Abnormal detection device

Abstract

This abnormality determination device determines whether or not there is an abnormality in a pulverizer that pulverizes a solid fuel and supplies the solid fuel to a boiler of a thermal power plant. The abnormality determination device includes: a data acquisition unit that acquires operation data indicating the operation state of the pulverizer; and an abnormality determination unit that determines whether or not there is an abnormality in the pulverizer on the basis of whether or not the acquired operation data is within a predetermined range.

Description

Technical Field

[0001] The present disclosure relates to an abnormality determination device for determining the presence or absence of an abnormality in a crusher.

Background Art

[0002] There is a crusher that pulverizes solid fuel supplied to a boiler of a thermal power plant. In such a crusher, for various reasons, it may become impossible to appropriately pulverize the solid fuel. In this case, the solid fuel may accumulate excessively in the crusher. However, the pulverization of the solid fuel is performed inside the housing of the crusher, and it is difficult to directly visually recognize the state of accumulation of the solid fuel inside the crusher. For this reason, for example, Patent Document 1 describes a device that determines an abnormality in a crusher using operation data of a boiler or the like.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The device described in Patent Document 1 uses operation data of a boiler different from the crusher to determine the presence or absence of an abnormality in the crusher. However, it is conceivable that changes in the state of the crusher may not be appropriately reflected in the operation data of the boiler. For this reason, the present disclosure will describe an abnormality determination device that can more accurately determine the presence or absence of an abnormality in a crusher.

Means for Solving the Problems

[0005] One aspect of the present disclosure is an abnormality determination device for determining whether there is an abnormality in a crusher that crushes solid fuel and supplies it to a boiler of a thermal power plant. The abnormality determination device comprises a data acquisition unit that acquires operating data indicating the operating status of the crusher, and an abnormality determination unit that determines whether there is an abnormality in the crusher based on the acquired operating data. [Effects of the Invention]

[0006] According to one aspect of this disclosure, the presence or absence of abnormalities in the crusher can be determined with greater accuracy using the crusher's operating data. [Brief explanation of the drawing]

[0007] [Figure 1] Figure 1 shows an example of a schematic configuration of a maintenance management system for a thermal power plant equipped with an anomaly detection device. [Figure 2] Figure 2 is a schematic diagram showing an example of the internal configuration of a crusher. [Figure 3] The upper part of Figure 3(a) shows the distribution of the first indicator. The lower part of Figure 3(a) shows the time series data of the deviation amount of the first indicator. The upper part of Figure 3(b) shows the distribution of the second indicator. The lower part of Figure 3(b) shows the time series data of the deviation amount of the second indicator. [Figure 4] The upper part of Figure 4(a) shows the distribution of the third indicator. The lower part of Figure 4(a) shows the time series data of the deviation amount of the third indicator. The upper part of Figure 4(b) shows the distribution of the fourth indicator. The lower part of Figure 4(b) shows the time series data of the deviation amount of the fourth indicator. [Modes for carrying out the invention]

[0008] One aspect of the present disclosure is an abnormality determination device for determining whether there is an abnormality in a crusher that crushes solid fuel and supplies it to a boiler of a thermal power plant. The abnormality determination device comprises a data acquisition unit that acquires operating data indicating the operating status of the crusher, and an abnormality determination unit that determines whether there is an abnormality in the crusher based on the acquired operating data.

[0009] In this abnormality detection device, the abnormality detection unit determines the presence or absence of an abnormality based on the operating data of the crusher, which is likely to show changes in the crusher's condition. In this way, the abnormality detection device can determine the presence or absence of an abnormality in the crusher with greater accuracy by using the operating data of the crusher.

[0010] The abnormality detection device described above may further include an index calculation unit that calculates an index showing the correlation between two different operating data sets acquired by the data acquisition unit. The abnormality detection unit may determine that there is an abnormality in the crusher if the calculated index falls outside a predetermined index range. In this case, the abnormality detection device can determine whether or not there is an abnormality in the crusher with greater accuracy by using an index showing the correlation between the two operating data sets.

