Device abnormality diagnosis method and device abnormality diagnosis device

A device abnormality diagnosis method for a device to be diagnosed constituting a plant includes: obtaining time-series data of a plurality of state amounts of the plant which are correlated to an abnormality of the device to be diagnosed; a step of obtaining abnormality diagnosis data on the plurality of state amounts by performing pre-processing on at least one state amount of the plurality of state amounts to exclude, from the time-series data on the state amounts, data of the at least one state amount obtained in an exclusion period which is at least a part of a transient state period during which the device to be diagnosed is affected by a state change of another constituent device of the plant; and a step of performing abnormality diagnosis on the device to be diagnosed on the basis of the abnormality diagnosis data of the plurality of state amounts.

TECHNICAL FIELD

The present disclosure relates to an abnormality diagnosis method and an abnormality diagnosis device for diagnosing an abnormality of a device on the basis of information collected from the device in operation.

BACKGROUND ART

Generally, to monitor whether at least one device constituting a plant is normally operated, state amounts such as the temperature and the pressure of a device are obtained from the device and monitored. In a specific method, state amounts of a plurality of monitoring items, which are monitoring targets of the device, are measured at a predetermined time interval, and a Mahalanobis distance is calculated by focusing on the correlation between the state amounts of the plurality of monitoring items. Then, it is determined whether there is a sign of abnormality in the plant, on the basis of the Mahalanobis distance.

In the abnormality diagnosis method using the Mahalanobis distance, to improve the abnormal detection accuracy, the Mahalanobis distance is calculated by excluding state amounts which are substantially unnecessary in abnormal diagnosis and state amounts that do not contribute to improvement of the abnormality diagnosis accuracy. Patent Document 1 discloses an abnormality diagnosis method for a device using the Mahalanobis distance calculated on the basis of the above idea. In the abnormality diagnosis method disclosed in Patent Document 1, a frequency distribution of data is obtained for each state amount from data collected in a time-series manner from the device for each of the plurality of state amounts, and abnormality diagnosis is performed by using the Mahalanobis distance calculated by excluding a state amount whose frequency distribution of data does not follow the normal distribution.

CITATION LIST

Patent Literature

SUMMARY

Problems to be Solved

However, if the Mahalanobis distance is calculated by fully excluding the entire data collected from a part of state amounts to be excluded as in Patent Document 1, it becomes difficult to find a particular abnormality whose diagnosis requires the excluded state amount.

Furthermore, as another problem, in a case where a rapid change of the state amount of another device connected to the preceding stage of the device to be diagnosed in the plant propagates as a non-stationary change of the state amount of the device to be diagnosed, the following problem arises. That is, the non-stationary state change of the device to be diagnosed due to influence of the state change of the other device is added to the variation characteristics of the plurality of state amounts collected from the device to be diagnosed as disturbance. As a result, due to the above disturbance added to the variation characteristic of the state amounts of the device to be diagnosed, it may become difficult to appropriately find a sign of occurrence of an abnormality of the device from the variation characteristics of the state amounts of the device to be diagnosed.

In view of the above problems, an object of some embodiments of the present invention is to provide an abnormality diagnosis method capable of accurately detecting a sign of occurrence of an abnormality in a device to be diagnosed, while taking account of data collected from the device to be diagnosed for all of the state amounts, without being affected by the non-stationary change caused by propagation of a state change of another device to the device to be diagnosed.

Solution to the Problems

(1) According to some embodiment of the present invention, a device abnormality diagnosis method for a device to be diagnosed constituting a plant includes: a step of obtaining time-series data of a plurality of state amounts of the plant which are correlated to an abnormality of the device to be diagnosed; a step of obtaining abnormality diagnosis data on the plurality of state amounts by performing pre-processing on at least one state amount of the plurality of state amounts to exclude, from the time-series data on the state amounts, data of the at least one state amount obtained in an exclusion period which is at least a part of a transient state period during which the device to be diagnosed is affected by a state change of another constituent device of the plant; and a step of performing abnormality diagnosis on the device to be diagnosed on the basis of the abnormality diagnosis data of the plurality of state amounts.

In the above method (1), the purpose of setting the exclusion period and excluding the data obtained in the exclusion period related to a part of the state amounts from abnormality diagnosis is as follows. In the exclusion period which is at least a part of the transient state period in which the device to be diagnosed is affected by the state change of the other constituent device, the non-stationary state change of the device to be diagnosed due to influence of the state change of the other device is added to the variation characteristics of the plurality of state amounts collected from the device to be diagnosed as disturbance. Thus, in the above method (1), from the abnormality diagnosis data for abnormality analysis, data obtained in the time range in which a part of the state amounts is affected by the state change of the other constituent device is excluded.

Thus, according to the above method (1), it is possible to detect a sign of occurrence of abnormality of the device to be diagnosed without fully excluding the entire data collected in a time-series manner from the device to be diagnosed for a part of the state amounts, and without being affected by propagation of a state change of the other constituent device.

(2) According to an illustrative embodiment, in the above method (1), the other constituent device is an abnormality prevention device disposed on the device to be diagnosed or a preceding device positioned in a preceding stage of the device to be diagnosed, and the transient state period is a period during which the device to be diagnosed is affected by operation of the abnormality prevention device.

