Equipment monitoring system

Abstract

The invention discloses an equipment monitoring system which comprises an on-line monitoring system which comprises a sensor, a 5G module, a server, a data nursing module and an upper computer, equipment operation data are collected in real time through the sensor fixed to equipment and then stored in the server through the 5G module, and after data processing, a final result is displayed on the upper computer; the data acquisition module is used for acquiring data in a running state of the equipment in real time; and the data analysis module is used for analyzing whether the detection data fed back in the current equipment running state is within a safety threshold value or reaches a set alarm threshold value. According to the fault diagnosis system, on-site equipment operation data collected by a point inspection system and an online system is used, an equipment diagnosis model is established according to on-site equipment data, and the degradation degree of state-monitored production equipment is analyzed by using technical means based on mechanism diagnosis, a regression algorithm and the like, so that the fault diagnosis accuracy is improved. Fault reasons, fault hazards and maintenance suggestions of the unit are automatically given, and finally a diagnosis report is formed.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment monitoring, in particular to an equipment monitoring system. Background Art

[0002] When the unit is in operation, manual inspection is usually performed. However, due to the large number of units, it will be difficult to quickly find the abnormal unit. In addition, when the unit is abnormal, the unit is usually stopped and repaired, and the operating status of the equipment cannot be monitored in real time. For this reason, we propose an equipment monitoring system. Summary of the invention

[0003] In order to solve the above problems, the purpose of the present invention is to provide an equipment monitoring system to solve the existing problems.

[0004] To achieve the above object, the present invention provides the following technical solutions: An equipment monitoring system, comprising: The online monitoring system includes sensors, 5G modules, servers, data care modules, and host computers. Through sensors fixed on the equipment, the equipment operation data is collected in real time and stored in the server through the 5G module. After data processing, the final results are presented on the host computer. The data acquisition module is used to collect data in real time under the operation status of the equipment; The data analysis module is used to analyze whether the detection data fed back under the current equipment operation status is within the safety threshold or reaches the set alarm threshold; The alarm module is used to promptly issue an alarm for abnormal equipment status and to grade the equipment status. Different alarm statuses are displayed in different, eye-catching colors on the software interface. The measurement point processing module is used to collect and analyze the measurement point data to analyze and display the health of the equipment; The data storage module is used to set data storage intervals for different alarm levels such as normal, warning, warning, and danger. The measurement point data will be automatically stored according to the set storage interval time strategy at different alarm levels; The data push module is used to push data in different levels or at one time through WeChat or email according to different conditions, customized message content templates, and time. Preferably, the alarm module Gradual alarm: You can set different levels of alarm values ​​for normal, early warning, warning, danger, etc., and manage the alarm information in different levels; Absolute value alarm: The preset alarm threshold is sent to the data logger, and the system automatically gives the alarm level according to the set alarm threshold; Variable operating condition alarm: The system can set corresponding alarm curves for the variable operating condition equipment at different speeds or loads, and alarm and protect the unit according to the specific operating conditions to eliminate the impact of variable operating conditions on fault prediction; Delayed warning: For alarm information, you can set a delay time to avoid false alarms.

[0005] An equipment monitoring system further comprising: The automatic diagnosis module is used to automatically combine the collected equipment data, and automatically diagnose equipment faults according to the model through the set diagnosis time interval. It can diagnose a single vibration data or jointly diagnose multiple vibration data of the entire equipment. Preferably, the data storage module can record the monitoring data directly into the fault case library, and can manually enter typical fault cases.

[0006] An equipment monitoring system further comprising: The fault report generation module can automatically generate a digital diagnostic report that fully records all fault-related information.

