Coal preparation equipment operation state monitoring system based on Internet of Things

Abstract

The invention relates to the field of coal preparation equipment supervision, in particular to a coal preparation equipment operation state monitoring system based on the Internet of Things, and aims to solve the problems that the supervision target of coal preparation equipment is single and independent, and effective operation supervision cannot be carried out on the relevance of multiple equipment among coal preparation production lines. Comprising a coal dressing process division unit, a fault time statistical unit, an operation fault statistical unit, an equipment fault supervision unit and a process correlation analysis unit. According to the method, when the coal preparation equipment operates, classification management is performed on the coal preparation equipment, linear analysis is performed according to the fault occurrence conditions of the equipment in multiple classifications during operation monitoring, and the fault occurrence conditions of the coal preparation equipment in multiple procedures are judged according to the analysis result; therefore, the supervision system of the coal preparation equipment not only can supervise single equipment, but also can maintain and overhaul the equipment in the related working procedures by managers, so that the situation that other equipment is affected and breaks down due to the fact that the equipment in one group of working procedures breaks down is avoided.

Description

Technical Field

[0001] The present invention relates to the field of coal preparation equipment supervision, and in particular to a coal preparation equipment operation status monitoring system based on the Internet of Things. Background Art

[0002] Coal preparation is also called coal washing. It is the process of selecting clean coal that meets the user's quality requirements from raw coal. Coking coal has certain requirements for ash and sulfur content, and clean coal that has been washed must be used. The lithologic composition of coal and the quantity, type, nature and distribution of minerals in coal are all factors that affect the selectivity of coal. With the rapid development of big data and Internet of Things technologies, the world has entered an industrial age dominated by intelligent manufacturing. Among them, coal preparation plants, as an important part of coal mining enterprises, rely on Internet of Things technology, big data analysis technology and information and communication technology in the construction of informatization and intelligence, and have always regarded online monitoring and intelligent management of equipment as the top priority of intelligent construction;

[0003] At present, although there are various supervision methods for coal preparation equipment in the prior art, the supervision is basically based on a single device. When the coal preparation equipment is in operation, multiple devices are often operated in an assembly line manner, and the output of the previous device enters the rear device as raw materials. Therefore, if the output of the previous device is abnormal, it may affect the operation of the rear device, so that when the coal preparation production line is in operation, the failure of a certain device may cause multiple devices to fail, which increases the equipment maintenance cost and difficulty, and affects the normal operation of the coal preparation operation.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0005] In the present invention, when the coal preparation equipment is in operation, the coal preparation equipment is classified and managed, and during operation monitoring, a linear analysis is performed on the failure conditions of equipment in multiple categories, and the failure conditions between the coal preparation equipment in multiple processes are judged based on the analysis results, so that the supervision system of the coal preparation equipment not only supervises a single device, but also enables management personnel to maintain and repair the equipment in related processes while performing maintenance and repair on the coal preparation equipment, so as to avoid the failure of equipment in one process causing other equipment to be affected and fail, thereby solving the problem that the supervision target of the coal preparation equipment is single and independent, and the correlation between multiple equipment between coal preparation production lines cannot be effectively supervised, and a coal preparation equipment operation status monitoring system based on the Internet of Things is proposed.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A coal preparation equipment operation status monitoring system based on the Internet of Things, comprising a coal preparation process division unit, wherein the coal preparation process division unit is used to divide the process of the coal preparation equipment and obtain different equipment classifications;

[0008] An equipment fault monitoring unit, which is used to detect equipment faults, obtain equipment operation information, and analyze the equipment production efficiency, equipment faults, and fault occurrence time according to the equipment operation information;

[0009] A fault time statistics unit, wherein the fault time statistics unit obtains the equipment fault and the fault occurrence time through the equipment fault supervision unit, performs a parallel comparative analysis on the timeline of the fault occurrence, and generates a fault correlation result on the fault timeline according to the comparative analysis result;

[0010] An operation fault statistics unit, wherein the operation fault statistics unit can obtain the equipment fault and the fault occurrence time through the equipment fault supervision unit, analyze the fault occurrence frequency in a preset time unit, and generate a fault frequency correlation result according to the fault and the fault occurrence time;

[0011] The process correlation analysis unit analyzes the fault frequency correlation result and the fault correlation result to generate correlation signals between multiple processes.

