An online monitoring system for washing equipment based on big data

Abstract

The present invention relates to an online monitoring system for washing and sorting equipment based on big data, comprising: a monitoring module, used for detecting the operation data of the washing and sorting equipment, and sending the measured data to an analysis module; an analysis module, which is connected to the monitoring module, used for receiving the data measured by the monitoring module, and drawing a function graph of the washing and sorting equipment according to the data; a display module, which is connected to the analysis module, used for outputting the early warning signal sent by the analysis module, and sending corresponding early warning information according to the level of the early warning signal; a server module, which is respectively connected to the monitoring module, the analysis module and the display module, and used for storing data generated when the system is running. The present invention can accurately predict the fault information of the washing and sorting equipment, and while improving the prediction accuracy, accurately repair the washing and sorting equipment predicted to have a fault, thereby improving the work efficiency.

Description

Technical Field

[0001] The present invention relates to the field of online monitoring technology, and in particular to an online monitoring system for washing and sorting equipment based on big data. Background Art

[0002] In the coal industry, the production system is complex and has many equipments. The workload of manual inspection is large. Passive maintenance and emergency repairs result in low system production efficiency. There is a problem that it is impossible to predict the fault information of the washing equipment according to the operation status of the washing equipment to carry out maintenance in advance. Summary of the invention

[0003] To this end, the present invention provides an online monitoring system for washing and sorting equipment based on big data, so as to overcome the problem in the prior art that the fault information of the washing and sorting equipment cannot be predicted according to the operating conditions of the washing and sorting equipment.

[0004] To achieve the above object, the present invention provides a method comprising:

[0005] A monitoring module, used for detecting the operation data of the washing and sorting equipment and sending the measured data to the analysis module, wherein the monitoring module includes a detection unit for detecting the operation data of the washing and sorting equipment and a sending unit for sending the operation data;

[0006] An analysis module connected to the monitoring module, for receiving data measured by the monitoring module, and drawing a function image of the washing and sorting equipment according to the data, wherein the analysis module includes a receiving unit for receiving data, a generating unit for drawing a function image according to the data, a comparing unit for comparing the function image with a preset function image, and an early warning unit for sending early warning fault information according to the comparison result; the comparing unit compares the peak coincidence in the actual function image with the preset value, and makes a preliminary prediction of the washing and sorting equipment fault according to the comparison result, wherein the comparing unit compares the average height of the peak in the actual function image with the preset value, and makes a secondary prediction of the washing and sorting equipment fault, and the comparing unit calculates the average height difference of the peak when it is predicted that the washing and sorting equipment has a fault to determine the fault level, and the early warning unit sends a corresponding early warning signal according to the actual predicted fault level of the washing and sorting equipment;

[0007] A display module connected to the analysis module, configured to output the warning signal sent by the analysis module and issue corresponding warning information according to the warning signal level;

[0008] The server module is respectively connected to the monitoring module, the analysis module and the display module, and is used to store data generated when the system is running. The server module includes a statistical unit for storing data generated when the system is running and for statistical data information.

[0009] Further, when the comparison unit compares the function image A of the actual washing and sorting equipment with the preset function image Ab, the comparison unit calculates the peak overlap B of the function image A of the real-time washing and sorting equipment and the peak overlap of the preset function image Ab, compares the peak overlap B with the preset peak overlap, and predicts whether the washing and sorting equipment has a fault according to the comparison result, setting B=(Ba / Bz), wherein Ba is the number of peak overlaps, and Bz is the total number of peaks in the function image Ab;

[0010] When B≥B0, the comparison unit predicts that the washing and sorting equipment is operating normally, and the early warning unit sends a fault-free signal to the display module;

[0011] When B<B0, the comparison unit preliminarily predicts that the washing and sorting equipment has a fault.

[0012] Further, when the comparison unit preliminarily predicts that the washing and sorting equipment has a fault, the comparison detection calculation function image A compares the actual peak average height Bf with the peak flatness height Bf0 in the preset function image Ab to secondarily predict whether the washing and sorting equipment has a fault;

[0013] When Bf>Bf0, the comparison unit predicts that the washing and sorting equipment has a fault, and the early warning unit sends a fault signal to the display module;

[0014] When Bf=Bf0, the comparison unit predicts that the washing and sorting equipment has no fault, and the early warning unit sends a fault-free signal to the display module;

[0015] When Bf<Bf0, the comparison unit needs to combine the degree of wear of the washing equipment to predict again whether the washing equipment has a fault.