[0007] Compared with the prior art, the present invention has the following beneficial effects: The fault diagnosis system of the present invention collects the field equipment operation data from the inspection system and the online system, and at the same time establishes an equipment diagnosis model based on the field equipment data. It uses technical means such as mechanism-based diagnosis and regression algorithm to analyze the degree of degradation of the production equipment under condition monitoring, automatically gives the cause of the unit's failure, the hazard of the failure and maintenance suggestions, and finally forms a diagnosis report. The fault diagnosis of the present invention is aimed at the monitored equipment unit, and the system can pre-set the characteristic parameters of the unit and the fault diagnosis rule base. Based on the fuzzy fault diagnosis principle, the system uses the currently measured fault signal, the fault diagnosis rule base (expert knowledge base), and the symptom fact base to perform reasoning and obtain possible fault sorting. The fault diagnosis rule base is user-editable, so that the diagnostic ability of the system is continuously enhanced. The diagnostic logic mode is input into the equipment database according to the equipment structure, bearing type, model, and speed. The present invention will realize functions such as panoramic information integration and display, data visualization chart analysis, etc. on a large unified display interface, and realize functions such as equipment key indicator analysis, vibration characteristic analysis, process parameter analysis, and equipment characteristic analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 It is a schematic diagram of the structure of the present invention; Figure 2 It is a schematic diagram of the vibration amplitude of the present invention; Figure 3It is a schematic diagram of the waveform spectrum of the present invention, including time domain analysis, frequency domain analysis, trend analysis, power spectrum, axis trajectory, Bode diagram, and Nyquist diagram; Figure 4 It is a schematic diagram of the waveform spectrum of the present invention, including envelope analysis, TFF, and cepstrum; Figure 5 It is a schematic diagram of the three-dimensional graph of the present invention, including three-dimensional time (waterfall graph) and three-dimensional speed (cascade graph); Figure 6 This is the plant area threshold warning report of the present invention; Figure 7 This is the device threshold warning report of the present invention; Figure 8 It is the trend warning of the measuring point of the present invention; Fig. 9 It is the AI ​​and mechanism early warning report of the present invention; Fig.10 This is the historical alarm report of the present invention; Fig.11 This is the information push setting page for the present invention. DETAILED DESCRIPTION

[0009] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0010] Implementation Case 1: An equipment monitoring system, comprising: The online monitoring system includes sensors, 5G modules, servers, data care modules, and host computers. Through sensors fixed on the equipment, the equipment operation data is collected in real time and stored in the server through the 5G module. After data processing, the final results are presented on the host computer. The data acquisition module is used to collect data in real time under the operation status of the equipment; The data analysis module is used to analyze whether the detection data fed back under the current equipment operation status is within the safety threshold or reaches the set alarm threshold; The alarm module is used to promptly issue an alarm for abnormal equipment status and to grade the equipment status. Different alarm statuses are displayed in different, eye-catching colors on the software interface. The measurement point processing module is used to collect and analyze the measurement point data to analyze and display the health of the equipment; The data storage module is used to set data storage intervals for different alarm levels such as normal, warning, warning, and danger. The measurement point data will be automatically stored according to the set storage interval time strategy at different alarm levels; The data push module is used to push data in different levels or at one time through WeChat or email according to different conditions, customized message content templates, and time. The automatic diagnosis module is used to automatically combine the collected equipment data, and automatically diagnose equipment faults according to the model through the set diagnosis time interval. It can diagnose a single vibration data or conduct joint diagnosis on multiple vibration data of the entire equipment. The data storage module can record monitoring data directly into the fault case library, and can manually enter typical fault cases; The fault report generation module can automatically generate a digital diagnostic report that fully records all fault-related information. The online monitoring system collects equipment operation data in real time through sensors fixed on key units, stores it on the server through the 5G private network, and after data processing, the final result is presented on the host computer (PC). The on-site DCS, PLC and other signals are connected through the OPC protocol, and the data is connected to the system, thereby realizing signal integration and unification. The online monitoring system has an automatic and intelligent alarm function, which can trigger the corresponding alarm limits according to different speed, power and other conditions. Different alarm states are marked in the system with different and eye-catching colors. After the alarm, the user can quickly view the operation status of the unit equipment on the status monitoring screen and promptly repair the faulty equipment. The online monitoring system has the function of collecting all signals, such as acceleration, velocity, displacement, rotation speed, current, voltage, temperature, noise, etc. Enterprise users can quickly grasp the operating status of the equipment by analyzing the time domain, frequency domain, trend, phase, envelope, three-dimensional time, three-dimensional rotation speed, axis trajectory, Bode diagram, Nyquist diagram, power spectrum, and power spectrum density of the measuring points, and ultimately achieve "early detection, early prevention, and early resolution" of equipment failures, thereby reducing the significant losses caused by abnormal equipment failures.