[0011] The abnormality detection device described above may further include an index calculation unit that calculates multiple indices that show the correlation between two different sets of operating data acquired by the data acquisition unit. The multiple indices calculated are each based on a different combination of two sets of operating data used to calculate the index. For each of the multiple indices, the abnormality detection unit determines whether or not the index falls outside a predetermined index range for that index. The abnormality detection unit may determine that there is an abnormality in the crusher if the number of indices that fall outside the index range is equal to or greater than a predetermined standard number. In this case, the abnormality detection device can determine the presence or absence of an abnormality in the crusher with greater accuracy by using multiple indices that show the correlation between operating data.

[0012] The above-described abnormality detection device may further include an index calculation unit that calculates multiple indices that show the correlation between two different sets of operating data acquired by the data acquisition unit. The multiple indices calculated are each based on different combinations of two sets of operating data used to calculate the indices. For each of the multiple indices, the abnormality detection unit calculates the degree of abnormality based on the deviation between the index and a predetermined index standard value for each index. The abnormality detection unit may determine that there is an abnormality in the crusher if the sum of the multiple abnormality indices calculated for each index exceeds a predetermined standard sum. In this case, the abnormality detection device can determine whether or not there is an abnormality in the crusher with greater accuracy by using the sum of the abnormality indices of the multiple indices.

[0013] In the abnormality detection device described above, the operating data may include at least one of the following: the flow rate of supply air supplied into the pulverizer to send the powdered solid fuel pulverized inside the pulverizer out of the pulverizer by airflow; the opening degree of the flow damper that controls the flow rate of supply air; the amount of solid fuel supplied to the pulverizer; the pressure difference between the pressure of the supply air and the pressure of the discharge air sent out from the pulverizer to send the powder out by airflow; and the current value of the power supplied to the electric motor that drives the movable part that pulverizes the solid fuel, which is installed in the pulverizer. In this case, the abnormality detection device can accurately determine whether or not there is an abnormality in the pulverizer based on this operating data related to the pulverizer.

[0014] Embodiments of this disclosure will be described below with reference to the drawings. In each drawing, the same or corresponding elements are denoted by the same reference numerals, and redundant explanations are omitted.

[0015] As shown in Figure 1, the maintenance management system A includes a thermal power plant 1 and an abnormality detection device 2. The thermal power plant 1 generates electricity using heat obtained by burning solid fuel. The solid fuel may be, for example, coal and biomass fuel. The thermal power plant 1 includes a crusher 10, a boiler 11, a steam turbine 12, and a generator 13. The crusher 10 crushes the solid fuel and supplies it to the boiler 11. The boiler 11 burns the crushed solid fuel to generate steam. The steam generated in the boiler 11 is sent to the steam turbine 12 and drives the steam turbine 12. The generator 13 is connected to the steam turbine 12. The generator 13 generates electricity when the steam turbine 12 is driven by steam. Although not shown or described in detail, the thermal power plant 1 is equipped with various devices such as a device for purifying exhaust gas.

[0016] Here, we will describe an example of the details of the crusher 10. As shown in Figure 2, the crusher 10 crushes solid fuel into a powder so that it can be transported by airflow. The crusher 10 is equipped with a rotary table (movable part) 101, mill rollers 102, an electric motor 103, a coal chute 104, and a crushing ECU (Electronic Control Unit) 105. The rotary table 101 is installed in the lower part of the housing 100 of the crusher 10. The rotary table 101 is rotationally driven by the electric motor 103. The mill rollers 102 are installed so as to be pressed against the upper surface of the rotary table 101. The mill rollers 102 rotate in conjunction with the rotation of the rotary table 101. For example, there are three mill rollers 102.

[0017] The coal chute 104 supplies solid fuel transported from the storage bunker onto the rotary table 101. The solid fuel is supplied via the coal chute 104 to the area near the center of rotation on the upper surface of the rotary table 101. The solid fuel supplied onto the rotary table 101 is crushed by being squeezed between the upper surface of the rotary table 101 and the mill roller 102. In this way, the rotary table 101 and the mill roller 102 crush the supplied solid fuel into a powder.