In an example, the other constituent device which has an effect of causing a non-stationary change of the device to be diagnosed by affecting the device to be diagnosed may be the abnormality prevention device provided to prevent occurrence of an abnormality of the device to be diagnosed. Furthermore, if an abnormality prevention device is disposed on a preceding device disposed in the preceding stage (upstream side) of the device to be diagnosed, the influence of the state change that occurs in the preceding device due to operation of the abnormality prevention device may further propagate to a downstream device to be diagnosed, and cause a state change of the device to be diagnosed.

Thus, in the above method (2), from the abnormality diagnosis data for abnormality analysis, data obtained in a period in which the device to be diagnosed is affected by operation of the abnormality prevention device provided for the preceding device and/or the device to be diagnosed is excluded. In this way, according to the above method (2), it is possible to accurately detect a sign of occurrence of abnormality of the device to be diagnosed without being affected by propagation of the state change of the other constituent device.

(3) According to an illustrative embodiment, in the above method (1) or (2), the step of obtaining the abnormality diagnosis data includes: determining a length of the exclusion period on the basis of a response characteristic of the at least one state amount after start of the state change of the other constituent device; and excluding, from the time-series data, data of the at least one state amount obtained within the exclusion period set on the basis of the response characteristic.

In the above method (3), the exclusion period is set on the basis of the response characteristic shown in the temporal change of the at least one state amount after start of a state change of the other constituent device. Thus, according to the above method (3), in a case where the response characteristics indicate a high response, it is possible to set the exclusion period to be accordingly short. On the other hand, if the response characteristics indicate a slow response, it is possible to set the exclusion period to be accordingly long.

(4) In an illustrative embodiment, in the above method (3), when setting the length of the exclusion period on the basis of the response characteristic, the length of the exclusion period is determined by applying a time constant obtained from a temporal change of the at least one state amount obtained after start of the state change of the other constituent device to a pre-set correlation between the time constant, which indicates the response characteristic of the at least one state amount after start of the state change of the other constituent device, and the length of the exclusion period.

In the above method (4), in a case where the response characteristic of the at least one state amount after start of the state change of the other constituent device indicates a quick response, the time constant indicating the response characteristic should be small. In a case where the response characteristic indicates a low response, the time constant indicating the response characteristic should be large. Thus, in the above method (4), the correlation between the time constant indicating the response characteristic and the length of the exclusion period is set in advance, and the exclusion period corresponding to the magnitude of the time constant is set on the basis of the correlation. Thus, according to the above method (4), it is possible to set an appropriate length for the exclusion period in accordance with the magnitude of the time constant.

(5) According to an illustrative embodiment, in the above method (1) or (2), the step of obtaining the abnormality diagnosis data includes determining a length of the exclusion period so as to reduce a difference between a frequency distribution and a normal distribution related to the time-series data of the at least one state amount.

In the above method (1) or (2), the data in the exclusion period excluded from abnormality diagnosis is data obtained in a transient time range in which a part of the state amounts is affected by a state change of the other constituent device. Thus, even in a case where data collected in a period other than the exclusion period from the device in relation to the plurality of state amounts is distributed according to the normal distribution, the data of the state amount collected in the exclusion period may not necessarily be distributed according to the normal distribution.

Thus, in the above method (5), the length of the exclusion period is determined so as to reduce the difference between the frequency distribution and the normal distribution related to the data obtained in relation to the at least one state amount. Thus, according to the above method (5), it is possible to set the exclusion period as a transient time range in which the state amount is affected by a state change of the other constituent device (a period in which the frequency distribution is offset from the normal distribution), and the data obtained in this period is not used in the abnormality diagnosis, which makes it possible to perform abnormality diagnosis accurately.

(6) According to an illustrative embodiment, in the above method (5), the step of obtaining the abnormality diagnosis data includes determining the length of the exclusion period on the basis of an index indicating consistency between the frequency distribution and the normal distribution.

According to the above method (6), when setting the length of the exclusion period, an index of consistency between the frequency distribution and the normal distribution is taken into account, and thus it is possible to more accurately identify a transient time range during which the state amount is affected by a state change of the other constituent device, by using the index. Thus, it is possible to set the exclusion period more appropriately, and to improve the accuracy of abnormal diagnosis.

(7) According to an illustrative embodiment, in the above method (1) to (6), the step of performing abnormality diagnosis on the device includes: calculating a Mahalanobis distance of the abnormality diagnosis data with reference to a unit space including the plurality of state amounts at a normal time of the device; and determining that the device has an abnormality if the Mahalanobis distance is greater than the threshold.

In the above method (7), a Mahalanobis distance of the abnormality diagnosis data is obtained with reference to a unit time including a plurality of state amounts at the time when the device to be diagnosed is normal. Thus, according to the above method (7), it is possible to evaluate quantitatively the extent of deviation of the abnormality diagnosis data not affected by operation of the abnormality prevention device from the unit space representing the state group at the time when the device to be diagnosed is normal. As a result, according to the above method (7), it is possible to diagnose an abnormality of the device with a high accuracy on the basis of the abnormality diagnosis data not affected by a state change of the other constituent device.

