Field equipment state monitoring system suitable for industrial internet

Abstract

The invention discloses a field equipment state monitoring system suitable for an industrial internet in the technical field of monitoring. The field equipment state monitoring system comprises a data acquisition module, a data processing module, a state monitoring and fault diagnosis module, a data analysis and early warning module and a user interaction interface, the state monitoring and fault diagnosis module is used for monitoring the state of the equipment in real time based on the preprocessed data, performing fault diagnosis, performing deep analysis on long-term data and predicting the fault trend of the equipment; the state monitoring and fault diagnosis module is preset with a vibration characteristic database, and the vibration characteristic database comprises vibration characteristics of various devices in a normal state and a fault state. And the state monitoring and fault diagnosis module compares the characteristic parameters obtained by the mechanical state parameter unit with the known vibration mode fault characteristics in the vibration characteristic database. The method is scientific and reasonable, and realizes real-time monitoring of various devices in the automobile intelligent production workshop by integrating a high-precision sensor and a data processing algorithm.

Description

Technical Field

[0001] The present invention belongs to the field of monitoring technology, and specifically is a field equipment status monitoring system suitable for industrial Internet. Background Art

[0002] The Industrial Internet is a new type of infrastructure, application model and industrial ecology that deeply integrates the new generation of information and communication technology with the industrial economy. It builds a new type of network infrastructure that fully connects people, machines and things by systematically constructing three functional systems: network, platform and security, and forms emerging formats and application models for intelligent development. As the core of the Industrial Internet, the Industrial Internet platform is aimed at the digitalization, networking and intelligent needs of the manufacturing industry, and builds a service system based on massive data collection, aggregation and analysis to support the ubiquitous connection, elastic supply and efficient configuration of manufacturing resources. Industrial cloud platform.

[0003] In terms of intelligent automobile production, the application of industrial Internet is particularly critical. Automobile manufacturing is a highly complex and sophisticated process involving a large number of precision equipment and complex production lines. The operating status of the equipment directly affects production efficiency, product quality and overall production cost control. Therefore, real-time monitoring of on-site equipment and timely detection and handling of faults are of great significance to ensure the stable operation of automobile production lines and product quality. By monitoring key parameters such as temperature, humidity, pressure, flow, vibration, etc. through monitoring devices and systems, the operating status of the equipment can be grasped in real time to prevent the occurrence of potential faults. Especially in intelligent automobile production, many links rely on highly automated equipment and robots, and any equipment failure may lead to stagnation of the production line or decline in product quality. Therefore, the application of real-time monitoring and early warning systems is particularly important. However, the failure of automobile production equipment is often diverse and complex. Different equipment and different fault types may show different symptoms. Therefore, how to accurately identify the fault type, locate the fault location, and give effective maintenance suggestions are key issues that need to be solved by the on-site equipment status monitoring system. Especially in intelligent automobile production, due to the high integration and automation of the production line, the identification and diagnosis of faults need to be faster and more accurate to reduce production interruptions and losses.

[0004] In summary, how to accurately identify the fault type has become an urgent problem that technicians in this field need to solve. Therefore, it is necessary to propose a field equipment status monitoring system suitable for the industrial Internet. Summary of the invention

[0005] In order to solve the above problems, the purpose of the present invention is to provide a field equipment status monitoring system suitable for the industrial Internet. By integrating high-precision sensors, advanced data processing algorithms, intelligent fault diagnosis technology and big data analysis capabilities, real-time monitoring, fault warning and preventive maintenance of various equipment in automobile intelligent production workshops can be realized, thereby improving production efficiency, reducing maintenance costs and ensuring product quality.