[0016] Furthermore, when the comparison unit calculates the average peak height Bf in the function image A, the comparison unit obtains the slope K of the actual function image A, and corrects the actual average peak height Bf according to K. The comparison unit records the corrected actual average peak height as Bf1, and sets Bf1=Bf×(K / K0), where K0 is the preset function image slope.

[0017] Further, when the comparison unit predicts that the washing and sorting equipment has a fault and the early warning unit sends a fault signal to the display module, the early warning unit calculates the peak height difference △B, compares the peak height difference △B with a preset peak height difference, and determines the predicted fault level according to the comparison result, and sets △B=Bf1-Bf0;

[0018] The preset peak height difference values ​​include a first preset peak height difference value △B1, a second preset peak height difference value △B2 and a third preset peak height difference value △B3, wherein △B1<△B2<△B3;

[0019] When △B<△B1, the early warning unit sends a first-level fault signal to the display module;

[0020] When △B1≤△B<△B2, the early warning unit sends a secondary fault signal to the display module;

[0021] When △B2≤△B<△B3, the early warning unit sends a third-level fault signal to the display module;

[0022] When △B≥△B3, the early warning unit sends a fourth-level fault signal to the display module.

[0023] Further, when the comparison unit needs to combine the degree of wear of the washing and sorting equipment to predict again whether the washing and sorting equipment has a fault, the comparison unit calculates the actual wear degree D of the washing and sorting equipment, compares the actual wear degree D with the preset wear degree D0, and predicts again whether the washing and sorting equipment has a fault based on the comparison result;

[0024] When D≥D0, the comparison unit predicts that the washing and sorting equipment has a fault, and the early warning unit sends a fault signal to the display module;

[0025] When D<D0, the comparison unit predicts that there is no fault in the washing and sorting equipment, and the early warning unit sends a fault-free signal to the display module.

[0026] Further, when the comparison unit predicts that there is no fault in the washing and sorting equipment and the early warning unit sends a fault-free signal to the display module, the comparison unit obtains the temperature T of the washing and sorting equipment measured by the detection unit, compares T with a preset temperature T0, and determines whether to modify the preset detection period t0 of the detection unit according to the comparison result;

[0027] When T>T0, the comparison unit determines that the preset detection period t0 of the detection unit needs to be corrected;

[0028] When T=T0, the comparison unit determines that there is no need to modify the preset detection period t0 of the detection unit;

[0029] When T<T0, the comparison unit determines to perform fault detection on the washing and sorting equipment, and the early warning unit sends a fault detection signal to the display module.

[0030] Further, when the comparison unit determines that the preset detection period t0 of the detection unit needs to be corrected, the comparison unit records the corrected preset detection period of the detection unit as t0', and sets t0'=t0×(1-(T-T0) / T0).

[0031] Furthermore, the actual loss degree D is calculated using formula (1);

[0032] D = 10 / (X+Z); (1)

[0033] Wherein, X is the actual working life of the washing equipment, and X≥1; Z is the actual number of maintenance times of the washing equipment.

[0034] Furthermore, the vibration sensor is a three-axis vibration sensor for collecting vibration frequency data of the washing equipment in three directions: X-axis, Y-axis, and Z-axis.

[0035] Compared with the prior art, the beneficial effect of the present invention lies in that, by setting a monitoring module for real-time collection of operating data of washing and sorting equipment, an analysis module for real-time analysis of the operating data of washing and sorting equipment, a display module for real-time display of fault information of washing and sorting equipment and a server module for real-time storage of system operating data, through the mutual cooperation of various modules, the analysis module can grasp the operating status of the washing and sorting equipment in real time, and by drawing the operating function graph of the washing and sorting equipment, on the one hand, it can accurately predict the fault status of the washing and sorting equipment, thereby improving the accuracy of predicted fault information, and accurately repair the equipment predicted to have fault information, thereby improving production efficiency. On the other hand, it can also judge whether there is a fault in the washing and sorting equipment during operation through the function graph, thereby improving the accuracy of predicted fault information and further improving production efficiency.