(8) According to some embodiments of the present invention, an abnormality diagnosis device for a device to be diagnosed disposed in a plant includes: an input/output part configured to obtain time-series data of a plurality of state amounts of the plant which are correlated to an abnormality of the device to be diagnosed from a sensor of the plant, and output a result of abnormality diagnosis of the device to be diagnosed based on the time-series data; a diagnosis data acquisition part configured to obtain abnormality diagnosis data on the plurality of state amounts by performing pre-processing on at least one state amount of the plurality of state amounts to exclude, from the time-series data on the state amounts, data of the at least one state amount obtained in an exclusion period which is at least a part of a transient state period during which a state change is caused in the device to be diagnosed by another constituent device which operates in the plant; and an abnormality diagnosis part configured to perform abnormality diagnosis on the device to be diagnosed on the basis of the abnormality diagnosis data of the plurality of state amounts.

In the above configuration (8), the purpose of setting the exclusion period and excluding the data obtained in the exclusion period related to a part of the state amounts from abnormality diagnosis is as follows. In the exclusion period which is at least a part of the transient state period in which the device to be diagnosed is affected by the state change of the other constituent device, the non-stationary state change of the device to be diagnosed due to influence of the state change of the other device is added to the variation characteristics of the plurality of state amounts collected from the device to be diagnosed as disturbance. Thus, in the above configuration (8), from the abnormality diagnosis data for abnormality analysis, data obtained in the time range in which a part of the state amounts is affected by the state change of the other constituent device is excluded.

Accordingly, with the above configuration (8), it is possible to detect a sign of occurrence of abnormality of the device to be diagnosed without fully excluding the entire data collected in a time-series manner from the device to be diagnosed for a part of the state amounts, and without being affected by propagation of the state change of the other constituent device.

Advantageous Effects

According to some embodiment of the present invention, it is possible to accurately detect a sign of occurrence of an abnormality in a device to be diagnosed, while taking account of data collected from the device to be diagnosed for all of the state amounts, without being affected by the non-stationary change caused by propagation of the state change of another device to the device to be diagnosed.

DETAILED DESCRIPTION

For instance, an expression of an equal state such as “same” “equal” and “uniform” shall not be construed as indicating only the state in which the feature is strictly equal, but also includes a state in which there is a tolerance or a difference that can still achieve the same function. On the other hand, an expression such as “comprise”, “include”, “have”, “contain” and “constitute” are not intended to be exclusive of other components.

Hereinafter, firstly, from among a plurality of constituent devices of a plant, a device to be diagnosed by abnormality diagnosis and an abnormality diagnosis device for performing abnormality diagnosis on the device to be diagnosed will be described with reference toFIGS. 1 to 3. Next, with reference toFIGS. 3 to 9, a processing method to be performed by the abnormality diagnosis device in the plant for abnormality diagnosis of the device to be diagnosed will be described. Finally, according to some embodiments of the present invention, with reference toFIG. 10, an IGCC plant for performing the device abnormality diagnosis with the abnormality diagnosis device will be described.

First, with reference toFIG. 1, an articulated device group32bwill be described, which is a part of a plant and includes at least one device1to be diagnosed by abnormality diagnosis. As shown inFIG. 1, the articulated device group32bincludes a plurality of devices1C to1X and a plurality of devices5C to5X articulated therein. In the articulated device group32bshown inFIG. 1, the devices1to be diagnosed by abnormality diagnosis are the plurality of devices1C to1X, and the plurality of devices5C to5X are each an abnormality prevention device for preventing abnormality of corresponding one of the devices1C to1X.

For instance, in the example shown inFIG. 1, the device5C, the device5D, the device5E, . . . , are disposed immediately above the device1C, the device1D, the device1E respectively. Further, the device5C, the device5D, and the device5E, . . . , are abnormality prevention devices provided to prevent the abnormality of the device1C, the device1D, the device1E, . . . , respectively.

From among the plurality of devices1C to1X shown inFIG. 1, the output of a preceding device1′ disposed upstream is transmitted to a subsequent device1″ disposed downstream. For instance, provided that the device1C is the preceding device1′ disposed upstream and the device1D is the subsequent device1″ disposed downstream, the output of the device1C is transmitted to the device1D. Further, specific examples of output from the preceding device1′ may include displacements of a fluid generated by the preceding device1′ and the physical amounts outputted from the preceding device1′ (fluid pressure, gas pressure, temperature, voltage, mechanical action). Furthermore, the output from the preceding device1′ may be a state change at the preceding device1′ propagating to the output side with a response delay (e.g. wave propagation or energy propagation). In the following description, one of the plurality of devices1C to1X constituting the articulated device group32binFIG. 1is regarded as the device1to be diagnosed by abnormality diagnosis.

FIG. 2shows a configuration of the device1to be diagnosed by abnormality diagnosis and the abnormality diagnosis device10for diagnosing an abnormality of the device1to be diagnosed. In an example, the device1to be diagnosed may be one of the plurality of devices1C to1X constituting the articulated device group32binFIG. 1. In this case, the abnormality prevention device5shown inFIG. 2may be an abnormality prevention device5disposed immediately above the device1to be diagnosed, in the articulated device group32binFIG. 1. For instance, in the articulated device group32bshown inFIG. 1, provided that the device1E is the device1to be diagnosed by abnormality diagnosis, the abnormality prevention device5for preventing abnormality of the device1to be diagnosed may be the abnormality prevention device5E disposed immediately above the device1E. Meanwhile, the embodiment described below with reference toFIGS. 2 to 8is for solving the problem due to provision of the abnormality prevention device5for abnormality prevention of the device1to be diagnosed.