[0006] In order to achieve the above-mentioned purpose, the technical solution of the present invention is as follows: a field equipment status monitoring system suitable for industrial Internet, including a data acquisition module, a data processing module, a status monitoring and fault diagnosis module, a data analysis and early warning module and a user interaction interface;

[0007] The data acquisition module includes a basic operating parameter unit for collecting equipment temperature, humidity, and pressure; a mechanical state parameter unit for collecting equipment vibration, speed, amplitude, and flow characteristic parameters; and an electrical parameter unit for collecting voltage, current, phase, oil level, gas concentration, equipment position, and status.

[0008] The data acquisition module is used to receive and store data of the basic operation parameter unit, the mechanical state parameter unit and the electrical parameter unit.

[0009] The data processing module is used to pre-process the received data.

[0010] The condition monitoring and fault diagnosis module is used to monitor the equipment status in real time and perform fault diagnosis based on preprocessed data, conduct in-depth analysis of long-term data and predict equipment failure trends. The condition monitoring and fault diagnosis module is preset with a vibration feature database, which contains the vibration characteristics of various equipment in normal and faulty states. The condition monitoring and fault diagnosis module uses the characteristic parameters obtained by the mechanical state parameter unit to compare with the known vibration mode fault characteristics in the vibration feature database to determine whether the current vibration state of the equipment is abnormal. When judging the vibration and amplitude, it combines the basic operating parameter unit and the electrical parameter unit for comprehensive analysis.

[0011] The data analysis and early warning module monitors the status parameters of equipment in the automobile intelligent production workshop in real time, discovers real-time operating characteristics, and warns and predicts future operating trends.

[0012] Furthermore, the mechanical state parameter unit also includes a sound-receiving and judging vibration sensor for assisting in collecting and verifying vibration and amplitude. The outer wall of the sound-receiving and judging vibration sensor is fixedly connected to a connecting layer, and the sound-receiving and judging vibration sensor is fixedly connected to a preset position of the monitored device through the connecting layer. A rolling groove is opened in the sound-receiving and judging vibration sensor, and a number of vibration collision balls are slidably fitted in the rolling groove. A number of sound-receiving microphones are fixedly connected to the outer wall of the sound-receiving and judging vibration sensor and the inner wall of the rolling groove.

[0013] Furthermore, the data processing module preprocesses the collected raw data, and the preprocessing includes data cleaning, format conversion and outlier processing. It filters and reduces the noise of the sound signals collected by the collision between the vibration collision ball and the rolling groove in the sound-receiving vibration sensor, and the collision between adjacent vibration collision balls, and extracts sound features for judging vibration and amplitude, and converts the sound signals into digital signals.

[0014] Furthermore, the status monitoring and fault diagnosis module monitors the operating status of the equipment in real time based on the data processed by the data processing module, and performs fault diagnosis by setting threshold monitoring, trend analysis and spectrum analysis methods.

[0015] Furthermore, the data analysis and early warning module uses big data and machine learning technology to conduct in-depth analysis of equipment operation data and establish an equipment failure prediction model. The equipment failure prediction model is based on the historical data of equipment operation, fault records and sound feature parameters, combined with convolutional neural networks for training and optimization.

[0016] Furthermore, the user interaction interface is used to display the real-time status, historical data, analysis results and warning information of the equipment, and supports data query and report generation functions.

[0017] Furthermore, it also includes a remote monitoring module, which is used for users to remotely access the system through the Internet to understand the operating status of the equipment.

[0018] Furthermore, it also includes a preventive maintenance module, which is used to detect early signs of equipment failure through real-time monitoring and data analysis, and send preventive measures to users.

[0019] Furthermore, it also includes integration modules for adding new sensor types and data analysis algorithms as needed.

[0020] Furthermore, the data analysis and early warning module has built-in data encryption and privacy protection mechanisms, and supports online update and upgrade functions.