[0012] As a preferred embodiment of the present invention, the method of the coal preparation process division unit for classifying and dividing the coal preparation equipment is:

[0013] The coal preparation process division unit obtains the function of the coal preparation equipment, and the coal preparation equipment with the same function is recorded as the same coal preparation process and divided into the same category, and the coal preparation equipment with different functions is divided into different categories. The coal preparation process division unit records the coal preparation equipment by classification and number. After the coal preparation process division is completed, the classification of each coal preparation equipment is sent to the equipment fault monitoring unit.

[0014] As a preferred embodiment of the present invention, when the equipment fault monitoring unit performs fault operation monitoring on the equipment, the monitored operation information includes the operation efficiency of the coal preparation equipment and the fault alarm reminder;

[0015] The equipment fault monitoring unit monitors the operation efficiency by selecting a preset time, calculating the production output of the equipment within the selected preset time, and recording the ratio of the output to the time as the operation efficiency. After obtaining the operation information of the coal preparation equipment, the equipment fault monitoring unit compares the operation efficiency in the operation information of the coal preparation equipment with the preset operation efficiency. If the operation efficiency is greater than or equal to the preset operation efficiency value, no response is made. If the operation efficiency is less than the preset operation efficiency value, an efficiency deficiency signal is generated.

[0016] After the equipment fault monitoring unit obtains the inefficiency signal, it obtains the process sequence of the equipment generating the inefficiency signal. If the equipment generating the inefficiency signal is at the front of the process sequence, the inefficiency signal is converted into an inefficiency alarm. If there is a process generating an inefficiency signal in front of the equipment generating the inefficiency signal, a suspected inefficiency signal is generated.

[0017] As a preferred embodiment of the present invention, the device operation supervision unit obtains the fault alarm reminder by performing self-inspection through the device's own fault self-inspection program. When the fault weight is greater than the preset fault weight, a fault alarm reminder is generated. When the fault weight is less than or equal to the preset fault weight, no response is made. When the device operation supervision unit generates a fault alarm reminder, it also records the time when the fault alarm reminder is generated, and sends the fault alarm reminder and the time when the fault alarm reminder is generated to the fault time statistics unit of the operation fault statistics unit at the same time.

[0018] As a preferred implementation of the present invention, the method for the operation fault statistics unit to analyze the frequency of occurrence of faults is:

[0019] The operation fault statistics unit counts the number of fault alarm reminders in each month in units of months, and records it as the fault frequency. The operation fault statistics unit uses time as the horizontal axis and the fault frequency as the vertical axis to draw a total fault change line. The operation fault statistics unit collects the highest point of the total fault change line and records it as the fault peak value. The operation fault statistics unit compares the fault peak value with a preset fault threshold value. If the fault peak value is greater than the preset fault threshold value, a fault rate too high signal is generated;

[0020] The operation fault statistics unit calculates the slope of each line segment in the total fault change curve, records it as the segment slope, performs arithmetic averaging on all segment slopes, and records it as the average slope. If the average slope is positive and the average slope is greater than a preset value, a fault increase signal is generated. If the average slope is positive but the average slope is less than the preset value, no response is made. If the average slope is negative, no response is made.

[0021] As a preferred implementation of the present invention, the generating fault frequency association result runs the fault statistics unit to count the number of fault alarm reminders of the coal preparation equipment under each classification in units of each month, and records it as the classification fault frequency, runs the fault statistics unit to count the total number of fault frequencies in each classification, and calculates the ratio of the fault frequency of each month to the total number of fault frequencies, and records it as the fault occurrence ratio of that month;

[0022] The running fault statistics unit selects any two adjacent categories and compares the monthly fault proportions in the two selected adjacent categories one by one. If the difference in the monthly fault proportions of the two adjacent categories is within the preset range when compared, it is recorded as a coincidence. If the difference in the monthly fault proportions of the two adjacent categories is not within the preset range when compared, it is recorded as a non-coincidence. The running fault statistics unit compares all adjacent categories pairwise and sends the number of coincidences and non-coincidences of the comparison results to the process association analysis unit.