Further, in the embodiment shown inFIG. 2, the abnormality diagnosis device10diagnoses presence or absence of an abnormality of each of at least one device1to be diagnosed, while monitoring the operation state of the at least one device1to be diagnosed constituting the plant. The abnormality diagnosis device10is, for instance, a computer including an input/output part (I/O)11, a processing part12, and a memory part13. In an illustrative embodiment, the abnormality diagnosis device10may include a computer, or combination of a central processing unit (CPU) and a memory.

The processing part12receives data of a plurality of state amounts sv (k) (1≤k≤K) including the state amount of the device1to be diagnosed, from a plurality of sensors which are various state amount detection units mounted to at least one device constituting the plant, via the input/output part11. The various state amount detection units obtain data of the corresponding state amount sv (k) (1≤k≤K) at a predetermined time interval from start, and input the same to the processing part12via the input/output part11. The data group showing the plurality of state amounts sv (k) (1≤k≤K) is transmitted to the processing part12of the abnormality diagnosis device10in the form of electrical signals. The processing part12includes, for instance, a CPU, and reads in a sequence of instructions called a program (computer program) on the memory part13, interprets the same, and moves and processes data according to the sequence of instructions.

A terminal device14is connected to the input/output part11of the abnormality diagnosis device10. The terminal device14includes a display14D, and an input unit14C for inputting instructions for the abnormality diagnosis device10. The memory part13of the abnormality diagnosis device10stores a computer program, data, and the like for implementing the monitoring/operating method for the device1to be diagnosed shown inFIG. 2. The processing part12uses the computer program and data to implement the monitoring/operating method for the device1to be diagnosed shown inFIG. 2, or to control operation of the device1to be diagnosed.

FIG. 3is a detailed interior configuration of the processing part12of the abnormality diagnosis device10shown inFIG. 2. With reference toFIG. 3, the processing part12includes a transient state detection part120, a diagnosis data acquisition part121, and an abnormality diagnosis part122. Furthermore, the diagnosis data acquisition part121includes an exclusion period setting part121aand a diagnosis data generation part121b. The input/output part11of the abnormality diagnosis device10obtains time-series data of a plurality of state amounts sv (k) (1≤k≤K) correlated to abnormality of the device1to be diagnosed, from sensors disposed on a plurality of devices disposed inside the plant. As the input/output part11obtains time-series data of the plurality of state amounts sv (k) (1≤k≤K), the transient state detection part120, the diagnosis data acquisition part121, and the abnormality diagnosis part122perform the following operation.

First, the transient state detection part120performs the following process on at least one state amount sv (ka) (ka=k1, k2, . . . ) of the plurality of state amounts sv (k) (1≤k≤K). That is, the transient state detection part120detects the start of the transient state period τt during which a state change of the state amount sv (ka) occurs in the device1to be diagnosed due to influence from another device operating inside the plant. Further, the other device which causes a state change of the device1to be diagnosed by affecting the device1to be diagnosed in the plant1may be the abnormality prevention device5provided for abnormality prevention of the device1to be diagnosed. Further, the other device may be an abnormality prevention device5′ provided to prevent abnormality of the preceding device1′ positioned on the preceding stage (upstream side) of the device1to be diagnosed. In this case, the transient state period τt detected by the transient state detection part120is a period during which the device1to be diagnosed is affected by operation of the abnormality prevention device5or the abnormality prevention device5′.

For instance, in the articulated device group32bshown inFIG. 1, provided that the device1E is the device1to be diagnosed, the abnormality prevention device5for preventing abnormality of the device1to be diagnosed is the abnormality prevention device5E disposed immediately above the device1E. Furthermore, in the articulated device group32bshown inFIG. 1, provided that the device1E is the device1to be diagnosed, the preceding device1′ positioned in the preceding stage (upstream side) of the device1to be diagnosed is the device1D and the device1C. In this case, the abnormality prevention device5′ provided to prevent occurrence of an abnormality of the preceding device1D and the preceding device1C is the abnormality prevention device5D and the abnormality prevention device5C disposed immediately above the device1D and the device1C. Thus, in the articulated device group32bshown inFIG. 1, the transient state period τt detected by the transient state detection part120is a period during which the device1E to be diagnosed is affected by operation of the abnormality prevention device5E,5D, or5C. Furthermore, a specific example of detection of start of the transient state period τt by the transient state detection part120will be described below.

Next, the diagnosis data acquisition part121performs the following pre-processing on the state amount sv (ka) (ka=k1, k2, . . . ). The pre-processing is a processing of excluding the data de (ka) of at least one state amount sv (ka) obtained in an exclusion period τe which is at least a part of the transient state period τt detected by the transient state detection part120, from the time-series data ds (ka) of the state amount sv (ka). Further, the diagnosis data acquisition part121generates the time-series data obtained by excluding the data de (ka) obtained in the exclusion period τe from the time-series data ds (ka) on the state amount sv (ka) as abnormality diagnosis data dd (k) of the plurality of state amounts sv (k) (1≤k≤K).