[0011] Specific implementation case 2: 1) System functions 11) Data collection and analysis function The system can conduct continuous online vibration monitoring of the unit and issue timely alarms for abnormal conditions through the alarm thresholds set in the system. The device status is graded and alarms are given. Different alarm statuses are displayed with different, eye-catching color labels on the software interface. Various waveform parameters: peak value, average value, effective value, peak index, waveform index, pulse index, margin index, kurtosis index Various frequency components: Automatically find the three characteristic frequencies with the largest vibration amplitude Spectra: vibration amplitude (peak value, average value, effective value, peak index, waveform index, pulse index, margin index, kurtosis index); waveform spectrum (time domain analysis, frequency domain analysis, trend analysis, envelope analysis, TFF, inverse spectrum, power spectrum, axis trajectory, Bode diagram, Nyquist diagram); three-dimensional spectrum (time three-dimensional, speed three-dimensional). 12) Alarm strategy Gradual alarm: You can set different levels of alarm values ​​for normal, early warning, warning, danger, etc., and manage the alarm information in different levels; Absolute value alarm: The preset alarm threshold is sent to the data logger, and the system automatically gives the alarm level according to the set alarm threshold; Variable operating condition alarm: The system can set corresponding alarm curves for the variable operating condition equipment at different speeds or loads, and alarm and protect the unit according to the specific operating conditions to eliminate the impact of variable operating conditions on fault prediction; Delayed warning: For alarm information, you can set a delay time to avoid false alarms. The early warning reports must include but are not limited to equipment threshold warning reports, trend warning reports, AI warning reports, mechanism alarm reports, etc. based on vibration data. Each alarm in the early warning report should support case marking, alarm suppression, alarm enhancement, alarm processing, trend analysis and other functions. Historical alarms and typical cases can be viewed in the alarm details. 13) Data processing Measuring point marking: When real-time data monitoring is performed on a device, the measuring points can be marked on the device image. Real-time data calibration: Real-time adjustment of measurement results can be achieved through custom expressions. Speed ​​binding: The speed can be bound to other measuring points through a custom expression; the speed can be automatically adjusted according to the set expression. Equipment start-stop management and health analysis: For a certain device, you can customize the measurement point expression to determine the device's operating status, including the start-stop monitoring of the device, online time, etc. By collecting and analyzing the measurement point data, you can analyze and display the health of the device. 14) Data push Support WeChat push Support email push Support push based on slot type, abnormal level, limit and other conditions Support custom message content templates Support push time interval setting, which can achieve only one push within the specified time Supports hierarchical push. 15) Data storage and management: Data storage intervals can be set separately for different alarm levels such as normal, warning, alert, and danger; the measuring point data will be automatically stored according to the set storage interval time strategy at different alarm levels. 