[0018] At the lower position of the housing 100, an air supply pipe L1 for supplying air into the housing 100 is connected. A flow damper D for controlling the flow rate of the supply air supplied into the housing 100 is provided in the air supply pipe L1. At the upper position of the housing 100, a powder delivery pipe L2 for sending out the pulverized solid fuel powder from the pulverizer 10 to the boiler 11 is connected. The powder of the solid fuel pulverized by the rotary table 101 and the mill roller 102 rides on the air current of the air supplied into the housing 100 from the air supply pipe L1 and is sent out of the housing 100 through the powder delivery pipe L2.

[0019] The pulverization ECU 105 controls the pulverization of the solid fuel in the pulverizer 10. The pulverization ECU 105 is an electronic control unit having, for example, a CPU (Central Processing Unit), a ROM (Read Only Memory), and a RAM (Random Access Memory). The pulverization ECU 105 realizes various functions by, for example, loading a program recorded in the ROM into the RAM and executing the program loaded into the RAM with the CPU.

[0020] Here, the pulverization ECU 105 controls the electric motor 103 and the flow damper D so that a certain amount of the pulverized solid fuel powder is sent out from the pulverizer 10. For example, when the amount of the pulverized solid fuel powder sent out becomes small, the pulverization ECU 105 controls the electric motor 103 so that the rotation speed of the rotary table 101 becomes faster. Here, the pulverization ECU 105 increases the current value of the electric power supplied to the electric motor 103 to increase the rotation speed of the rotary table 101. Also, for example, when the amount of the pulverized solid fuel powder sent out becomes small, the pulverization ECU 105 controls the flow damper D so that the opening degree of the flow damper D becomes larger.

[0021] As shown in FIG. 1, the abnormality determination device 2 can acquire the operation data of the pulverizer 10 by performing wired or wireless communication with the thermal power plant 1. The abnormality determination device 2 determines the presence or absence of an abnormality in the pulverizer 10 provided in the thermal power plant 1 based on the operation data of the pulverizer 10. Here, the "abnormality" refers to a state in which solid fuel cannot be appropriately pulverized due to various reasons and an excessive amount of solid fuel remains in the pulverizer 10. Further, the "abnormality" here is not limited to the occurrence of an abnormality, but also includes a sign of an abnormality. The abnormality determination device 2 may be a server that supports the maintenance of the thermal power plant 1 and may be configured using cloud computing.

[0022] The abnormality determination device 2 includes a determination ECU 20 and a presentation unit 30. The presentation unit 30 is a device that presents information to a supervisor or the like of the thermal power plant 1. For example, the presentation unit 30 may include at least one of a monitor and a lamp that present information through vision and a speaker that outputs sound. The presentation unit 30 presents to a supervisor or the like of the thermal power plant 1 that an abnormality has occurred in the pulverizer 10 based on an instruction from the determination ECU 20.

[0023] The determination ECU 20 is an electronic control unit having, for example, a CPU, a ROM, and a RAM, similar to the pulverization ECU 105 of the pulverizer 10. Functionally, the determination ECU 20 includes a data acquisition unit 21, an index calculation unit 22, an abnormality determination unit 23, and a presentation control unit 24.

[0024] The data acquisition unit 21 acquires operation data indicating the operation state of the pulverizer 10 by communicating with the thermal power plant 1. The data acquisition unit 21 acquires a plurality of operation data indicating the operation state of the pulverizer 10. In the present embodiment, the data acquisition unit 21 acquires five pieces of operation data. Here, the data acquisition unit 21 acquires five pieces of operation data: (1) the flow rate of supply air, (2) the opening degree of the flow damper, (3) the supply amount of solid fuel, (4) the differential pressure, and (5) the current value.

[0025] (1) The flow rate of the supplied air is the flow rate of air supplied into the crusher 10 to send the solid fuel powder crushed in the crusher 10 out of the crusher 10 by airflow. In other words, (1) the flow rate of the supplied air is the flow rate of air supplied from the air supply pipe L1 into the housing 100. For example, the detected value of an air flow sensor installed in the air supply pipe L1 may be used as this (1) flow rate of the supplied air.