[0036] Furthermore, the monitoring module of the present invention obtains the operating data of the washing equipment in real time through the detection unit, draws the operating function image of the washing equipment according to the data obtained by the detection unit through the generation unit, and compares it in real time through the comparison unit. On the one hand, by drawing the function image through the generation unit, the operating status of the washing equipment can be grasped more intuitively and three-dimensionally. On the other hand, when the comparison unit compares the function image, by calculating the actual peak overlap, when the peak overlap is less than the preset value, the comparison unit preliminarily determines that the washing equipment has a fault. When the peak overlap is greater than or equal to the preset value, the comparison unit determines that the washing equipment does not have a fault. Through the real-time comparison of the comparison unit, while grasping the operating status of the washing equipment, the accuracy of predicting faults of the washing equipment can be improved, thereby further improving production efficiency.

[0037] Furthermore, the present invention compares the height of the peak in the actual function image with the height of the peak in the preset image through a comparison unit. When the actual peak height average value is greater than the preset value, the comparison unit determines that the operation of the equipment is abnormal, and predicts that the washing equipment has a fault. Through the accurate prediction of the comparison unit, on the one hand, the current operating status of the washing equipment can be grasped in real time, and the fault of the washing equipment can be accurately predicted. On the other hand, by predicting the fault information of the washing equipment, while improving the prediction accuracy, the washing equipment predicted to have a fault can be accurately repaired, thereby improving work efficiency.

[0038] Furthermore, the comparison unit of the embodiment of the present invention reads the slope in the function image A, and corrects the actual average height of the wave peaks according to the actual slope. When the slope of the function image is too large, it indicates that the washing equipment is operating abnormally, and the comparison unit predicts that the washing equipment has a fault. By correcting the actual average height of the wave peaks by the comparison unit, the operating condition of the washing equipment can be more accurately grasped, thereby improving the prediction accuracy and accurately repairing the washing equipment predicted to have a fault, thereby improving work efficiency.

[0039] Furthermore, the early warning unit of the present invention calculates the actual wave peak height difference and compares the actual wave peak height difference with the preset wave peak height difference. When the wave peak difference is too large, the early warning unit predicts that the fault level of the washing equipment is high and sends a high-level fault signal. When the wave peak difference is too small, the early warning unit predicts that the fault level of the washing equipment is low and sends a low-level fault signal. By classifying the fault level by the early warning unit, the fault level of the washing equipment can be accurately predicted, the fault type of the washing equipment can be further understood, and the washing equipment can be accurately repaired. At the same time, maintenance time is saved, maintenance efficiency is improved, and the accuracy of predicting faults of the washing equipment can be further improved, thereby further improving production efficiency.

[0040] Furthermore, the comparison unit in the embodiment of the present invention can accurately grasp the working life of each washing equipment by calculating the wear degree of the washing equipment. When the wear degree of the washing equipment is too large, the comparison unit determines that the average peak height of the peaks in the actual function graph does not match the current operating load of the equipment, and the comparison unit predicts that the equipment has a fault. By grasping the wear degree through the comparison unit, the fault of the washing equipment with an average peak height below the standard value can be accurately predicted. While improving the prediction accuracy, the washing equipment predicted to have a fault can be accurately repaired, thereby improving work efficiency.

[0041] Furthermore, in the embodiment of the present invention, the comparison unit obtains the temperature of the washing equipment measured by the detection unit in real time, and corrects the detection cycle of the washing equipment through different temperatures. When the temperature of the washing equipment is too high, the detection cycle is corrected to make a more accurate prediction of the fault of the washing equipment. When the temperature of the washing equipment is too low, the comparison unit determines to perform fault detection on the washing equipment so as to better repair the washing equipment. Through temperature monitoring, the prediction accuracy can be improved while the washing equipment predicted to have faults can be accurately repaired, thereby improving work efficiency.

[0042] Furthermore, the comparison unit of the embodiment of the present invention can accurately grasp the working life of each washing equipment by calculating the wear degree of the washing equipment. When the equipment is repaired too many times, the actual wear degree of the washing equipment calculated by the comparison unit is greater. When the wear degree of the washing equipment is too large, the comparison unit determines that the average peak height of the peaks in the actual function graph does not match the current operating load of the equipment, and the comparison unit predicts that the equipment has a fault. Through the comparison unit's grasp of the wear degree, the fault of the washing equipment with an average peak height below the standard value can be accurately predicted. While improving the prediction accuracy, the washing equipment predicted to have a fault can be accurately repaired, thereby improving work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a structural block diagram of the online monitoring system for washing and sorting equipment based on big data described in the present invention. DETAILED DESCRIPTION