16) Massive graph comparison and analysis Monitor vibrations of multiple units on the same interface: The monitoring interface simultaneously displays the amplitude, time domain waveform, frequency domain waveform, Bode diagram, trend diagram, etc. of multiple units and any measuring points. Display various analysis functions of a measuring point on the same interface: The monitoring interface simultaneously displays multiple analysis graphs of a measuring point, such as vibration value, time domain, frequency domain, trend, time 3D graph, speed 3D graph, Bode graph, etc. 17) Rapid graph linkage playback analysis When analyzing historical data, the cursor selects the data at a certain time point in the historical trend chart, and the spectrum of each measuring point at that time point is linked to display, and you can simultaneously view the spectrum functions such as time domain, frequency domain, power spectrum, power spectrum density, axis trajectory, speed 3D chart, time 3D chart, etc. This function supports spectrum analysis of multiple units and multiple measuring points. 18) Types of faults that can be monitored Rotating machinery deformation, wear, unevenness, fluid irregularity, thermal imbalance, surge, resonance, foreign matter entry and collision, rectifier appearance wear, deformation, turbulence, improper assembly or design defects; Unbalanced rotating machinery, rotor wear, rotor block loss, shaft imbalance, moving and stationary parts, and collision and wear; Parallel misalignment, angular misalignment, comprehensive misalignment, and thermal deformation misalignment of rotating machinery; Shaft bending, shaft thermal deformation, shaft cracks; Rolling bearing pitting, wear, peeling, detachment, poor lubrication and other faults; Determination of oil film vortex, oil film oscillation, clearance and oil pressure of sliding bearings; Gearbox and gear wear, broken teeth, etc. Loose parts and loose basic structures; Resonance of various components, spindles, foundations, etc.; Check the rotating machinery maintenance errors and results, predict potential failures, and determine the development trend of failures. 19) Bearing database The bearing library contains more than 70,000 bearings of different manufacturers and models, and the characteristic frequencies of all bearings can be accurately found. Supports calculation of bearing characteristic frequency and rapid location of bearing faults. 20) Automatic diagnosis function The system can automatically combine the collected equipment data (inspection data, online data, etc.) and automatically diagnose equipment faults based on the model at the set diagnosis interval, rather than relying solely on manual experience to analyze the collected characteristic values ​​for diagnosis. Diagnosis can be performed on a single vibration data or on multiple vibration data of the entire equipment. (21) Customized equipment failure modeling The system can open up the function of configuring equipment component parameters or creating a mechanism model of the equipment, rather than fixing a few equipment parameters or models. (22) Fault Case Library Supports direct recording of monitoring data into the fault case library; supports manual entry of typical fault cases. The system supports the diagnostic case library function, which can record the fault analysis process, conclusions, etc., and can view key graphs used in the analysis (such as waveforms, spectra, etc.). (23) Automatically generate digital fault reports It meets the specific requirements of "process integrity, digital assetization, and operability". It can automatically generate digital diagnostic reports, fully recording all fault-related information, including but not limited to: key monitoring values, waveforms, spectrum graphs and other related professional graphs; digital diagnostic reports can be downloaded and viewed through tools such as Microsoft Office and WPS.

