algorithm modeling in fault diagnosis, leading the data set to be larger and the data characteristics to be more, and greatly improving the accuracy of fault judgment.
The control unit is the acquisition card shown in fig. 1, and comprises a singlechip minimum control system and a digital-to-analog conversion module. The function of the system is that analog signals acquired by the sensor are converted into digital signals through the acquisition card, and then the acquisition card stores the converted digital signals into a corresponding register of the chip to wait for being read by the local gateway.
The communication link unit comprises two links, specifically, a communication link from the sensor to the acquisition card, from the acquisition card to the serial server, from the serial server to the local gateway, and a communication link from the programmable logic controller to the local gateway. The communication protocol from the sensor to the minimum acquisition module to the serial port server is a Modbus serial communication protocol, the adopted communication interface is RS485, the communication protocol from the serial port server to the local gateway is a TCP protocol, and the adopted communication interface is Ethernet; the programmable logic controller is directly connected to the local gateway through the ethernet using the TCP protocol.
Further, in one embodiment of the present invention, the local gateway module 200 includes: the device comprises a data reading unit, a data analysis unit, a local storage unit and a remote pushing unit.
The data reading unit is used for reading the state data according to a preset frequency. The data analysis unit is used for analyzing the state data to obtain physical data. The local storage unit is used for storing physical data. The remote pushing unit is used for pushing physical data.
Specifically, as shown in fig. 3, the local gateway module includes a data reading unit, a data parsing unit, a local storage unit and a remote pushing unit. The data reading unit is responsible for reading sensor data in corresponding register addresses in the acquisition card chip by using a serial port communication protocol according to the specified frequency and then receiving the data transmitted from the acquisition card. The data analysis unit analyzes received binary data into actual physical values according to the types of different sensor values, the local storage unit stores the data, and the remote pushing unit pushes the data to the cloud platform module through a wireless communication link by adopting an MQTT protocol. The local storage unit is used for locally backing up data, only data of nearly seven days are stored, and data of more than seven days are deleted.
Further, in one embodiment of the present invention, the cloud platform module 300 includes: a data storage unit; a data analysis unit; and a data presentation unit.
Specifically, as shown in fig. 4 and 5, fig. 4 is a diagram of a large data platform architecture, which includes two functions of storage and analysis. The storage unit comprises a Hadoop distributed file system and a Hive data warehouse tool, and is used for storing received data into the distributed file system, performing backup disaster tolerance and dynamic capacity expansion on mass data regularly, and providing data query and analysis capabilities. The data analysis unit comprises a series of regularly executed data statistical analysis scripts and fault diagnosis algorithms, and is used for carrying out annual, quarterly and monthly statistics on various indexes of historical data, and simultaneously predicting data in a period of time window in real time by using a trained equipment fault diagnosis model to give a diagnosis result. Fig. 5 is a flowchart of a data presentation method, and the data presentation unit includes a single-page Web application for presenting, in real time, the collected multidimensional sensor data and historical statistical data of various indexes in the form of a graph, and displaying a current operating state of each device, and displaying a corresponding fault type if a fault occurs.
According to the detection system for the operation condition of the industrial equipment, provided by the embodiment of the invention, the functions of acquiring real-time data of the operation of the equipment, remotely monitoring the state of the equipment in real time and timely alarming the fault event of the equipment are realized by acquiring data such as current, temperature, vibration and the like on the industrial equipment and matching the local gateway with the cloud platform module, and the system can enable workers to quickly find problems, improve the maintenance efficiency, carry out remote guidance when necessary, greatly reduce the maintenance cost of the equipment, and provide services such as field data acquisition, operation process monitoring, historical data statistics, equipment fault alarming and the like for enterprises.
Next, a method for detecting an operating condition of an industrial device according to an embodiment of the present invention will be described with reference to the drawings.
Fig. 6 is a flowchart of a method for detecting an operating condition of an industrial device according to an embodiment of the present invention.
As shown in fig. 6, the method for detecting the operation status of the industrial equipment includes the following steps:
in step S601, status data of a critical part on the industrial device is collected and transmitted to the local gateway through the communication link.