[0023] As a preferred implementation mode of the present invention, the fault time statistics unit selects a classification to draw a time axis, and marks the fault alarm reminder on the time axis according to the time when the fault alarm reminder occurs, so as to obtain the fault time axis, and the fault time statistics unit draws the fault time axis for all classifications;

[0024] The fault time statistics unit selects any two categories for parallel comparison. The fault time statistics unit selects a fault alarm reminder on one of the groups of fault time axes, and extends a preset length to both sides with the fault alarm reminder as the midpoint. If a fault alarm reminder on another group of fault time axes exists within the extended preset length, the two groups of fault alarm reminders are recorded as associated faults. If a fault alarm reminder on another group of fault time axes does not exist within the extended preset length, the fault alarm reminder is recorded as an unassociated fault. The fault time statistics unit compares all adjacent categories in pairs, and sends the obtained corresponding associated fault times and unassociated fault times to the process association analysis unit.

[0025] As a preferred embodiment of the present invention, the process association analysis unit selects any two adjacent classifications, and calculates the sum of the number of overlaps, the number of non-overlaps, the number of associated failures and the number of non-associated failures, and records them as the sample cardinality; calculates the sum of the number of overlaps and the number of associated failures, and records them as the sample marking number; the process association analysis unit calculates the ratio of the sample marking number to the sample cardinality, and records it as the marking ratio; if the marking ratio is greater than the preset standard value, the two selected adjacent classifications are recorded as related process classifications; if the marking ratio is less than the preset standard value, the two selected adjacent classifications are recorded as unrelated process classifications.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] 1. In the present invention, when the coal preparation equipment is in operation, the coal preparation equipment is classified and managed, and during operation monitoring, a linear analysis is performed on the failure conditions of the equipment in multiple categories, and the failure conditions between the coal preparation equipment in multiple processes are judged based on the analysis results, so that the supervision system of the coal preparation equipment not only supervises a single device, but also enables the management personnel to maintain and repair the equipment in related processes while performing maintenance and repair on the coal preparation equipment, so as to avoid failures of other equipment caused by failures of equipment in one group of processes.

[0028] 2. In the present invention, the production efficiency of the coal preparation equipment during operation is supervised to obtain the production efficiency supervision result of the coal preparation equipment. At the same time, when obtaining the production efficiency supervision result, a linear analysis is performed according to the process flow, so that the supervision of production efficiency can be combined with the raw material delivery process to avoid the misjudgment of the production efficiency of the subsequent process due to the process chain break. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0030] Figure 1 is a system block diagram of the present invention;

[0031] Figure 2 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0032] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] Embodiment 1:

[0034] See also Figure 1 - Figure 2As shown, a coal preparation equipment operation status monitoring system based on the Internet of Things includes a coal preparation process division unit, a fault time statistics unit, an operation fault statistics unit, an equipment fault supervision unit and a process correlation analysis unit. The coal preparation process division unit can classify and divide multiple coal preparation equipment when the coal preparation equipment is running. The classification is based on the different functions of the coal preparation equipment. Coal preparation equipment with the same function is recorded as the same coal preparation process and divided into the same classification. Coal preparation equipment with different functions is divided into different classifications. The coal preparation process division unit records the classification number of the coal preparation equipment as i, i=1, 2, 3, ..., n, wherein the number of the preceding coal preparation process is at the front, and the number of the subsequent coal preparation process is at the back, so that the coal preparation processes are sequentially numbered according to the process sequence. After the coal preparation process division is completed, the classification of each coal preparation equipment is sent to the equipment fault supervision unit;