[0026] (2) The flow damper opening is the opening of the flow damper D, which controls the flow rate of the supply air supplied into the housing 100 via the air supply pipe L1. For example, the flow damper opening may be the control value used when the crushing ECU 105 controls the opening of the flow damper D. (3) The amount of solid fuel supplied is the amount of solid fuel supplied into the crusher 10 via the coal chute 104. (3) The amount of solid fuel supplied may be the weight of solid fuel transported per unit time from the storage bunker to the coal chute 104.

[0027] (4) The differential pressure is the pressure difference between the pressure of the supply air and the pressure of the discharged air sent from the pulverizer 10 to deliver the solid fuel powder. The pressure of the supply air is the pressure of the air supplied from the air supply pipe L1 into the housing 100 of the pulverizer 10. For example, the pressure of the supply air may be the value detected by an air pressure sensor installed on the air supply pipe L1 near the connection point between the housing 100 and the air supply pipe L1. The pressure of the discharged air sent from the pulverizer 10 is the pressure of the air sent from the housing 100 of the pulverizer 10 to the powder discharge pipe L2. For example, the pressure of the discharged air may be the value detected by an air pressure sensor installed on the powder discharge pipe L2 near the connection point between the housing 100 and the powder discharge pipe L2.

[0028] (5) The current value is the current value of the power supplied to the electric motor 103 that rotates the rotary table 101. For example, the current value may be the detected value of a current sensor that detects the current value of the power supplied to the electric motor 103. Alternatively, the current value may be the control value used when the crushing ECU 105 controls the current value of the power supplied to the electric motor 103.

[0029] The index calculation unit 22 uses two different sets of operating data acquired by the data acquisition unit 21 to calculate an index that shows the correlation between these two sets of operating data. The two different sets of operating data can be of different types. Furthermore, the two different sets of operating data can be acquired using different methods.

[0030] In this embodiment, the index calculation unit 22 calculates four indices: a first index, a second index, a third index, and a fourth index. The combination of two operating data used to calculate the first index, the combination of two operating data used to calculate the second index, the combination of two operating data used to calculate the third index, and the combination of two operating data used to calculate the fourth index are all different from each other.

[0031] For example, the index calculation unit 22 calculates an index as a first index that shows the correlation between (1) the flow rate of the supplied air and (2) the flow damper opening. This first index is represented by a distribution diagram with the vertical axis being (1) the flow rate of the supplied air and the horizontal axis being (2) the flow damper opening, as shown in the upper part of Figure 3(a). For example, the index calculation unit 22 calculates an index as a second index that shows the correlation between (1) the flow rate of the supplied air and (3) the amount of solid fuel supplied. This second index is represented by a distribution diagram with the vertical axis being (1) the flow rate of the supplied air and the horizontal axis being (3) the amount of solid fuel supplied, as shown in the upper part of Figure 3(b).

[0032] For example, the index calculation unit 22 calculates an index as a third index that shows the correlation between (1) the flow rate of supplied air and (4) the differential pressure. This third index is represented by a distribution diagram with the vertical axis being (1) the flow rate of supplied air and the horizontal axis being (4) the differential pressure, as shown in the upper part of Figure 4(a). For example, the index calculation unit 22 calculates an index as a fourth index that shows the correlation between (3) the amount of solid fuel supplied and (5) the current value. This fourth index is represented by a distribution diagram with the vertical axis being (5) the current value and the horizontal axis being (3) the amount of solid fuel supplied, as shown in the upper part of Figure 4(b).

[0033] Here, we will explain the state of each part of the crusher 10 shown in Figure 2 when an abnormality occurs. The abnormality here is the accumulation of solid fuel inside the crusher 10. First, when an excessive amount of solid fuel begins to accumulate inside the crusher 10, the flow rate of the discharged air sent out from the casing 100 of the crusher 10 via the powder discharge pipe L2 decreases. As a result, the pressure difference between the pressure of the supply air supplied to the crusher 10 and the pressure of the discharged air sent out from the crusher 10 increases.