It is understood that embodiments of the present invention utilize sensors to collect status data of critical components on the industrial equipment, control the local gateway to read the sensor data and the programmable logic controller data, and then transmit the data to the local gateway via a wired communication link. The communication interface is converted into an Ethernet interface from an RS485 communication interface by using a serial server in the middle of the wired communication link, a transmission protocol is converted into a TCP protocol from a Modbus serial communication protocol, and finally data are transmitted to the local gateway through the Ethernet interface.
In one embodiment of the invention, the key parts comprise one or more parts of an electric box, a stand column, a rotating chassis, a speed reducer, a motor, a speed reducer, an upper bearing and a lower bearing; the status data includes one or more of current data, vibration data, rotational speed data, swing data, temperature data, and programmable logic controller data.
It can be understood that the installation positions of various sensors are that a current Hall ring needs to be arranged in an electric box to measure A, B, C three-phase current; the three-axis acceleration vibration sensor is arranged on the upright post; the rotating speed sensor is arranged on the rotating chassis; the displacement sensor is arranged on the speed reducer; the temperature sensors need to be arranged at four positions, namely a motor, a speed reducer, an upper bearing and a lower bearing. Wherein, the acquisition frequency of each type of sensor is 10Hz for the Hall ring sensor and the temperature sensor, 5Hz for the displacement sensor, 1Hz for the rotating speed sensor and 2000Hz for the vibration sensor.
In step S602, the state data is analyzed through the local gateway to obtain physical data according to the binary data stream, and the physical data is pushed to the cloud server while being stored locally.
It can be understood that the collected binary data stream is analyzed in the local gateway, the analyzed actual physical data is stored locally, and the analyzed actual physical data is pushed to the cloud platform by using the MQTT protocol.
Specifically, the data analysis method is to read the value in the corresponding register address in the designated control unit module through the device id, continuously read two bytes, and finally obtain the true value:
Figure BDA0001824274210000071
wherein v isiIndicating the value of the register read, tmaxRepresenting the maximum value of the range of the control unit module, tminRepresenting the minimum value of the control unit module range.
And the gateway packages the analyzed sensor data of each channel once every 1 second and transmits the packaged data to the cloud platform. The communication mode of the local gateway and the cloud platform for transmitting data adopts a wireless communication mode, the communication protocol adopts an MQTT protocol, the data is transmitted to the cloud platform by using a publish/subscribe message mode, and a publish and subscribe theme needs to be negotiated with the cloud platform before the data is transmitted.
In step S603, the physical data is stored in the preset data storage unit through the cloud server, the physical data is displayed on the single-page Web application in a preset chart and/or characters, the current operation state of the industrial device is detected according to the physical data, and when the current operation state is the fault state, the fault type of the industrial device is further identified.
It can be understood that the cloud platform receives data sent from the local gateway in real time, and on one hand, stores the data in the big data storage unit; on one hand, various data are displayed on a single-page Web application in a form of appropriate diagrams or characters in real time, so that any terminal equipment accessing a cloud platform can remotely monitor the running state of the equipment in real time; meanwhile, the background can also judge the equipment fault type by using an equipment fault diagnosis algorithm model, and the diagnosis result obtained by the model is stored in a database and finally presented on a Web page.
Specifically, the single-page Web application adopts an MQTT Websocket technology to subscribe a theme and acquire real-time data of the sensor, and the data display mode is as follows: the current and the temperature are displayed by using a line graph, the rotating speed is displayed by using an instrument panel, the swing amplitude is displayed by using a bar graph, and the vibration data are subjected to FFT (fast Fourier transform) to obtain main frequency data and then are displayed by using the line graph. The cloud platform stores the programmable logic controller data received in real time into a Redis database, and the single-page Web application acquires the programmable logic controller data by requesting an REST interface once per second, so that whether the equipment is in a working state or not is known, and the equipment is displayed in a text form. The big data storage platform can be a Hadoop distributed file system, a Hive tool is used for regularly running a statistical script, and historical data statistical information is stored in a MySql database; and the single-page Web application acquires data in the MySql database by requesting an REST interface once per second, and outputs the result to the single-page Web application.