[0044] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0045] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0046] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0047] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0048] See also Figure 1 As shown, it is a structural block diagram of the online monitoring system for washing and sorting equipment based on big data provided by an embodiment of the present invention, including:

[0049] The monitoring module is used to detect the washing and sorting equipment, and includes a detection unit and a sending unit, wherein the detection unit includes a three-axis vibration sensor for detecting the vibration frequency and a temperature sensor for detecting the temperature. When the monitoring module monitors the washing and sorting equipment, the detection unit detects the actual vibration frequency and actual temperature data of the key components in the washing and sorting equipment in real time, and during the monitoring process, the sending unit sends the data measured by the detection unit to the analysis module.

[0050] Specifically, the monitoring module in the embodiment of the present invention is preferably an intelligent spot inspection device, and a detection unit and a sending unit are provided in the intelligent spot inspection device, wherein the detection unit includes a three-axis vibration sensor for detecting the vibration frequency and a temperature sensor for detecting the temperature, and the detection unit detects the actual vibration frequency and actual temperature data of the key components in the washing and sorting equipment in real time, and during the monitoring process, the sending unit sends the data measured by the detection unit to the analysis module. Specifically, the three-axis vibration sensor collects data on the vibration frequency of the key components of the washing and sorting equipment in three directions of the X-axis, Y-axis, and Z-axis, and the temperature sensor collects data on the temperature of the key components of the washing and sorting equipment, wherein the vibration detection range is 0.1Hz to 1KHz. It can be understood by those skilled in the art that the position of the monitoring module of the present invention is not limited, and it can be an intelligent spot inspection device, or a mobile terminal that can realize vibration frequency and temperature detection, and the monitoring module can also be directly installed at the key components of the washing and sorting equipment, as long as the real-time detection of the washing and sorting equipment can be realized.

[0051] An analysis module is connected to the monitoring module to receive the data measured by the monitoring module and draw the vibration-power function image of the washing and sorting equipment according to the data. The analysis module includes a receiving unit for receiving data, a generating unit for drawing the function image according to the data, a comparing unit for comparing the function image with the preset function image, and an early warning unit for sending early warning fault information according to the comparison result. When the analysis module analyzes the data measured by the monitoring module, the receiving unit receives the data sent by the sending unit, and when the receiving is completed, the generating unit reads the data received in the receiving unit, and draws the vibration-power function image of the actual washing and sorting equipment according to the data, and when the drawing is completed, the generating unit sends the vibration-power function image of the actual washing and sorting equipment to the comparing unit, and the comparing unit compares the vibration-power function image of the actual washing and sorting equipment with the preset vibration-power function image corresponding to the washing and sorting equipment. When the comparison is completed, the early warning unit obtains the comparison result of the comparing unit, and sends the early warning information to the display module according to the actual comparison result. Those skilled in the art will appreciate that a preferred embodiment of the present invention is that the generating unit reads the data received in the receiving unit and draws a vibration-power function image of the actual washing equipment based on the data. The generating unit may also generate a time domain analysis graph, a frequency domain analysis graph or a wavelet analysis graph based on the data received in the receiving unit, as long as the comparing unit can obtain accurate comparison results when comparing the images and the warning unit can accurately send warning information.

[0052] The display module is connected to the analysis module to output the warning signal sent by the analysis module and send corresponding warning information according to the warning signal level. The display module pre-stores the position of each washing and sorting equipment. When sending the warning information, the position of the washing and sorting equipment with the warning fault is marked in the display module. Specifically, in the embodiment of the present invention, the display module sends different colors to indicate different warning information registrations. Specifically, if the display module displays green, it means that the washing and sorting equipment has no fault, if the display module displays yellow, it means that the washing and sorting equipment has a first-level fault, if the display module displays blue, it means that the washing and sorting equipment has a second-level fault, if the display module displays orange, it means that the washing and sorting equipment has a third-level fault, and if the display module displays red, it means that the washing and sorting equipment has a fourth-level fault. The severity of the fault is: fourth-level fault> third-level fault> second-level fault> first-level fault. It can be understood by those skilled in the art that the display module can also use other methods to send warning information, such as voice prompts or other methods that can distinguish the fault level, as long as the fault level can be identified.