[0034] When the differential pressure increases, the crushing ECU 105 of the crusher 10 increases the opening of the flow damper D to increase the flow rate of the discharged air. Also, the crushing ECU 105 of the crusher 10 increases the rotation speed of the rotary table 101 to crush the stagnant solid fuel more quickly. In other words, the crushing ECU 105 increases the current value of the power supplied to the electric motor 103. Furthermore, if solid fuel remains stagnant, the flow rate of the supply air supplied into the housing 100 of the crusher 10 via the air supply pipe L1 decreases. In short, if an abnormality occurs in the crusher 10, changes will occur in the first to fourth indicators described above.

[0035] As shown in Figure 1, the abnormality determination unit 23 determines whether or not there is an abnormality in the crusher 10 based on the operating data acquired by the data acquisition unit 21. In this embodiment, the abnormality determination unit 23 determines whether or not there is an abnormality in the crusher 10 based on an index that shows the correlation between the operating data. Two specific examples of the abnormality determination process in the abnormality determination unit 23 will be described below.

[0036] (Example 1) First, let's describe the first example of the process for determining whether or not there is an abnormality. The abnormality determination unit 23 determines whether or not each of the multiple indicators calculated by the indicator calculation unit 22 falls outside a predetermined indicator range. This indicator range is predetermined for each indicator. This indicator range is the range that the indicator can take when the operating state of the crusher 10 is in a normal state.

[0037] For example, for the first indicator shown in the upper part of Figure 3(a), an indicator range H1 is predetermined. This indicator range H1 is the range that the first indicator can take when the crusher 10 is operating in a normal state. Similarly, for the second indicator shown in the upper part of Figure 3(b), an indicator range H2 is predetermined. For the third indicator shown in the upper part of Figure 4(a), an indicator range H3 is predetermined. For the fourth indicator shown in the upper part of Figure 4(b), an indicator range H4 is predetermined. In these figures, white circles indicate when the indicator value is within the predetermined indicator range (i.e., in a normal state), and black circles indicate when the indicator value is outside the predetermined indicator range (i.e., in an abnormal state).

[0038] The abnormality detection unit 23 determines that there is an abnormality in the crusher 10 if the number of indicators that fall outside the indicator range among multiple indicators is equal to or greater than a predetermined standard number. The predetermined standard number is set to a value less than or equal to the number of indicators calculated by the indicator calculation unit 22. Furthermore, the predetermined standard number may be changed based on the state of the solid fuel supplied to the crusher 10 and the external environment, etc. For example, the abnormality detection unit 23 can determine that there is an abnormality in the crusher 10 if the number of indicators that fall outside the indicator range among multiple indicators becomes equal to or greater than the standard number within a predetermined period.

[0039] (Example 2) Next, a second example of the process for determining whether or not there is an abnormality will be described. The abnormality determination unit 23 calculates the amount of deviation between the indicator and a predetermined indicator reference value for each of the multiple indicators. The amount of deviation between the indicator reference value and the indicator is the difference between the indicator reference value and each value indicated by the indicator. The amount of deviation is calculated for each value indicated by the indicator. The indicator reference value is predetermined for each indicator. As an example, the median value of the indicator range that the indicator can take when the operating state of the crusher 10 is in a normal state is set as the indicator reference value. However, it is not limited to this, and the indicator reference value only needs to be set within the range of the indicator that the indicator can take when the operating state of the crusher 10 is in a normal state.

[0040] For example, a predetermined index reference value K1 is set for the distribution of the first index shown in the upper part of Figure 3(a). This index reference value K1 is set within the index range H1 that the first index can take when the operating state of the crusher 10 is normal. The lower part of Figure 3(a) shows time-series data of the deviation amount, with the vertical axis representing the deviation amount between the index reference value K1 and the first index and the horizontal axis representing time. In the time-series data in the lower part of Figure 3(a), the "lower limit" of the deviation amount corresponds to the lower limit of the index range H1 shown in the upper part of Figure 3(a), and the "upper limit" of the deviation amount corresponds to the upper limit of the index range H1 shown in the upper part of Figure 3(a).