[0021] The following principles and beneficial effects are achieved by adopting the above scheme:

[0022] Basic principle: By integrating high-precision sensors, advanced data processing algorithms, intelligent fault diagnosis technology and big data analysis capabilities, a comprehensive on-site equipment status monitoring system is constructed. The system can monitor the status parameters of various equipment in the intelligent automobile production workshop in real time, including temperature, humidity, pressure, flow, vibration, speed, amplitude, voltage, current, phase, oil level, gas concentration, etc., as well as the location and status information of the equipment. These data are collected and transmitted to the system through sensors. After preprocessing by the data processing module, they are monitored and diagnosed in real time by the status monitoring and fault diagnosis module.

[0023] Beneficial effects:

[0024] 1. By integrating high-precision sensors and advanced fault diagnosis technology, the system can accurately identify the fault type, locate the fault location, improve the accuracy of fault diagnosis, and reduce false alarms and missed alarms. This helps to reduce production losses caused by downtime and improve the overall efficiency of the production line. By comparing known fault features in the vibration feature database, the system can accurately diagnose equipment failures and predict future equipment failure trends through big data analysis, providing strong support for preventive maintenance.

[0025] 2. The system of the present invention can monitor the operating status of the equipment in real time and provide early warning information through data analysis and early warning modules, so that users can promptly discover early signs of equipment failure, take preventive measures to avoid failures, and ensure the stable operation of the production line. This helps to reduce the impact of equipment failures on production and improve the reliability and stability of the production line.

[0026] 3. The present invention uses big data and machine learning technology to conduct in-depth analysis of equipment operation data and establish an equipment failure prediction model, which can predict the future operation trend and potential failure points of the equipment. This helps users to formulate maintenance plans in advance, arrange maintenance resources reasonably, and reduce maintenance costs.

[0027] 4. The user interaction interface of the present invention provides a friendly user interface, supports data query and report generation functions, and enables users to easily understand the real-time status and historical data of the equipment. This helps users better understand the operating status of the equipment and provides strong support for the maintenance and management of the equipment.

[0028] 5. The present invention also includes a remote monitoring module, and users can access the system remotely through the Internet to understand the operating status of the device. This helps users to understand the operating status of the device anytime and anywhere and improve management efficiency.

[0029] 6. The preventive maintenance module of the present invention can detect early signs of equipment failure in time through real-time monitoring and data analysis, and send preventive measures to users. This helps users take measures in advance to avoid failures and extend the service life of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a flow chart of an embodiment of the present invention.

[0031] Figure 2 It is a side cross-sectional view of a sound pickup determination vibration sensor according to an embodiment of the present invention.

[0032] The figure marks in the drawings of the specification include: 1. connection layer; 2. sound reception judgment vibration sensor; 3. sound reception microphone; 4. rolling groove; 5. vibration collision ball. DETAILED DESCRIPTION

[0033] The following is further described in detail through specific implementation methods:

[0034] Example 1

[0035] Basically as attached Figure 1 As shown: A field equipment status monitoring system suitable for the industrial Internet, the system includes a data acquisition module, a data processing module, a status monitoring and fault diagnosis module, and a data analysis and early warning module. The data acquisition module includes a basic operation parameter unit for collecting equipment temperature, humidity, and pressure; a mechanical state parameter unit for collecting equipment vibration, speed, amplitude, and flow; and an electrical parameter unit for collecting voltage, current, phase, oil level, gas concentration, equipment position, and status.

[0036] Temperature, humidity, pressure, and flow in the basic operating parameter unit: By installing corresponding temperature sensors, humidity sensors, pressure sensors, and flow sensors, the analog signals are converted into digital signals for collection.

[0037] Vibration, rotation speed, and amplitude in the mechanical state parameter unit: Use vibration sensors, acceleration sensors, rotation speed sensors, etc. to monitor the vibration and rotation status of the equipment in real time.

[0038] Voltage, current, and phase in the electrical parameter unit: obtain the electrical parameters of the equipment in real time through electrical measuring instruments.

[0039] Oil level: Use an oil level sensor to monitor changes in the lubricating oil level.