[0053] Specifically, in this embodiment, the fault classification is illustrated by examples. The fourth-level fault is shaft misalignment and bearing damage, the third-level fault is coupling damage, the second-level fault is blade damage, and the first-level fault is base looseness. It can be understood by those skilled in the art that the above faults are the preferred classification standards in the embodiments of the present invention, and the classification standards can also be reselected according to actual conditions. At the same time, the embodiments of the present invention do not exhaustively list the fault types of the washing and sorting equipment, and those skilled in the art can classify the fault types according to actual conditions.

[0054] The server module is connected to the monitoring module, the analysis module and the display module respectively, and is used to store the data generated when the system is running. The server module includes a statistical unit for storing the data generated when the system is running and for statistical information. The storage unit also pre-stores the basic information of each washing and sorting equipment. The statistical unit counts the number of failures, the number of alarms, and the proportion of failure levels of each washing and sorting equipment according to the system operation data stored in the storage unit, and when the statistics are completed, the statistical data is generated into a corresponding statistical chart and sent to the storage unit for storage.

[0055] Specifically, in this embodiment, a user terminal may be provided to query the operating status of each washing and sorting equipment in real time, and the operating status of the monitored washing and sorting equipment may be known at any time even in a remote location.

[0056] Specifically, the embodiment of the present invention sets a monitoring module for real-time collection of operating data of the washing equipment, an analysis module for real-time analysis of the operating data of the washing equipment, a display module for real-time display of fault information of the washing equipment, and a server module for real-time storage of system operating data. Through the cooperation of each module, the analysis module can grasp the operating status of the washing equipment in real time, and by drawing the operating function graph of the washing equipment, on the one hand, it can accurately predict the fault condition of the washing equipment, improve the accuracy of the predicted fault information, and accurately repair the equipment predicted to have fault information, thereby improving production efficiency. On the other hand, it can also judge whether there is a fault in the washing equipment during operation through the function graph, which improves the accuracy of the predicted fault information and further improves the production efficiency.

[0057] Specifically, when the comparison unit compares the function image A of the actual washing and sorting equipment with the preset function image Ab, the comparison unit calculates the peak overlap B of the function image A of the real-time washing and sorting equipment and the peak overlap B of the preset function image Ab, compares the peak overlap B with the preset peak overlap, and predicts whether the washing and sorting equipment has a fault according to the comparison result, setting B=(Ba / Bz), wherein Ba is the number of peak overlaps, and Bz is the total number of peaks in the function image Ab;

[0058] When B≥B0, the comparison unit predicts that the washing and sorting equipment is operating normally, and the early warning unit sends a fault-free signal to the display module;

[0059] When B<B0, the comparison unit preliminarily predicts that the washing and sorting equipment has a fault.

[0060] Specifically, the monitoring module of the embodiment of the present invention obtains the operating data of the washing equipment in real time through the detection unit, draws the operating function image of the washing equipment according to the data obtained by the detection unit through the generation unit, and performs real-time comparison through the comparison unit. On the one hand, by drawing the function image through the generation unit, the operating status of the washing equipment can be grasped more intuitively and three-dimensionally. On the other hand, when the comparison unit compares the function image, by calculating the actual peak overlap, when the peak overlap is less than the preset value, the comparison unit preliminarily determines that the washing equipment has a fault. When the peak overlap is greater than or equal to the preset value, the comparison unit determines that the washing equipment does not have a fault. Through the real-time comparison of the comparison unit, while grasping the operating status of the washing equipment, the accuracy of predicting faults of the washing equipment can be improved, thereby further improving production efficiency.

[0061] Specifically, when the comparison unit preliminarily predicts that the washing and sorting equipment has a fault, the actual peak average height Bf in the comparison detection calculation function image A is compared with the peak flatness height Bf0 in the preset function image Ab to secondarily predict whether the washing and sorting equipment has a fault;

[0062] When Bf>Bf0, the comparison unit predicts that the washing and sorting equipment has a fault, and the early warning unit sends a fault signal to the display module;

[0063] When Bf=Bf0, the comparison unit predicts that the washing and sorting equipment has no fault, and the early warning unit sends a fault-free signal to the display module;

[0064] When Bf<Bf0, the comparison unit needs to combine the degree of wear of the washing equipment to predict again whether the washing equipment has a fault.