Further, in an embodiment of the present invention, the method further includes: generating an alarm signal according to the fault type; and sending the alarm signal to a preset terminal through a mail and/or a short message.
It can be understood that after the judgment is carried out by the equipment fault diagnosis algorithm, if the equipment has a fault, the alarm is carried out in the form of mails and short messages, and the alarm is notified to relevant workers.
Further, in one embodiment of the invention, the fault type is one or more of a motor fault, a reducer fault, or a bearing fault.
Specifically, the equipment fault types are divided into motor faults, speed reducer faults and bearing faults; the cloud platform receives data in real time and sets a time window, when the data is accumulated in the time window, the algorithm model counts the average value and variance of current in the time window, the average value and variance of temperature, the average value and method of swing amplitude and the average value of rotating speed as model characteristics, and a support vector machine algorithm model is used for modeling to judge motor faults and reducer faults; the algorithm model directly uses vibration data as input and uses a GRU model in a recurrent neural network to diagnose bearing faults.
And the equipment fault diagnosis algorithm stores the judgment result into a Redis database, and the single-page Web application acquires the diagnosis result data in the Redis database in a mode of requesting an REST interface once per second and then outputs the diagnosis result data to the single-page Web application.
The method for detecting the operating condition of the industrial equipment is further described with reference to specific embodiments.
S1, arranging the sensor on a key component of the industrial equipment, collecting corresponding data to a register of the collection card by using a Modbus serial communication protocol, then arranging a data reading unit in the local gateway module, reading the data in the register of the collection card at a certain frequency, and converting the read data into TCP byte stream through the serial server for receiving.
The data reading unit in the local gateway module respectively acquires frequencies of a Hall ring sensor and a temperature sensor of 10Hz, a displacement sensor of 5Hz, a rotating speed sensor of 1Hz and a vibration sensor of 2000Hz for various sensors.
S2, after receiving the data, the data reading unit in the local gateway module transmits the data to the data parsing unit, as shown in fig. 3. The data analysis unit analyzes the acquired binary data stream, stores the analyzed actual physical data into a local MongoDB database, and meanwhile, the data analysis unit directly pushes the analyzed data to the cloud platform by using an MQTT protocol.
The data analysis method comprises the steps of reading a value in a corresponding register address in an appointed control unit module through a device id, continuously reading two bytes, and finally obtaining a true value as follows:
Figure BDA0001824274210000091
wherein v isiIndicating the value of the register read, tmaxRepresenting the maximum value of the range of the control unit module, tminRepresenting the minimum value of the control unit module range.
The analyzed data needs to be packed and sent once in one second before being sent to the cloud platform, so that the sending frequency can be reduced, and the data flow can be saved. The communication mode of the local gateway and the cloud platform for transmitting data adopts a wireless communication mode, the communication protocol adopts an MQTT protocol, the data is transmitted to the cloud platform by using a publish/subscribe message mode, and a publish and subscribe theme needs to be negotiated with the cloud platform before the data is transmitted. The MQTT protocol is an internet of things transmission protocol and is suitable for scenes with low power consumption and limited bandwidth, and corresponding data can be obtained by the cloud platform as long as the cloud platform subscribes a required theme.
S3, the cloud platform subscribes data sent from the gateway through an MQTT protocol, on one hand, the data are stored in the big data storage unit for data storage, and then the data analysis unit is used for performing historical data analysis on the stored data, as shown in fig. 4. On one hand, various data are displayed on a single-page Web application in a form of appropriate diagrams or characters in real time, so that any terminal equipment accessing a cloud platform can remotely monitor the running state of the equipment in real time, and a specific method is shown in FIG. 5. Meanwhile, the background can also judge the equipment fault type by using an equipment fault diagnosis algorithm model, and the diagnosis result obtained by the model is stored in a database and finally presented on a Web page. After the judgment is carried out by the equipment fault diagnosis algorithm, if the equipment has a fault, the alarm is carried out in the form of mails and short messages, and the alarm is notified to relevant workers.