[0035] The equipment fault monitoring unit performs fault operation monitoring on each equipment in each category to obtain the operation information of the equipment. The fault operation monitoring includes the operation efficiency of the coal preparation equipment and the fault alarm reminder. The equipment fault monitoring unit monitors the operation efficiency by selecting a preset time, and calculating the production output of the equipment within the selected preset time, and recording the ratio of the output to the time as the operation efficiency. After the equipment fault monitoring unit obtains the operation information of the coal preparation equipment, the operation efficiency in the operation information of the coal preparation equipment is compared with the preset operation efficiency. If the operation efficiency is greater than or equal to the preset operation efficiency value, no response is made. If the operation efficiency is less than the preset operation efficiency value, an efficiency deficiency signal is generated. After the equipment fault monitoring unit obtains the efficiency deficiency signal , obtain the process sequence of the equipment generating the inefficiency signal; if the equipment generating the inefficiency signal is at the front of the process sequence, the inefficiency signal is converted into an inefficiency alarm; if there is a process generating the inefficiency signal in front of the equipment generating the inefficiency signal, a suspected inefficiency signal is generated; the suspected inefficiency signal and the inefficiency signal are sent to the management personnel through the network; after the management personnel obtain the inefficiency signal, they maintain the corresponding equipment; after the management personnel obtain the suspected inefficiency signal, they first maintain the equipment generating the inefficiency signal in the preceding process equipment; if the suspected inefficiency signal disappears after the maintenance is completed, no response is made; if the suspected inefficiency signal still exists, the equipment generating the suspected inefficiency signal is maintained;

[0036] The equipment operation supervision unit obtains the fault alarm reminder by performing self-check through the equipment's own fault self-check program. When the fault weight is greater than the preset fault weight, a fault alarm reminder is generated. When the fault weight is less than or equal to the preset fault weight, no response is made. When the equipment operation supervision unit generates the fault alarm reminder, it also records the time when the fault alarm reminder is generated, and sends the fault alarm reminder and the time when the fault alarm reminder is generated to the fault time statistics unit of the operation fault statistics unit at the same time;

[0037] The operation fault statistics unit counts the number of fault alarm reminders in each month on a monthly basis and records it as the fault frequency. The operation fault statistics unit draws a total fault change line with time as the horizontal axis and the fault frequency as the vertical axis. The operation fault statistics unit collects the highest point of the total fault change line and records it as the fault peak value. The operation fault statistics unit compares the fault peak value with the preset fault threshold. If the fault peak value is greater than the preset fault threshold, a high fault rate signal is generated and sent to the management personnel through the network. The management personnel perform large-scale maintenance and overhaul of the coal preparation equipment based on the high fault rate signal to avoid increased maintenance costs and affected production efficiency due to frequent failures of the coal preparation equipment.

[0038] The operation fault statistics unit calculates the slope of each line segment in the total fault change curve and records it as the segment slope. The arithmetic average of all segment slopes is recorded as the average slope. If the average slope is positive and greater than the preset value, an increased fault signal is generated. If the average slope is positive but less than the preset value, no response is made. If the average slope is negative, no response is made. The equipment operation supervision unit sends the increased fault signal to the management personnel through the network. After receiving the increased fault signal, the management personnel will maintain the coal preparation equipment in advance to avoid the increase in coal preparation equipment failures and the impact on coal preparation production.

[0039] Embodiment 2:

[0040] See also Figure 1 - Figure 2 As shown, the operation fault statistics unit counts the number of fault alarm reminders of coal preparation equipment under each category on a monthly basis, and records it as the category fault frequency. The operation fault statistics unit counts the total number of fault frequencies in each category, and calculates the ratio of the fault frequency of each month to the total number of fault frequencies, and records it as the fault occurrence ratio of the month.

[0041] The running fault statistics unit selects any two adjacent categories, and compares the monthly fault proportions in the two selected adjacent categories one by one. When comparing, the same month is selected for comparison. If the difference in the monthly fault proportions of the two adjacent categories is within the preset range when compared, it is recorded as a coincidence. If the difference in the monthly fault proportions of the two adjacent categories is not within the preset range when compared, it is recorded as a non-coincidence. The running fault statistics unit compares all adjacent categories pairwise, and sends the number of coincidences and non-coincidences of the comparison results to the process association analysis unit.

[0042] Embodiment three:

[0043] See also Figure 1 - Figure 2 As shown, the fault time statistics unit selects a category i, draws a time axis, and marks the fault alarm reminder on the time axis according to the time when the fault alarm reminder occurs, to obtain the fault time axis, and the fault time statistics unit selects another category i, and draws the fault time axis again, until the fault time axes of all categories are drawn;

[0044] The failure time statistics unit selects any two adjacent categories, and compares the failure time axes of the two adjacent categories in parallel. The failure time statistics unit selects a fault alarm reminder on one of the groups of fault time axes, and extends a preset length to both sides with the fault alarm reminder as the midpoint. If a fault alarm reminder on another group of fault time axes exists within the extended preset length, the two groups of fault alarm reminders are recorded as associated faults. If a fault alarm reminder on another group of fault time axes does not exist within the extended preset length, the fault alarm reminder is recorded as an unrelated fault. The failure time statistics unit compares all adjacent categories in pairs, and sends the corresponding associated fault times and unrelated fault times obtained to the process association analysis unit.