[0065] Specifically, the embodiment of the present invention compares the height of the peak in the actual function image with the height of the peak in the preset image through a comparison unit. When the actual peak height average value is greater than the preset value, the comparison unit determines that the operation of the equipment is abnormal, and predicts that the washing equipment has a fault. Through the accurate prediction of the comparison unit, on the one hand, the current operating status of the washing equipment can be grasped in real time, and the fault of the washing equipment can be accurately predicted. On the other hand, by predicting the fault information of the washing equipment, while improving the prediction accuracy, the washing equipment predicted to have a fault can be accurately repaired, thereby improving work efficiency.

[0066] Specifically, when the comparison unit calculates the average peak height Bf in the function image A, the comparison unit obtains the slope K of the actual function image A, and corrects the actual average peak height Bf according to K. The comparison unit records the corrected actual average peak height as Bf1, and sets Bf1=Bf×(K / K0), where K0 is the preset function image slope.

[0067] Specifically, the comparison unit of the embodiment of the present invention reads the slope in the function image A, and corrects the actual average height of the wave peak according to the actual slope. When the slope of the function image is too large, it indicates that the washing equipment is operating abnormally, and the comparison unit predicts that the washing equipment has a fault. By correcting the actual average height of the wave peak by the comparison unit, the operating condition of the washing equipment can be more accurately grasped, and while improving the prediction accuracy, the washing equipment predicted to have a fault can be accurately repaired, thereby improving work efficiency.

[0068] Specifically, when the comparison unit predicts that the washing and sorting equipment has a fault and the early warning unit sends a fault signal to the display module, the early warning unit calculates the peak height difference △B, compares the peak height difference △B with the preset peak height difference, and determines the predicted fault level according to the comparison result, and sets △B=Bf1-Bf0;

[0069] The preset peak height difference values ​​include a first preset peak height difference value △B1, a second preset peak height difference value △B2 and a third preset peak height difference value △B3, wherein △B1<△B2<△B3;

[0070] When △B<△B1, the early warning unit sends a first-level fault signal to the display module;

[0071] When △B1≤△B<△B2, the early warning unit sends a secondary fault signal to the display module;

[0072] When △B2≤△B<△B3, the early warning unit sends a third-level fault signal to the display module;

[0073] When △B≥△B3, the early warning unit sends a fourth-level fault signal to the display module.

[0074] Specifically, the early warning unit of the embodiment of the present invention calculates the actual peak height difference and compares the actual peak height difference with the preset peak height difference. When the peak difference is too large, the early warning unit predicts that the fault level of the washing equipment is high and sends a high-level fault signal. When the peak difference is too small, the early warning unit predicts that the fault level of the washing equipment is low and sends a low-level fault signal. By classifying the fault level by the early warning unit, the fault level of the washing equipment can be accurately predicted, the fault type of the washing equipment can be further understood, and the washing equipment can be accurately repaired. At the same time, maintenance time is saved, maintenance efficiency is improved, and the accuracy of predicting faults of the washing equipment can be further improved, thereby further improving production efficiency.

[0075] Specifically, when the comparison unit needs to combine the degree of wear of the washing and sorting equipment to predict again whether the washing and sorting equipment has a fault, the comparison unit calculates the actual wear degree D of the washing and sorting equipment, compares the actual wear degree D with the preset wear degree D0, and predicts again whether the washing and sorting equipment has a fault based on the comparison result;

[0076] When D≥D0, the comparison unit predicts that the washing and sorting equipment has a fault, and the early warning unit sends a fault signal to the display module;

[0077] When D<D0, the comparison unit predicts that there is no fault in the washing and sorting equipment, and the early warning unit sends a fault-free signal to the display module.

[0078] Specifically, the comparison unit of the embodiment of the present invention can accurately grasp the working life of each washing equipment by calculating the wear degree of the washing equipment. When the wear degree of the washing equipment is too large, the comparison unit determines that the average peak height of the peaks in the actual function graph does not match the current operating load of the equipment, and the comparison unit predicts that the equipment has a fault. Through the comparison unit's grasp of the wear degree, the fault of the washing equipment with an average peak height below the standard value can be accurately predicted. While improving the prediction accuracy, the washing equipment predicted to have a fault can be accurately repaired, thereby improving work efficiency.