Data received by the cloud platform can be divided into two categories, namely sensor data and programmable logic controller data. For sensor data, a Hadoop distributed file system, namely HDFS, is used as a storage platform of a big data storage unit in the cloud platform. The cloud platform firstly stores sensor data subscribed from the MQTT into a local temporary file, and then periodically runs a script file to import the data in the temporary file into the HDFS. This has the advantage of providing a high reliability and scalability of the storage system. Meanwhile, the data analysis unit utilizes the HIVE data warehouse to periodically execute the SQL script, queries the data stored in the HDFS, counts and calculates various data indexes of year, quarter and month, and stores the statistical result into the MySQL database.
For the programmable logic controller data, a Python script is run by a cloud platform background to subscribe and receive the data in real time, meanwhile, the received data is analyzed, and then the analyzed data is stored in a Redis database.
The data analysis unit needs to perform offline analysis on the historical data to count various index data, and also needs to perform current fault diagnosis on the equipment by using a fault diagnosis algorithm model according to the real-time data. The invention can diagnose three fault types, namely motor fault, speed reducer fault and bearing fault. The process of establishing the fault diagnosis algorithm model is shown in fig. 7. Firstly, equipment with good operation condition and three pieces of equipment with motor fault, speed reducer fault and bearing fault are prepared, the sensors are used for collecting operation state data of the same position and the same type respectively, and then the system of the embodiment of the invention is used for transmitting and storing the data into an HIVE data warehouse on a cloud platform. Then, SQL sentences need to be written in an HIVE warehouse to extract features, wherein the features required to be extracted for motor faults and reducer faults are shown in figure 8, and vibration data are directly used for building a model for bearing faults. The method comprises the steps that data collected from each type of equipment are correspondingly input into a motor fault learning system, a speed reducer fault learning system and a bearing fault learning system respectively, specifically, a support vector machine classifier and a GRU model are adopted as algorithms, the support vector machine classifier is adopted for diagnosing motor faults and speed reducer faults, the GRU model is adopted for bearing faults, finally, the motor fault algorithm model, the speed reducer fault algorithm model and the bearing fault algorithm model are obtained through the learning system respectively, and the three models jointly form a final judging system. When new real-time data exists, the system selects data in a time window with the same duration as the extracted features, wherein the time window is 3 seconds, then the features are extracted and input into the judgment system, and finally the judgment system outputs the diagnosis result to a Redis database.
As shown in fig. 5, four types of data, namely, sensor data, status data, historical statistical data, and diagnostic result data uploaded in real time, need to be displayed in a single page Web application. The display method of the sensor data is characterized in that a JS script in a single-page Web application directly subscribes the sensor data by using an MQTT WebSocket technology, and the subscribed data is directly displayed in a page. The current and the temperature are displayed by using a line graph, the rotating speed is displayed by using an instrument panel, the swing amplitude is displayed by using a bar graph, and the vibration data are subjected to FFT to obtain main frequency data and then displayed by using a line graph, as shown in FIG. 9. The display method of the state data, the historical data and the diagnosis result data is to establish an Http Server on the cloud service and respond to the request of the Web application. The single-page Web application requests data from the Http Server in a mode of requesting an REST interface once per second, the Http Server receives the request and then sends the request to a corresponding database to obtain the data, and then the result is returned to the Web application, and the Web application displays the result in a proper representation form. The state data and the diagnosis result data need to be obtained from a Redis database, and the historical data need to be obtained from a MySql database.
In summary, rapid development of big data and cloud computing technology provides new opportunities for deep research and application of remote monitoring and intelligent fault diagnosis of industrial equipment, and the embodiment of the invention effectively solves the problems of low maintenance efficiency, high maintenance cost, low diagnosis accuracy and the like when equipment state monitoring and fault diagnosis are carried out by using a traditional maintenance planning mode. The method comprises the steps of firstly, acquiring data of each important part on the industrial equipment by using various sensors, then reading the data and analyzing the data through a local gateway, locally storing the analyzed data and pushing the analyzed data to a cloud platform, finally, carrying out big data distributed data storage and modeling analysis on the acquired data on the cloud platform, further carrying out fault diagnosis on the equipment, simultaneously presenting the acquired real-time data and data analysis results to the cloud platform, and carrying out alarm processing if the equipment fails. The embodiment of the invention realizes the functions of remotely monitoring the operation condition of the factory equipment in real time, judging whether the equipment has a fault and giving the fault type, improves the efficiency of the factory to manage the equipment and reduces the maintenance cost of enterprises to the equipment.