[0040] Gas concentration: Install gas detectors to monitor the concentration of toxic or flammable gases in the workshop in real time.

[0041] Equipment location and status: Get the real-time location and status information of the equipment through GPS module or RFID technology.

[0042] The mechanical state parameter unit also includes a sound-receiving and judging vibration sensor 2 for assisting in collecting and verifying vibration and amplitude. The outer wall of the sound-receiving and judging vibration sensor 2 is fixedly connected to a connecting layer 1 by screws. The sound-receiving and judging vibration sensor 2 is bonded to a preset position of the monitored device through the connecting layer 1. A rolling groove 4 is opened in the sound-receiving and judging vibration sensor 2. A number of vibration collision balls 5 are slidably fitted in the rolling groove 4. The outer wall of the sound-receiving and judging vibration sensor 2 and the inner wall of the rolling groove 4 are fixedly connected to a number of sound-receiving microphones 3 by screws.

[0043] The data acquisition module is used to receive and store various sensor data. The data processing module is used to pre-process the data received from the data acquisition module. The data processing module pre-processes the collected feedback data, including data cleaning, format conversion and abnormal value processing, and filters and de-noises the sound signals collected by the sound-receiving judgment vibration sensor 2, the collision between the vibration collision ball 5 and the rolling groove 4, and the collision between adjacent vibration collision balls 5, and extracts sound features for judging vibration and amplitude, and converts the sound signals into digital signals.

[0044] The status monitoring and fault diagnosis module is used to monitor the equipment status in real time and perform fault diagnosis based on the preprocessed data. The status monitoring and fault diagnosis module monitors the operating status of the equipment in real time based on the data processed by the data processing module, and performs fault diagnosis by setting threshold monitoring, trend analysis and spectrum analysis methods.

[0045] The condition monitoring and fault diagnosis module is preset with a vibration feature database, which contains the vibration features of various devices in normal and faulty states. The condition monitoring and fault diagnosis module compares the feature parameters obtained by the mechanical condition parameter unit with the known vibration mode fault features in the vibration feature database to determine whether the current vibration state of the equipment is abnormal. When judging the vibration and amplitude, it combines the basic operation parameter unit and the electrical parameter unit for comprehensive analysis.

[0046] The data analysis and early warning module is used to conduct in-depth analysis of long-term data and predict equipment failure trends. The data analysis and early warning module uses big data and machine learning technology to conduct in-depth analysis of equipment operation data and establish an equipment failure prediction model. The equipment failure prediction model is based on historical data, fault records and sound feature parameters of equipment operation, combined with convolutional neural networks for training and optimization. The data analysis and early warning module monitors the status parameters of equipment in the automotive intelligent production workshop in real time, discovers real-time operation characteristics, and warns and predicts future operation trends. The data analysis and early warning module has built-in data encryption and privacy protection mechanisms and supports online update and upgrade functions.

[0047] The preventive maintenance module is used to detect early signs of equipment failure through real-time monitoring and data analysis, and send preventive measures to users.

[0048] Integration modules for adding new sensor types and data analysis algorithms as needed.

[0049] The remote monitoring module is used for users to remotely access the system through the Internet to understand the operating status of the equipment.

[0050] The user interaction interface is used to display the real-time status, historical data, analysis results and warning information of the equipment, and supports data query and report generation functions.

[0051] Specific implementation steps:

[0052] Deploy corresponding sensors on key equipment in the automotive intelligent production workshop, such as stamping machines, welding machines, coating equipment and assembly lines, including temperature sensors, humidity sensors, pressure sensors, flow sensors, vibration sensors, acceleration sensors, speed sensors, oil level sensors, gas detectors, sound-receiving vibration sensors 2, and GPS modules or RFID technology. Ensure that all sensors are properly connected to the system signal, the system is initialized, and start data collection.