At this time, the exclusion period setting part121aof the diagnosis data acquisition part121performs a process of appropriately setting the exclusion period τe by the following method, with reference toFIGS. 5 to 8. Further, the diagnosis data generation part121bof the diagnosis data acquisition part121performs a process of generating abnormality diagnosis data dd (k) by excluding the data de (ka) obtained in the exclusion period τe set by the exclusion period setting part121afrom the time-series data ds (ka) of the state amount sv (ka).

Furthermore, when receiving the abnormality diagnosis data dd (k) of the plurality of state amounts sv (k) (1≤k≤K) from the diagnosis data acquisition part121, the abnormality diagnosis part122performs abnormality diagnosis of the device1to be diagnosed, on the basis of the abnormality diagnosis data dd (k) of the plurality of state amounts sv (k) (1≤k≤K). The result of abnormality diagnosis of the device1to be diagnosed by the abnormality diagnosis part122is outputted to the terminal device14via the input/output part11.

Hereinafter, in the above configuration of the abnormality diagnosis device10shown inFIGS. 2 and 3, the purpose of setting the exclusion period τe and excluding the data de (ka) obtained in the exclusion period τe related to a part of the state amounts sv (ka) from abnormality diagnosis will be described. First, to clarify the problem of not excluding the data de (ka) obtained in the exclusion period τe from abnormality diagnosis, a comparative example will be described with reference toFIG. 4, in comparison to the embodiment shown inFIGS. 2 and 3. Further, in the following description, the abnormality prevention device provided to prevent abnormality of the preceding device1′ positioned upstream of the device1to be diagnosed will be referred to as the abnormality prevention device5′.FIG. 4is a diagram showing the temporal change of the Mahalanobis distance in a case where a MT method based on the Mahalanobis distance is used as an abnormality diagnosis method. In the example shown inFIG. 4, the Mahalanobis distance is calculated without excluding the data de (ka) obtained in the exclusion period τe set by the exclusion period setting part121a, from the data ds (k) of the state amounts sv (k) (1≤k≤K) of the device1to be diagnosed.

InFIG. 4, at time t1, the Mahalanobis distance obtained from actual data of the state amount sv (k) (1≤k≤K) of the device1to be diagnosed increases extremely, because the operation state of the device1to be diagnosed is abnormal, and the state amount sv (k) of the device1to be diagnosed is deviated from the unit space considerably. Further, at time t2, even though the operation state of the device1to be diagnosed is normal, the Mahalanobis distance increases extremely due to the non-stationary state change added as turbulence to the state amount sv (k) of the device1to be diagnosed, and the state amount sv (k) of the device1to be diagnosed is deviated from the unit space considerably. Thus, unless excluding the influence of the non-stationary state change added as turbulence to the state amount sv (k) of the device1to be diagnosed, an abnormality may be wrongly detected in the abnormality diagnosis of the device1to be diagnosed. The reason of the Mahalanobis distance increasing extremely even though the operation state of the device1to be diagnosed is normal at time t2inFIG. 4can be described as follows.

In the MT method, accurate abnormality diagnosis can be performed on the basis of the Mahalanobis distance only if the actual data of the state amount sv (k) (1≤k≤K) of the device1to be diagnosed by abnormality diagnosis is distributed following the normal distribution. That is, in the MT method, accurate abnormality diagnosis can be performed only if the state amount sv (k) (1≤k≤K) of the device1to be diagnosed can be approximated appropriately by a random variable which randomly changes in accordance with a stationary probability distribution.

However, if influence due to operation of the abnormality prevention device5or5′ of the device1to be diagnosed is added to a part of the state amounts sv (ka) of a part of the device1to be diagnosed as turbulence, a non-stationary state change is applied to a part of the state amounts sv (ka). As result, in the transient state period τt until the turbulence applied to the state amount sv (ka) due to operation of the abnormality prevention device5or5′ attenuates, the above non-stationary state change raises the following inconvenience. That is, for the state amount sv (k) (1≤k≤K) of the device1to be diagnosed is distributed according to a non-normal distribution, it is no longer possible to approximate with a random variable which randomly changes in accordance with a stationary probability distribution. Thus, even if the operation state of the device1to be diagnosed is not abnormal, the state amount sv (k) of the device1to be diagnosed observed in the transient state period τt may deviate greatly from the unit space.

Also in the case of the device1to be diagnosed shown inFIG. 1, in the exclusion period τe including the time immediately after operation of the abnormality prevention device5or5′, the non-stationary state change due to operation of the abnormality prevention device5or5′ is added to the variation characteristics of the plurality of state amounts sv (k) (1≤k≤K) collected from the device1to be diagnosed as turbulence. Thus, with the above configuration, from the abnormality diagnosis data dd (k) (1≤k≤K) for abnormality analysis, data obtained in the time range τe in which a part of the state amounts sv (ka) is affected by influence of turbulence due to operation of the abnormality prevention device5or5′ is excluded.