[0079] Specifically, when the comparison unit predicts that there is no fault in the washing and sorting equipment and the early warning unit sends a fault-free signal to the display module, the comparison unit obtains the temperature T of the washing and sorting equipment measured by the detection unit, compares T with the preset temperature T0, and determines whether to modify the preset detection period t0 of the detection unit according to the comparison result;

[0080] When T>T0, the comparison unit determines that the preset detection period t0 of the detection unit needs to be corrected;

[0081] When T=T0, the comparison unit determines that there is no need to modify the preset detection period t0 of the detection unit;

[0082] When T<T0, the comparison unit determines to perform fault detection on the washing and sorting equipment, and the early warning unit sends a fault detection signal to the display module.

[0083] Specifically, the comparison unit in the embodiment of the present invention obtains the temperature of the washing equipment measured by the detection unit in real time, and corrects the detection cycle of the washing equipment through different temperatures. When the temperature of the washing equipment is too high, the detection cycle is corrected to make a more accurate prediction of the fault of the washing equipment. When the temperature of the washing equipment is too low, the comparison unit determines to perform fault detection on the washing equipment so as to better repair the washing equipment. Through temperature monitoring, the prediction accuracy can be improved while the washing equipment predicted to have faults can be accurately repaired, thereby improving work efficiency.

[0084] Specifically, when the comparison unit determines that the preset detection period t0 of the detection unit needs to be corrected, the comparison unit records the corrected preset detection period of the detection unit as t0', and sets t0'=t0×(1-(T-T0) / T0).

[0085] Specifically, the actual loss degree D is calculated using formula (1);

[0086] D = 10 / (X+Z); (1)

[0087] Wherein, X is the actual working life of the washing equipment, and X≥1; Z is the actual number of maintenance times of the washing equipment.

[0088] Specifically, the preset loss degree D0 is calculated using formula (2);

[0089] D = 10 / (X+Z0); (2)

[0090] Wherein, X is the actual working life of the washing equipment, and X≥1; Z0 is the preset number of maintenance times when the washing equipment has worked for X years.

[0091] Specifically, the comparison unit of the embodiment of the present invention can accurately grasp the working life of each washing equipment by calculating the wear degree of the washing equipment. When the equipment is repaired too many times, the actual wear degree of the washing equipment calculated by the comparison unit is greater. When the wear degree of the washing equipment is too large, the comparison unit determines that the average peak height of the peaks in the actual function graph does not match the current operating load of the equipment, and the comparison unit predicts that the equipment has a fault. Through the comparison unit's grasp of the wear degree, the fault of the washing equipment with an average peak height below the standard value can be accurately predicted. While improving the prediction accuracy, the washing equipment predicted to have a fault can be accurately repaired, thereby improving work efficiency.

[0092] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0093] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An online monitoring system for washing and sorting equipment based on big data, characterized in that: include: A monitoring module, used for detecting the operation data of the washing and sorting equipment and sending the measured data to the analysis module, wherein the monitoring module includes a detection unit for detecting the operation data of the washing and sorting equipment and a sending unit for sending the operation data; An analysis module connected to the monitoring module for receiving data measured by the monitoring module and drawing a function image of the washing equipment according to the data, wherein the analysis module includes a receiving unit for receiving data, a generating unit for drawing a function image according to the data, a comparing unit for comparing the function image with a preset function image, and an early warning unit for sending early warning fault information according to the comparison result; the comparing unit compares the peak coincidence in the actual function image with the preset value, and makes a preliminary prediction of the washing equipment fault according to the comparison result, wherein the comparing unit compares the average height of the peak in the actual function image with the preset value, and makes a secondary prediction of the washing equipment fault, the comparing unit calculates the average height difference of the peak when it is predicted that the washing equipment has a fault to determine the fault level, and the early warning unit sends a corresponding early warning signal according to the actual predicted fault level of the washing equipment; A display module connected to the analysis module, configured to output the warning signal sent by the analysis module and issue corresponding warning information according to the warning signal level; A server module, which is connected to the monitoring module, the analysis module and the display module respectively, and is used to store data generated when the system is running, wherein the server module includes a statistical unit for storing data generated when the system is running and for statistical data information; When the comparison unit compares the function image A of the actual washing and sorting equipment with the preset function image Ab, the comparison unit calculates the peak overlap B of the function image A of the real-time washing and sorting equipment and the peak overlap B of the preset function image Ab, compares the peak overlap B with the preset peak overlap, and predicts whether the washing and sorting equipment has a fault according to the comparison result, setting B=(Ba / Bz), where Ba is the number of peak overlaps and Bz is the total number of peaks in the function image Ab; When B≥B0, the comparison unit predicts that the washing and sorting equipment is operating normally, and the early warning unit sends a fault-free signal to the display module; When B<B0, the comparison unit preliminarily predicts that the washing and sorting equipment has a fault; When the comparison unit preliminarily predicts that the washing and sorting equipment has a fault, the actual peak average height Bf in the comparison detection calculation function image A is compared with the peak flatness height Bf0 in the preset function image Ab to secondarily predict whether the washing and sorting equipment has a fault; When Bf>Bf0, the comparison unit predicts that the washing and sorting equipment has a fault, and the early warning unit sends a fault signal to the display module; When Bf=Bf0, the comparison unit predicts that the washing and sorting equipment has no fault, and the early warning unit sends a fault-free signal to the display module; When Bf<Bf0, the comparison unit needs to combine the degree of wear of the washing equipment to predict again whether the washing equipment has a fault.