According to the method for detecting the operation condition of the industrial equipment, which is provided by the embodiment of the invention, the collected multidimensional data such as current, temperature, vibration and the like on the equipment are subjected to distributed storage and operation. The system method constructs an off-line data processing system and a real-time streaming data processing system based on the mass data. On one hand, the running state of the equipment can be monitored in a remote real-time manner by utilizing a message publishing and subscribing mechanism; on the other hand, the frequency and the influence of the machine learning self-training are analyzed by combining the characteristics of the machine learning self-training, the fault occurrence type and the corresponding characteristics are summarized, a set of equipment fault diagnosis algorithm model is finally constructed, and then the health condition of the mechanical equipment is analyzed by relying on a big data analysis platform, so that the fault diagnosis effect is achieved. The diagnosis method can make up for the defects of the experience of technicians, assist the technicians in overhauling equipment, further improve the maintenance efficiency and the accuracy of fault detection, facilitate overall planning of the technicians, provide scientific guidance for enterprise production and reduce the enterprise cost.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.

Claims (4)

1. A system for detecting an operating condition of an industrial device, comprising:
the system comprises an acquisition module, a local gateway and a communication module, wherein the acquisition module is used for acquiring state data of key parts on industrial equipment and transmitting the state data to the local gateway through a communication link, the key parts at least comprise a speed reducer, a motor, an upper bearing and a lower bearing, the key parts also comprise one or more parts of an electric box, an upright post, a rotating chassis and a programmable logic controller, the state data at least comprise current data, rotating speed data, swing amplitude data and temperature data, the state data also comprise one or more of vibration data and programmable logic controller data, and whether the equipment is in a working state currently or not is acquired according to the programmable logic controller data; the acquisition module comprises: a communication link; the sensor unit comprises a current Hall ring, a three-axis acceleration vibration sensor, a rotating speed sensor, a displacement sensor and/or a temperature sensor, wherein the current Hall ring is arranged in the electric box, the three-axis acceleration vibration sensor is arranged on the upright post, the rotating speed sensor is arranged on the rotating chassis, the displacement sensor is arranged on the speed reducer, and a plurality of temperature sensors are respectively arranged on the motor, the speed reducer, the upper bearing and the lower bearing; the control unit is used for converting the analog signals acquired by the sensor into digital signals and storing the digital signals into a register;
the local gateway module is used for analyzing the state data through the local gateway so as to obtain physical data according to binary data streams, storing the physical data to the local and simultaneously pushing the physical data to a cloud server;
the cloud platform module is used for storing the physical data to a preset data storage unit through the cloud server, displaying the physical data on a single-page Web application in a preset chart and/or characters, detecting the current operation state of the industrial equipment according to the physical data, and further identifying the fault type of the industrial equipment when the current operation state is a fault state, wherein the fault type comprises one or more of motor faults, reducer faults or bearing faults; the cloud platform module includes: the data analysis system comprises a data storage unit, a data analysis unit and a data presentation unit, wherein the data storage unit comprises a Hive data warehouse tool; the data analysis unit comprises a data statistical analysis script and a fault diagnosis algorithm model which are executed periodically, the data statistical analysis script is operated at regular time by using the Hive data warehouse tool so as to carry out annual, quarterly and monthly statistics on historical data of various indexes, and meanwhile, the trained fault diagnosis algorithm model is used for predicting the data in a time window in real time to give a diagnosis result;
the establishing of the fault diagnosis algorithm model specifically comprises the following steps: the method comprises the following steps of respectively acquiring running state data of the same position and the same type of equipment with good running condition and three equipment with motor fault, speed reducer fault and bearing fault by using a sensor, transmitting and storing the acquired running state data into a Hive data warehouse on a cloud platform, compiling SQL sentences in the Hive data warehouse to extract features, wherein the features required to be extracted by the motor fault and the speed reducer fault comprise: the temperature of the motor, the speed reducer, the upper bearing and the lower bearing, the mean value and the variance of each current in a time window, and the mean value of the rotating speed and the swing amplitude in the time window are obtained, and the bearing faults are directly modeled by using vibration data; respectively and correspondingly inputting data acquired on each type of equipment into a motor fault learning system, a speed reducer fault learning system and a bearing fault learning system to obtain a motor fault algorithm model, a speed reducer fault algorithm model and a bearing fault algorithm model, adopting a support vector machine classifier for diagnosing motor faults and speed reducer faults, adopting a GRU model for bearing faults, and forming a discrimination system by using the motor fault algorithm model, the speed reducer fault algorithm model and the bearing fault algorithm model; and for the collected real-time data, selecting data in a time window with the same duration as the extracted features, and inputting the data into the judgment system to obtain a diagnosis result.