Accordingly, with the above configuration, it is possible to detect a sign of occurrence of abnormality of the device1to be diagnosed without fully excluding the entire data collected in a time-series manner from the device for a part of the state amount sv (ka), and without being affected by turbulence due to operation of the abnormality prevention device5or5′.

In an illustrative embodiment, the articulated device group32bincluding the device1to be diagnosed may be a gas cooler shown inFIG. 1. In the example shown inFIG. 1, the gas cooler32bforms a coal gasification furnace32of the IGCC plant with a coal gasification part32a. The coal gasification part32acombusts a carbon including fuel which includes powdered coal supplied from a powdered coal supplying facility to produce fuel gas, and supplies the fuel gas to the upper section of the gas cooler32b. The fuel gas supplied from the coal gasification part32aflows from the top section to the bottom section inside the gas cooler32b, and the gas cooler32bproduces steam through heat exchange between fuel gas (carbon containing fuel gas) and water flowing inside the gas cooler32b. Accordingly, the gas cooler32bcools fuel gas produced in the gasification furnace32, and supplies the produced steam to the steam turbine to drive the steam turbine.

Furthermore, each of the devices1C to1X of the gas cooler32bshown inFIG. 1may be the carbon containing fuel heat exchanger1shown inFIG. 2. The carbon containing fuel heat exchanger1is a heat exchanger that performs heat exchange between a primary side fuel and a secondary side heat exchanging medium (e.g. water) when the primary side is supplied with a coal containing fuel (e.g. coal containing fuel gas produced by the coal gasification furnace32from a coal containing fuel). That is, the gas cooler32bshown inFIG. 1exchanges heat between fuel gas from the coal gasification part32aand water being a heat exchanging medium with a plurality of coal containing fuel heat exchangers1C to1X provided therein, and supplies the produced water vapor to the steam turbine. Furthermore, soot removing devices5C to5X are disposed directly above corresponding coal containing fuel heat exchangers1C to1X disposed inside the gas cooler32b. Each of the soot removing devices5C to5X is an abnormality prevention device5for preventing abnormality of each of the coal containing fuel heat exchangers1C to1X. As described below in detail, each of the soot removing devices5C to5X intermittently performs operation for preventing a decrease with time in the heat exchange efficiency of the coal containing fuel heat exchangers1C to1X provided immediately below the respective soot removing devices5C to5X. Furthermore, as shown inFIG. 2, the coal containing fuel heat exchanger1to be diagnosed by abnormality diagnosis includes a soot removing device5as an abnormality prevention device5, as well as a heat exchanger2, a fuel flow passage3, and a heat-transfer tube4.

Hereinafter, with reference toFIG. 2, the internal configuration of each coal containing fuel heat exchanger1will be described.

In the carbon containing fuel heat exchanger1shown inFIG. 2, the coal containing fuel is supplied into the heat exchanger2via the fuel flow passage3. As an example of coal containing fuel, a coal containing fuel gas and a powdered fuel can be named. The heat-transfer tube4is disposed through the inside of the heat exchanger2, thereby forming a heat transfer surface6. On the heat transfer surface6, heat is exchanged between a fuel flowing from the fuel flow passage3to the heat exchanger2, and a heat exchange medium flowing through the heat-transfer tube4. An example of the heat exchange medium includes water, for instance. Furthermore, the soot removing device5removes soot due to carbon in the fuel adhering to the heat transfer surface6formed by the heat-transfer tube4. The soot removing device5may be an oscillation type soot removing device which applies oscillation to the heat transfer surface6, a hard ball dropping soot removing device which drops a hard ball on the heat transfer surface6, or an injection type soot removing device (e.g. soot blower) which injects compressed gas (nitrogen, steam) to the heat transfer surface6, for instance.

Further, in some embodiments described below, the following assumption is made to simplify the description. First, the device1to be diagnosed by abnormality diagnosis is the carbon containing fuel heat exchanger1shown inFIG. 2, which includes the soot removing device5as the abnormality prevention device5for preventing abnormality of the carbon containing fuel heat exchanger1. Further, in some embodiments described below, provided that the carbon containing fuel heat exchanger1E shown inFIG. 1is the device1to be diagnosed, only the influence of operation of the soot removing device5E disposed directly above the carbon containing fuel heat exchanger1E on the state amount of the carbon containing fuel heat exchanger1is taken into account. That is, as influence from another device which causes a state change of the carbon containing fuel heat exchanger1, only the influence of operation of the soot removing device5, which directly removes soot from the carbon containing fuel heat exchanger1, on the state amount of the carbon containing fuel heat exchanger1is taken into account.

The state amount sv (k) (1≤k≤K) for monitoring the carbon containing fuel heat exchanger1may include, for instance, the temperatures of a plurality of positions in the flow direction G on the primary side of the heat exchanger2(e.g. inlet temperature and outlet temperature of the heat exchanger2), the differential pressure of the inlet and outlet in the flow direction G of the primary side, the flow rate of the primary side, the plurality of temperatures in the flow direction W on the secondary side, and the flow rate of the heat exchange medium in the heat-transfer tube4. The primary side of the heat exchanger2refers to the high temperature side. That is, in the embodiment shown inFIG. 1, the primary side of the heat exchanger2refers to the side where the fuel is flowing. Furthermore, the secondary side of the heat exchanger2refers to the low temperature side. That is, the secondary side of the heat exchanger2refers to the side where the heat exchange medium flows. Further, the state amounts sv (k) (1≤k≤K) are shown as data to be monitored.