[0041] Similarly, a baseline value K2 is predetermined for the distribution of the second indicator shown in the upper part of Figure 3(b). The lower part of Figure 3(b) shows the time series data of the deviation between the baseline value K2 and the second indicator. A baseline value K3 is predetermined for the distribution of the third indicator shown in the upper part of Figure 4(a). The lower part of Figure 4(a) shows the time series data of the deviation between the baseline value K3 and the third indicator. A baseline value K4 is predetermined for the distribution of the fourth indicator shown in the upper part of Figure 4(b). The lower part of Figure 4(b) shows the time series data of the deviation between the baseline value K4 and the fourth indicator.

[0042] The anomaly detection unit 23 then calculates the degree of anomaly for each indicator based on the calculated amount of deviation. The anomaly detection unit 23 increases the degree of anomaly if the amount of deviation is large. The anomaly detection unit 23 may use different increments or decrements for each indicator as the increment or decrement for the degree of anomaly per unit amount of deviation.

[0043] The abnormality determination unit 23 then calculates the sum of the multiple abnormality scores calculated for each indicator. The abnormality determination unit 23 determines that there is an abnormality in the crusher 10 if the calculated sum exceeds a predetermined standard sum.

[0044] Furthermore, when the abnormality determination unit 23 calculates the total abnormality score, it may weight the abnormality score according to each indicator and then calculate the total abnormality score. In addition, the predetermined standard total value may be changed based on the state of the solid fuel supplied to the crusher 10, the external environment, etc.

[0045] If the abnormality detection unit 23 determines that there is an abnormality in the crusher 10, the display control unit 24 displays a message to the supervisor or other personnel via the display unit 30 indicating that there is an abnormality in the crusher 10. This allows the supervisor or other personnel to recognize that an abnormality has occurred in the crusher 10 without directly inspecting the inside of the crusher 10. This enables the supervisor or other personnel to perform maintenance work or other actions to resolve the abnormality in the crusher 10.

[0046] As described above, the abnormality detection unit 23 determines whether or not there is an abnormality based on the operating data of the crusher 10, which is likely to show changes in the state of the crusher 10. As a result, the abnormality detection device 2 can determine whether or not there is an abnormality in the crusher 10 with greater accuracy by using the operating data of the crusher 10. In this way, since the abnormality detection device 2 can determine whether or not there is an abnormality, the work of monitoring personnel at the thermal power plant 1 to determine whether or not there is an abnormality in the crusher 10 based on various data becomes unnecessary.

[0047] The abnormality detection device 2 includes an index calculation unit 22 that calculates an index showing the correlation between two different sets of operating data. As a first example of the abnormality detection process, the abnormality detection unit 23 of the abnormality detection device 2 determines whether or not there is an abnormality based on whether or not the index falls outside a predetermined index range. In this case, the abnormality detection device 2 can accurately determine whether or not there is an abnormality in the crusher 10 by using an index showing the correlation between two sets of operating data.

[0048] As a first example of the process for determining whether or not there is an abnormality, the abnormality determination unit 23 of the abnormality determination device 2 determines that there is an abnormality if the number of indicators that fall outside a predetermined indicator range is equal to or greater than a predetermined standard number. In this case, the abnormality determination device 2 can determine whether or not there is an abnormality in the crusher 10 with even greater accuracy by using multiple indicators that show the correlation between operating data.

[0049] As a second example of the process for determining whether or not there is an abnormality, the abnormality determination unit 23 of the abnormality determination device 2 calculates the degree of abnormality for each of the multiple indicators. The abnormality determination unit 23 determines that there is an abnormality in the crusher 10 if the sum of the calculated degrees of abnormality exceeds a predetermined standard sum. In this case, the abnormality determination device 2 can determine whether or not there is an abnormality in the crusher 10 with even greater accuracy by using the sum of the degrees of abnormality of the multiple indicators.

[0050] The data acquisition unit 21 acquires the following operating data for the crusher 10: (1) the flow rate of the supplied air, (2) the opening degree of the flow damper, (3) the amount of solid fuel supplied, (4) the differential pressure, and (5) the current value. The abnormality determination unit 23 uses an index calculated based on this operating data to determine whether or not there is an abnormality in the crusher 10. In this way, the abnormality determination device 2 can determine whether or not there is an abnormality in the crusher 10 based on this operating data related to the crusher 10.