[0045] The process association analysis unit selects any two adjacent classifications, and obtains the number of overlaps, non-overlaps, associated faults and unassociated faults of the two selected adjacent classifications. The process association analysis unit calculates the sum of the number of overlaps, non-overlaps, associated faults and unassociated faults, and records them as the sample base number, calculates the sum of the number of overlaps and the number of associated faults, and records them as the sample mark number. The process association analysis unit calculates the ratio of the sample mark number to the sample base number, and records it as the mark ratio. If the mark ratio is greater than the preset standard value, the two selected adjacent classifications are recorded as related process classifications. If the mark ratio is less than the preset standard value, the two selected adjacent classifications are recorded as unrelated process classifications. The process association analysis unit sends the related process classifications to the management personnel through the network. After the management personnel obtains the related processes, when performing maintenance and repair on the coal preparation equipment, they also perform maintenance and repair on the equipment in the related processes to avoid failures of equipment in a group of processes causing other equipment to be affected and fail.

[0046] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A coal preparation equipment operation status monitoring system based on the Internet of Things, characterized in that: It includes a coal preparation process division unit, which is used to divide the process of coal preparation equipment and obtain different equipment classifications; An equipment fault monitoring unit, which is used to detect equipment faults, obtain equipment operation information, and analyze the equipment production efficiency, equipment faults, and fault occurrence time according to the equipment operation information; A fault time statistics unit, wherein the fault time statistics unit obtains the equipment fault and the fault occurrence time through the equipment fault supervision unit, performs a parallel comparative analysis on the timeline of the fault occurrence, and generates a fault correlation result on the fault timeline according to the comparative analysis result; An operation fault statistics unit, wherein the operation fault statistics unit can obtain the equipment fault and the fault occurrence time through the equipment fault supervision unit, analyze the fault occurrence frequency in a preset time unit, and generate a fault frequency correlation result according to the fault and the fault occurrence time; The process correlation analysis unit analyzes the fault frequency correlation result and the fault correlation result to generate correlation signals between multiple processes.

2. The coal preparation equipment operation status monitoring system based on the Internet of Things according to claim 1 is characterized in that: The method of the coal preparation process division unit for classifying and dividing the coal preparation equipment is as follows: The coal preparation process division unit obtains the function of the coal preparation equipment, and the coal preparation equipment with the same function is recorded as the same coal preparation process and divided into the same category, and the coal preparation equipment with different functions is divided into different categories. The coal preparation process division unit records the coal preparation equipment by classification and number. After the coal preparation process division is completed, the classification of each coal preparation equipment is sent to the equipment fault monitoring unit.

3. The coal preparation equipment operation status monitoring system based on the Internet of Things according to claim 1 is characterized in that: When the equipment fault monitoring unit performs fault operation monitoring on the equipment, the monitored operation information includes the operation efficiency of the coal preparation equipment and the fault alarm reminder; The equipment fault monitoring unit monitors the operation efficiency by selecting a preset time, calculating the production output of the equipment within the selected preset time, and recording the ratio of the output to the time as the operation efficiency. After obtaining the operation information of the coal preparation equipment, the equipment fault monitoring unit compares the operation efficiency in the operation information of the coal preparation equipment with the preset operation efficiency. If the operation efficiency is greater than or equal to the preset operation efficiency value, no response is made. If the operation efficiency is less than the preset operation efficiency value, an efficiency deficiency signal is generated. After the equipment fault monitoring unit obtains the inefficiency signal, it obtains the process sequence of the equipment generating the inefficiency signal. If the equipment generating the inefficiency signal is at the front of the process sequence, the inefficiency signal is converted into an inefficiency alarm. If there is a process generating an inefficiency signal in front of the equipment generating the inefficiency signal, a suspected inefficiency signal is generated.