Thus, in this embodiment, as a state amount that causes a state change in response to influence of operation of the soot removing device5, the temperatures svt (k) (kt=kt1, kt2, kt3, . . . ) of a plurality of locations along the flow direction G of the primary side of the heat exchanger2may be used. The reason is as follows. In the carbon containing fuel heat exchanger1shown inFIG. 1, when soot accumulates on the heat transfer surface6of the heat exchanger2, the efficiency of heat exchange on the heat transfer surface6deteriorates. Thus, the temperature of fuel is less likely to decrease at the primary side of the heat exchanger2. At this time, the values of the temperatures svt (k) (kt=kt1, kt2, kt3, . . . ) at the plurality of locations arranged along the flow direction G on the primary side of the heat exchanger2are different from those measured during normal operation of the heat exchanger2. On the other hand, when soot accumulating on the heat transfer surface6is removed by operation of the soot removing device5, the efficiency of heat exchange on the heat transfer surface6improves at once, and thus the temperature of fuel decreases at a greater rate on the primary side of the heat exchanger2. Thus, after operation of the soot removing device5, the values of the temperatures svt (k) (kt=kt1, kt2, kt3, . . . ) at the plurality of locations arranged along the flow direction G on the primary side of the heat exchanger2change in response to influence of improvement of the heat exchange efficiency.

Furthermore, as a state amount that causes a state change in response to influence of operation of the soot removing device5, the temperatures svt (u) (ku=ku1, ku2, ku3, . . . ) of at least one point along the flow direction W on the secondary side of the heat exchanger2may be further used, in addition to the temperatures svt (kt) at a plurality of positions in the flow direction G on the primary side of the heat exchanger2. The reason is as follows. As soot accumulates on the heat transfer surface6, the efficiency of heat exchange between the fuel and the heat exchange medium decreases, and thus the temperature at a point of the secondary side of the heat exchanger2decreases. However, after operation of the soot removing device5, when soot on the heat transfer surface6is removed and the efficiency of the heat exchange between the fuel and the heat exchange medium is improved, the temperature svu (ku) of at least one location in the flow direction W of the secondary side of the heat exchanger2rapidly increases.

Accordingly, in the exclusion period τe, which is at least a part of the transient state period τt immediately after operation of the soot removing device5, the state change due to operation of the soot removing device5is added as turbulence to the variation characteristic of the temperatures (kt) at the plurality of locations in the flow direction G of the primary side of the heat exchanger2. Furthermore, in the exclusion period τe, which is at least a part of the transient state period τt immediately after operation of the soot removing device5, the state change due to operation of the soot removing device5is added as turbulence to the variation characteristic of the temperature (ku) of at least one location in the flow direction W on the secondary side of the heat exchanger2. Thus, in the above configuration, from the abnormality diagnosis data dd (k) (1≤k≤K) for abnormality analysis, data de (ke) and/or de (ku) obtained in the time range in which a part of the state amounts svt (kt) and/or svu (ku) is affected by influence of turbulence due to operation of the soot removing device5is excluded.

That is, the abnormality diagnosis device10firstly sets an exclusion period τe, which is at least a part of the transient state period τt immediately after operation of the soot removing device5, for at least one state amount svt (kt) and/or svu (ku) of the plurality of state amounts sv (k) (1≤k≤K). Subsequently, the abnormality diagnosis device10performs pre-processing of excluding, from the time-series data ds (k) of the state amount sv (k), the data de (ke) and/or de (ku) of the state amount svt (kt) and/or svu (ku) obtained in the exclusion period τe. By performing the above pre-processing, the abnormality diagnosis device10obtains the abnormality diagnosis data dd (k) from the time-series data ds (k) of the plurality of state amounts sv (k). For the transient state period τt starts immediately after operation of the soot removing device5, the transient state detection part120may detect a control signal for switching the state of the soot removing device5from a stop state to an operation state, and detect start of the transient state period τt.

Thus, according to this embodiment, it is possible to detect a sign of occurrence of abnormality of the carbon containing fuel heat exchanger1without fully excluding the entire data collected in a time-series manner from the carbon containing fuel heat exchanger1for a part of the state amount svt (kt) and/or svu (ku), and without being affected by turbulence due to operation of the soot removing device5.

According to an illustrative embodiment, in the process of obtaining the abnormality diagnosis data dd (k) with the abnormality diagnosis device10shown inFIGS. 2 and 3, the exclusion period setting part121asets the exclusion period τe on the basis of the response characteristics of the at least one state amount svt (kt) after operation of the soot removing device5. Subsequently, the diagnosis data generation part121bexcludes, from the time series data ds (kt) obtained for the plurality of state amounts sv (k) (1≤k≤K), the data of the at least one state amount svt (kt) obtained in the exclusion period τe which is set based on the response characteristics. Herein, the exclusion period setting part121acan set the exclusion period τe on the basis of the response characteristics of the at least one state amount svt (kt) after operation of the soot removing device5as follows. That is, the exclusion period setting part121aselects the exclusion period τe from the transient state period it, which is a period until turbulence applied to the state amount svt (kt) of the carbon containing fuel heat exchanger1due to operation of the soot removing device5after the operation point of the soot removing device5attenuates and falls within a predetermined range.