2. The online monitoring system for washing and sorting equipment based on big data according to claim 1 is characterized in that: When the comparison unit calculates the average peak height Bf in the function image A, the comparison unit obtains the slope K of the actual function image A, and corrects the actual average peak height Bf according to K. The comparison unit records the corrected actual average peak height Bf as Bf1, and sets Bf1=Bf×(K / K0), where K0 is the preset function image slope.

3. The online monitoring system for washing and sorting equipment based on big data according to claim 2 is characterized in that: When the comparison unit predicts that the washing and sorting equipment has a fault and the early warning unit sends a fault signal to the display module, the early warning unit calculates the peak height difference △B, compares the peak height difference △B with the preset peak height difference, and determines the predicted fault level according to the comparison result, setting △B=Bf1-Bf0; The preset peak height difference values ​​include a first preset peak height difference value △B1, a second preset peak height difference value △B2 and a third preset peak height difference value △B3, wherein △B1<△B2<△B3; When △B<△B1, the early warning unit sends a first-level fault signal to the display module; When △B1≤△B<△B2, the early warning unit sends a secondary fault signal to the display module; When △B2≤△B<△B3, the early warning unit sends a third-level fault signal to the display module; When △B≥△B3, the early warning unit sends a fourth-level fault signal to the display module.

4. The online monitoring system for washing and sorting equipment based on big data according to claim 1 is characterized in that: When the comparison unit needs to combine the degree of wear of the washing and sorting equipment to predict again whether the washing and sorting equipment has a fault, the comparison unit calculates the actual wear degree D of the washing and sorting equipment, compares the actual wear degree D with the preset wear degree D0, and predicts again whether the washing and sorting equipment has a fault based on the comparison result; When D≥D0, the comparison unit predicts that the washing and sorting equipment has a fault, and the early warning unit sends a fault signal to the display module; When D<D0, the comparison unit predicts that there is no fault in the washing and sorting equipment, and the early warning unit sends a fault-free signal to the display module.

5. The online monitoring system for washing and sorting equipment based on big data according to claim 1 is characterized in that: When the comparison unit predicts that the washing and sorting equipment has no fault and the early warning unit sends a fault-free signal to the display module, the comparison unit obtains the temperature T of the washing and sorting equipment measured by the detection unit, compares T with the preset temperature T0, and determines whether to modify the preset detection period t0 of the detection unit according to the comparison result; When T>T0, the comparison unit determines that the preset detection period t0 of the detection unit needs to be corrected; When T=T0, the comparison unit determines that there is no need to modify the preset detection period t0 of the detection unit; When T<T0, the comparison unit determines to perform fault detection on the washing and sorting equipment, and the early warning unit sends a fault detection signal to the display module.

6. The online monitoring system for washing and sorting equipment based on big data according to claim 5 is characterized in that: When the comparison unit determines that the preset detection period t0 of the detection unit needs to be corrected, the comparison unit records the corrected preset detection period of the detection unit as t0', and sets t0'=t0×(1-(T-T0) / T0).

7. The online monitoring system for washing and sorting equipment based on big data according to claim 4 is characterized in that: The actual loss degree D is calculated using formula (1); D = 10 / (X+Z); (1) Wherein, X is the actual working life of the washing equipment, and X≥1; Z is the actual number of maintenance times of the washing equipment.

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