[0051] While embodiments of the present disclosure have been described above, the present disclosure is not limited to the above embodiments. For example, the data acquisition unit 21 is not limited to acquiring five operational data: (1) the flow rate of the supplied air, (2) the opening degree of the flow damper, (3) the amount of solid fuel supplied, (4) the differential pressure, and (5) the current value. The data acquisition unit 21 may acquire a number of operational data other than five. Furthermore, the data acquisition unit 21 may acquire data other than the above-mentioned (1) the flow rate of the supplied air to (5) the current value as operational data for the crusher 10, as long as it relates to the operation of the crusher 10.

[0052] For example, the indicator calculation unit 22 is not limited to calculating the four indicators, the first to fourth indicators. The indicator calculation unit 22 may calculate a number of indicators other than four. Furthermore, the indicator calculation unit 22 may calculate indicators based on combinations of operating data other than those of the first to fourth indicators described above. The abnormality determination unit 23 is not limited to determining the presence or absence of an abnormality using the four indicators. The abnormality determination unit 23 may determine the presence or absence of an abnormality based on a number of indicators other than four, for example, by using only one indicator.

[0053] The crusher 10 may have a configuration other than that described with reference to Figure 2. Even in this case, the abnormality detection device 2 can determine whether or not there is an abnormality based on the operating data of the crusher.

[0054] [Note] This disclosure enables the determination of whether or not there are abnormalities in the crushers at thermal power plants, thereby promoting the efficient operation of thermal power plants. Therefore, this disclosure also contributes to the following Sustainable Development Goals (SDGs) led by the United Nations. Goal 7: "Ensure access to affordable, reliable, sustainable, and modern energy for all." [Explanation of symbols]

[0055] 1 Thermal power plant 2 Abnormality determination device 10. Crusher 11 Boiler 21 Data Acquisition Unit 22 Indicator calculation section 23 Abnormality determination section 101 Rotating table (movable part) 103 Electric motor D Flow Damper

Claims

1. An abnormality determination device for determining whether there is an abnormality in a crusher that crushes solid fuel and supplies it to a boiler of a thermal power plant, A data acquisition unit that acquires operating data indicating the operating status of the aforementioned crusher, An abnormality determination unit determines whether or not there is an abnormality in the crusher based on the acquired operating data, The system includes an index calculation unit that calculates multiple indices showing the correlation between two different sets of operating data acquired by the data acquisition unit, The multiple indicators calculated are such that the combination of the two operating data used to calculate the indicators is different from each other. The abnormality determination unit, For each of the multiple indicators, the degree of abnormality is calculated based on the amount of deviation between the indicator and the predetermined indicator standard value for each indicator. An abnormality detection device that determines that there is an abnormality in the crusher when the sum of the multiple abnormality scores calculated for each of the aforementioned indicators exceeds a predetermined standard sum.

2. The aforementioned operating data is The flow rate of supply air supplied into the crusher to send the powdered solid fuel, which has been crushed in the crusher, out of the crusher by airflow, The flow damper opening degree of the flow damper that controls the flow rate of the supply air, The amount of solid fuel supplied to the crusher, The pressure difference between the pressure of the supply air and the pressure of the discharge air sent from the pulverizer to deliver the powder by airflow, and An abnormality determination device according to claim 1, wherein the abnormality determination device is at least one of the current values ​​of the power supplied to an electric motor that drives a movable part for crushing the solid fuel, which is provided in the crusher.

3. An abnormality detection device for determining whether there is an abnormality in a crusher that crushes solid fuel and supplies it to a boiler of a thermal power plant, A data acquisition unit that acquires operating data indicating the operating status of the aforementioned crusher, An abnormality determination unit determines whether or not there is an abnormality in the crusher based on the acquired operating data, The system includes an index calculation unit that calculates an index showing the correlation between two different sets of operating data acquired by the data acquisition unit, The two operating data mentioned above are the flow rate of supply air supplied into the pulverizer to expel the powdered solid fuel, which has been pulverized within the pulverizer, out of the pulverizer by airflow, and the opening degree of the flow damper that controls the flow rate of the supply air. The abnormality determination unit is an abnormality determination device that determines that there is an abnormality in the crusher when the calculated index falls outside a predetermined index range.

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