4. The coal preparation equipment operation status monitoring system based on the Internet of Things according to claim 1 is characterized in that: The equipment operation supervision unit obtains the fault alarm reminder by performing self-inspection through the equipment's own fault self-inspection program. When the fault weight is greater than the preset fault weight, a fault alarm reminder is generated. When the fault weight is less than or equal to the preset fault weight, no response is made. When the equipment operation supervision unit generates the fault alarm reminder, it also records the time when the fault alarm reminder is generated, and sends the fault alarm reminder and the time when the fault alarm reminder is generated to the fault time statistics unit of the operation fault statistics unit at the same time.

5. The coal preparation equipment operation status monitoring system based on the Internet of Things according to claim 1 is characterized in that: The method for the operation fault statistics unit to analyze the frequency of occurrence of faults is: The operation fault statistics unit counts the number of fault alarm reminders in each month in units of months, and records it as the fault frequency. The operation fault statistics unit uses time as the horizontal axis and the fault frequency as the vertical axis to draw a total fault change line. The operation fault statistics unit collects the highest point of the total fault change line and records it as the fault peak value. The operation fault statistics unit compares the fault peak value with a preset fault threshold value. If the fault peak value is greater than the preset fault threshold value, a fault rate too high signal is generated; The operation fault statistics unit calculates the slope of each line segment in the total fault change curve, records it as the segment slope, performs arithmetic averaging on all segment slopes, and records it as the average slope. If the average slope is positive and the average slope is greater than a preset value, a fault increase signal is generated. If the average slope is positive but the average slope is less than the preset value, no response is made. If the average slope is negative, no response is made.

6. The coal preparation equipment operation status monitoring system based on the Internet of Things according to claim 1 is characterized in that: The generated fault frequency association result runs the fault statistics unit to count the number of fault alarm reminders of the coal preparation equipment under each category in units of each month, and records it as the category fault frequency. The fault statistics unit is run to count the total number of fault frequencies in each category, and the ratio of the fault frequency of each month to the total number of fault frequencies is calculated, and recorded as the fault occurrence ratio of the month; The running fault statistics unit selects any two adjacent categories and compares the monthly fault proportions in the two selected adjacent categories one by one. If the difference in the monthly fault proportions of the two adjacent categories is within the preset range when compared, it is recorded as a coincidence. If the difference in the monthly fault proportions of the two adjacent categories is not within the preset range when compared, it is recorded as a non-coincidence. The running fault statistics unit compares all adjacent categories pairwise and sends the number of coincidences and non-coincidences of the comparison results to the process association analysis unit.

7. The coal preparation equipment operation status monitoring system based on the Internet of Things according to claim 1 is characterized in that: The fault time statistics unit selects a category to draw a time axis, and marks the fault alarm reminder on the time axis according to the time when the fault alarm reminder occurs, to obtain the fault time axis, and the fault time statistics unit draws the fault time axis for all categories; The fault time statistics unit selects any two categories for parallel comparison. The fault time statistics unit selects a fault alarm reminder on one of the groups of fault time axes, and extends a preset length to both sides with the fault alarm reminder as the midpoint. If a fault alarm reminder on another group of fault time axes exists within the extended preset length, the two groups of fault alarm reminders are recorded as associated faults. If a fault alarm reminder on another group of fault time axes does not exist within the extended preset length, the fault alarm reminder is recorded as an unassociated fault. The fault time statistics unit compares all adjacent categories in pairs, and sends the obtained corresponding associated fault times and unassociated fault times to the process association analysis unit.

8. The coal preparation equipment operation status monitoring system based on the Internet of Things according to claim 1 is characterized in that: The process association analysis unit selects any two adjacent classifications, and calculates the sum of the number of overlaps, the number of non-overlaps, the number of associated failures and the number of non-associated failures, and records them as the sample cardinality; calculates the sum of the number of overlaps and the number of associated failures, and records them as the sample mark number; the process association analysis unit calculates the ratio of the sample mark number to the sample cardinality, and records it as the mark ratio; if the mark ratio is greater than a preset standard value, the two selected adjacent classifications are recorded as related process classifications; if the mark ratio is less than the preset standard value, the two selected adjacent classifications are recorded as unrelated process classifications.

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