[0012] in conclusion: 1) Integrate various types and sources of big data for large unit status monitoring. Use mining technology to quickly obtain valuable information related to the operating status of large units from various types of massive data. 2) Use big data to perform order analysis to find fault characteristic frequencies and establish an alarm fault directory tree. By solving the deep mining of massive waveform, spectrum, correlation analysis and other data, an alarm fault directory tree model is established to help companies fully automate 90% of diagnostic data analysis work and use data-driven decision-making. 3) Use big data to set predictive alarm curves and danger alarm curves. Safety assurance system multi-level vibration alarm settings, frequency band alarms, establish a multi-layer protection system, and increase the buffering capacity of the online protection system. 4) Use data mining software to conduct massive diagnostic analysis and comparison. The massive monitoring interface simultaneously displays effective value, time domain, phase, frequency domain, crest factor, trend, three-dimensional graph, and Bode graph; it displays the time domain, frequency domain, amplitude, energy spectrum, and trend of multiple frequencies or frequency bands at a measuring point, and can instantly find out the vibration conditions of each frequency or frequency band.

[0013] The fault diagnosis system provides complete equipment status information, automatically controls and alarms the equipment status, and accurately judges equipment failures through its auxiliary diagnosis function and expert diagnosis, so that equipment management personnel can accurately grasp the status of key units in real time, bringing the following benefits to enterprises: 1) Realize integrated management and control Digital management and intelligent control to prevent unexpected accidents are important components of equipment status monitoring and control systems that have become an indispensable part of digital factories and promote the improvement of enterprise management level. 2) Realize predictive maintenance of equipment The integration of monitoring and diagnosis theory, objective data and on-site experience can greatly improve the accuracy of fault diagnosis, so that you can have a clear idea before maintenance, be handy during maintenance, and be safe and worry-free after maintenance, thus ensuring safe production. 3) Promote staff reduction and efficiency improvement Optimize maintenance plans, significantly reduce unreasonable maintenance and repairs, improve maintenance efficiency, and improve the company's automation level to create the best conditions for reducing staff and increasing efficiency. 4) Improve KPI indicators and increase corporate profits Improve return on net assets (RONA): reduce inventory costs; reduce spare parts; extend maintenance cycles. Improve overall equipment effectiveness (OEE): extend equipment working time and improve production capacity; accumulate operation data and respond to abnormal changes in advance (machine failure accounts for more than 50% of factory accidents); reduce unplanned shutdowns and optimize shutdown plans; reduce maintenance time; improve safety and ensure quality. 5)Control the device status anytime and anywhere The system adopts B\S web page architecture. Personnel with management or browsing permissions can learn about the real-time operating status of the equipment through the Internet anytime and anywhere, making the work of management and equipment managers easier. It is of great benefit to the management and maintenance of important equipment in the industrial sector, improving the production capacity of enterprises, ensuring safe production, and improving product quality. It is of great significance in the development of various sectors of the national economy.

[0014] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A device monitoring system, characterized in that: include: The online monitoring system includes sensors, 5G modules, servers, data care modules, and host computers. Through sensors fixed on the equipment, the equipment operation data is collected in real time and stored in the server through the 5G module. After data processing, the final results are presented on the host computer. The data acquisition module is used to collect data in real time under the operation status of the equipment; The data analysis module is used to analyze whether the detection data fed back under the current equipment operation status is within the safety threshold or reaches the set alarm threshold; The alarm module is used to promptly issue an alarm for abnormal equipment status and to grade the equipment status. Different alarm statuses are displayed in different, eye-catching colors on the software interface. The measurement point processing module is used to collect and analyze the measurement point data to analyze and display the health of the equipment; The data storage module is used to set data storage intervals for different alarm levels such as normal, warning, warning, and danger. The measurement point data will be automatically stored according to the set storage interval time strategy at different alarm levels; The data push module is used to push data in different levels or at one time through WeChat or email according to different conditions, customized message content templates, and time.

2. The equipment monitoring system according to claim 1, characterized in that: The alarm module Gradual alarm: You can set different levels of alarm values ​​for normal, early warning, warning, danger, etc., and manage the alarm information in different levels; Absolute value alarm: The preset alarm threshold is sent to the data logger, and the system automatically gives the alarm level according to the set alarm threshold; Variable operating condition alarm: The system can set corresponding alarm curves for the variable operating condition equipment at different speeds or loads, and alarm and protect the unit according to the specific operating conditions to eliminate the impact of variable operating conditions on fault prediction; Delayed warning: For alarm information, you can set a delay time to avoid false alarms.

3. The equipment monitoring system according to claim 1, characterized in that: Also includes: The automatic diagnosis module is used to automatically combine the collected equipment data, and automatically diagnose equipment faults according to the model through the set diagnosis time interval. It can diagnose a single vibration data or jointly diagnose multiple vibration data of the entire equipment.

4. The equipment monitoring system according to claim 1, characterized in that: The data storage module can record the monitoring data directly into the fault case library, and can manually enter typical fault cases.

5. The equipment monitoring system according to claim 1, characterized in that: Also includes: The fault report generation module can automatically generate a digital diagnostic report that fully records all fault-related information.

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