2. The system for detecting the operational condition of an industrial device according to claim 1, wherein the local gateway module comprises:
the data reading unit is used for reading the state data according to a preset frequency;
the data analysis unit is used for analyzing the state data to obtain the physical data;
a local storage unit for storing the physical data;
and the remote pushing unit is used for pushing the physical data.
3. A method for detecting the operation condition of industrial equipment is characterized by comprising the following steps:
acquiring state data of key parts on industrial equipment, and transmitting the state data to a local gateway through a communication link, wherein the key parts at least comprise a speed reducer, a motor, an upper bearing and a lower bearing, the key parts also comprise one or more parts in an electric box, an upright post, a rotating chassis and a programmable logic controller, the state data at least comprise current data, rotating speed data, swing amplitude data and temperature data, the state data also comprise one or more of vibration data and programmable logic controller data, and whether the equipment is in a working state currently is acquired according to the programmable logic controller data;
analyzing the state data through the local gateway to obtain physical data according to a binary data stream, storing the physical data to the local, and simultaneously pushing the physical data to a cloud server;
storing the physical data to a preset data storage unit through the cloud server, displaying the physical data on a single-page Web application in a preset chart and/or characters, detecting the current operation state of the industrial equipment according to the physical data, and further identifying the fault type of the industrial equipment when the current operation state is a fault state, wherein the fault type comprises one or more of motor faults, reducer faults or bearing faults; the data storage unit comprises a Hive data warehouse tool, the Hive data warehouse tool is used for regularly running a data statistical analysis script to carry out annual, quarterly and monthly statistics on various indexes of historical data, and a trained fault diagnosis algorithm model is used for predicting data in a time window in real time to give a diagnosis result;
the establishing of the fault diagnosis algorithm model specifically comprises the following steps: the method comprises the following steps of respectively acquiring running state data of the same position and the same type of equipment with good running condition and three equipment with motor fault, speed reducer fault and bearing fault by using a sensor, transmitting and storing the acquired running state data into a Hive data warehouse on a cloud platform, compiling SQL sentences in the Hive data warehouse to extract features, wherein the features required to be extracted by the motor fault and the speed reducer fault comprise: the temperature of the motor, the speed reducer, the upper bearing and the lower bearing, the mean value and the variance of each current in a time window, and the mean value of the rotating speed and the swing amplitude in the time window are obtained, and the bearing faults are directly modeled by using vibration data; respectively and correspondingly inputting data acquired on each type of equipment into a motor fault learning system, a speed reducer fault learning system and a bearing fault learning system to obtain a motor fault algorithm model, a speed reducer fault algorithm model and a bearing fault algorithm model, adopting a support vector machine classifier for diagnosing motor faults and speed reducer faults, adopting a GRU model for bearing faults, and forming a discrimination system by using the motor fault algorithm model, the speed reducer fault algorithm model and the bearing fault algorithm model; and for the collected real-time data, selecting data in a time window with the same duration as the extracted features, and inputting the data into the judgment system to obtain a diagnosis result.
4. The method for detecting the operating condition of the industrial equipment according to claim 3, further comprising:
generating an alarm signal according to the fault type;
and sending the alarm signal to a preset terminal through a mail and/or a short message.
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