[0053] The data acquisition module receives data from various sensors in real time, including temperature, humidity, pressure, flow, vibration, speed, amplitude, voltage, current, phase, oil level, gas concentration, equipment position and status parameters, etc. The data processing module pre-processes the received raw data, which includes data cleaning, removing invalid or erroneous data; format conversion, converting data into a unified format; and outlier processing, identifying and processing abnormal data points.

[0054] For the sound signals collected by the collision between the vibration collision ball 5 and the rolling groove 4 in the sound-receiving vibration sensor 2, and the collision between adjacent vibration collision balls 5, filtering and noise reduction processing are first performed to eliminate the interference of environmental noise and other non-target vibration sources. Then, the key features in the sound signal are extracted, which may include the frequency, amplitude, duration of the sound, and the waveform of the sound signal. Next, the state monitoring and fault diagnosis module uses these characteristic parameters to compare with the known vibration modes or fault features in the vibration feature database. By matching the extracted sound feature parameters with the features in the vibration feature database, it can be determined whether the current vibration state of the device is abnormal. When judging vibration and amplitude, other sensor data such as acceleration sensors, speed sensors, etc. can also be combined for comprehensive analysis. Through the fusion of multi-sensor data, the accuracy and reliability of vibration and amplitude judgment can be further improved.

[0055] The condition monitoring and fault diagnosis module monitors the operating status of the equipment in real time based on the preprocessed data. By setting thresholds, the thresholds of parameters such as temperature, pressure and amplitude are monitored, and an alarm is triggered when the parameter exceeds the set threshold. The condition monitoring and fault diagnosis module performs trend analysis, observes the trend of parameter changes over time, and identifies potential faults. The vibration data is processed using the spectrum analysis method to identify the vibration frequency and amplitude, and determine whether the equipment has abnormal vibration. Based on the monitoring results and the fault diagnosis algorithm, it is determined whether the equipment has a fault.

[0056] The data analysis and early warning module uses big data and machine learning technology to conduct in-depth analysis of equipment operation data. Based on the historical data of equipment operation, fault records and sound feature parameters, combined with convolutional neural network training and optimization, an equipment failure prediction model is established. The model predicts the future failure trend of the equipment, and when the risk of failure is predicted, early warning information is sent to the user in advance.

[0057] Through the remote monitoring module, users can access the system through the Internet anytime and anywhere to understand the operating status of the equipment. The preventive maintenance module detects early signs of equipment failure based on real-time monitoring and data analysis results. The system sends preventive measures to users, such as adjusting equipment parameters, replacing worn parts, etc., to avoid failures. The user interaction interface provides a friendly user interface to display the real-time status, historical data, analysis results and early warning information of the equipment. Users can perform operations such as data query and report generation through the interface, which is convenient for managing and analyzing the operating status of the equipment.

[0058] Integration modules allow users to add new sensor types and data analysis algorithms as needed. When new equipment is added to the workshop or new parameters need to be monitored, the system can be easily expanded through integration modules.

[0059] The above is only an embodiment of the present invention. The common sense such as the known specific structure and characteristics in the scheme is not described in detail here. The ordinary technicians in the relevant field know all the common technical knowledge in the technical field of the invention before the application date or priority date, can know all the existing technologies in the field, and have the ability to apply the conventional experimental means before that date. The ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the relevant field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can be made, which should also be regarded as the protection scope of the present invention, which will not affect the effect of the implementation of the present invention and the practicality of the patent. The protection scope required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A field equipment status monitoring system suitable for industrial Internet, characterized by: It includes data acquisition module, data processing module, status monitoring and fault diagnosis module, data analysis and early warning module and user interaction interface; The data acquisition module includes a basic operating parameter unit for collecting equipment temperature, humidity, and pressure; Mechanical state parameter unit for collecting equipment vibration, speed, amplitude and flow characteristic parameters; electrical parameter unit for collecting voltage, current, phase, oil level, gas concentration, equipment position and status; A data acquisition module, used to receive and store data of basic operation parameter units, mechanical state parameter units and electrical parameter units; A data processing module, used for preprocessing the received data; The state monitoring and fault diagnosis module is used to monitor the equipment state in real time and perform fault diagnosis based on preprocessed data, conduct in-depth analysis of long-term data and predict equipment failure trends. The state monitoring and fault diagnosis module is preset with a vibration feature database, which contains the vibration features of various equipment in normal and faulty states. The state monitoring and fault diagnosis module compares the feature parameters obtained by the mechanical state parameter unit with the known vibration mode fault features in the vibration feature database to determine whether the current vibration state of the equipment is abnormal. When judging the vibration and amplitude, it combines the basic operation parameter unit and the electrical parameter unit for comprehensive analysis; The data analysis and early warning module monitors the status parameters of equipment in the automobile intelligent production workshop in real time, discovers real-time operating characteristics, and warns and predicts future operating trends.

2. The field equipment status monitoring system applicable to the industrial Internet according to claim 1 is characterized in that: The mechanical state parameter unit also includes a sound-receiving and judging vibration sensor (2) for assisting in collecting and verifying vibration and amplitude. The outer wall of the sound-receiving and judging vibration sensor (2) is fixedly connected with a connection layer (1). The sound-receiving and judging vibration sensor (2) is fixedly connected to a preset position of the monitored device through the connection layer (1). A rolling groove (4) is provided inside the sound-receiving and judging vibration sensor (2). A plurality of vibration collision balls (5) are slidably fitted inside the rolling groove (4). A plurality of sound-receiving microphones (3) are fixedly connected to the outer wall of the sound-receiving and judging vibration sensor (2) and the inner wall of the rolling groove (4).

3. The field equipment status monitoring system applicable to the industrial Internet according to claim 2 is characterized in that: The data processing module performs preprocessing on the collected raw data, the preprocessing including data cleaning, format conversion and abnormal value processing, filtering and noise reduction processing on the sound signals collected from the collision between the vibration collision ball (5) and the rolling groove (4) in the sound receiving judgment vibration sensor (2), and the collision between adjacent vibration collision balls (5), and extracts sound features for judging vibration and amplitude, and converts the sound signals into digital signals.

4. The field equipment status monitoring system applicable to the industrial Internet according to claim 3 is characterized in that: The status monitoring and fault diagnosis module monitors the operating status of the equipment in real time based on the data processed by the data processing module, and performs fault diagnosis by setting threshold monitoring, trend analysis and spectrum analysis methods.

5. The field equipment status monitoring system applicable to the industrial Internet according to claim 4 is characterized in that: The data analysis and early warning module uses big data and machine learning technology to conduct in-depth analysis of equipment operation data and establish an equipment failure prediction model. The equipment failure prediction model is based on historical equipment operation data, fault records and sound feature parameters, combined with convolutional neural networks for training and optimization.

6. The field equipment status monitoring system applicable to the industrial Internet according to claim 5 is characterized in that: The user interaction interface is used to display the real-time status, historical data, analysis results and warning information of the equipment, and supports data query and report generation functions.

7. The field equipment status monitoring system applicable to the industrial Internet according to claim 6 is characterized in that: It also includes a remote monitoring module, which is used by users to remotely access the system through the Internet to understand the operating status of the equipment.

8. The field equipment status monitoring system applicable to the industrial Internet according to claim 7 is characterized in that: It also includes a preventive maintenance module, which is used to detect early signs of equipment failure through real-time monitoring and data analysis, and send preventive measures to users.

9. The field equipment status monitoring system applicable to the industrial Internet according to claim 8 is characterized in that: Also included are integration modules for adding new sensor types and data analysis algorithms as needed.

10. The field equipment status monitoring system applicable to the industrial Internet according to claim 9 is characterized in that: The data analysis and early warning module has built-in data encryption and privacy protection mechanisms, and supports online update and upgrade functions.

Images