Hereinafter, on the basis of the response characteristics of the change of the state amount svt (kt) and/or svu (ku) that occurs in response to operation of the soot removing device5, the method of setting the exclusion period τe by the exclusion period setting part121awill be described in detail with reference toFIGS. 5 and 6. The exclusion period setting part121asets the selected exclusion period τe as follows. That is, the exclusion period setting part121aselects the exclusion period τe from the transient state period τt, which is a period until turbulence applied to the state amount svt (kt) and/or svu (ku) of the carbon containing fuel heat exchanger1due to operation of the soot removing device5after the operation point of the soot removing device5attenuates and falls within a predetermined range. As described above, the state amount svt (kt) and/or svu (ku) corresponds to the temperature measured at at least one location in the flow direction G on the primary side of the heat exchanger2and the flow direction W on the secondary side of the heat exchanger2, and is affected by turbulence applied by operation of the soot removing device5.

For instance, with reference toFIG. 5, the exclusion period setting part121ain the abnormality diagnosis device10determines an appropriate exclusion period τe as follows. The curve graph shown inFIG. 5includes the first section81and the second section82, where y-axis is the temperature measured at at least one location in the flow direction G on the primary side of the heat exchanger2, and x-axis is time. Herein, the above temperature corresponding the y-axis ofFIG. 5is actual measurement data of the state amount svt (kt1) of the carbon containing fuel heat exchanger1.

Furthermore, at time TB1and time TB2shown inFIG. 5, the soot removing device5(e.g. soot blower) is in operation. Thus, in the first section81positioned in the time range immediately after time TB1, temperature variation corresponding to turbulence caused by operation of the soot removing device5is applied. Thus, the exclusion period setting part121aselects the exclusion period τe from the transient state period τt, which is a period until turbulence applied due to operation of the soot removing device5after the operation point TB1of the soot removing device5attenuates and falls within a predetermined range. For instance, as shown inFIG. 5, the influence of turbulence due to operation of the soot removing device5at time TB1is sufficiently attenuated at time TS2, and thus the exclusion period setting part121aselects the time range tc from the operational point TB1of the soot removing device5to time TS2as an exclusion period τe. Thus, according to the embodiment shown inFIG. 5, it is possible to perform abnormal diagnosis by excluding only the data de (kt1) obtained in a period affected by the influence of the turbulence due to operation of the soot removing device5, of the data ds (k1) collected from the carbon containing fuel heat exchanger1, in relation to the state amount svt (tk1).

Meanwhile, in the embodiment shown inFIG. 5, the duration of the transient state until the turbulence applied to the state amount svt (kt) of the carbon containing fuel heat exchanger1due to operation of the soot removing device5from time TB1attenuates and falls within a predetermined range can be estimated focusing on the response characteristics shown in a change of the state amount svt (kt1). Hereinafter, in the setting process of the exclusion period τe described above with reference toFIG. 5, a specific example of appropriate selection taking account of the response characteristics of the change in the state amount svt (kt1) will be described with reference toFIG. 6.

InFIG. 6A, the graph (a1) is a graph plotting time variation of the actual measurement data of the temperature svt (kt1) at a plurality of positions in the flow direction G on the primary side of the heat exchanger2, when the load of the carbon containing fuel heat exchanger1is 100% (when the flow rate of the fuel flowing through the heat exchanger2is at maximum). Further, inFIG. 6A, the graph (a2) is a graph plotting the abnormality diagnosis data dd (kt1) obtained from the temperature change shown in (a1) ofFIG. 6A, in a case where the exclusion period τe is fixed so as to be a period of one hour from the operation time T1of the soot removing device5. Further, inFIG. 6A, the graph (a3) is a graph plotting the abnormality diagnosis data dd (kt1) obtained from the temperature change shown in (a1) ofFIG. 6A, in a case where the length of the exclusion period τe starting from the operation time of the soot removing device5is determined on the basis of the response characteristics of the temporal change of the temperature svt (kt1).

Furthermore, inFIG. 6B, the graph (b1) is a graph plotting time variation of the actual measurement data of the temperature svt (kt1) at a plurality of positions in the flow direction G on the primary side of the heat exchanger2, when the load of the carbon containing fuel heat exchanger1is 50% (when the flow rate of the fuel flowing through the heat exchanger2is at half the maximum). Further, inFIG. 6B, the graph (b2) is a graph plotting the abnormality diagnosis data dd (kt1) obtained from the temperature change shown in (b1) ofFIG. 6B, in a case where the exclusion period τe is fixed so as to be a period of one hour from the operation time T1of the soot removing device5. Further, inFIG. 6B, the graph (b3) is a graph plotting the abnormality diagnosis data dd (kt1) obtained from the temperature change shown in (b1) ofFIG. 6B, in a case where the length of the exclusion period τe starting from the operation time of the soot removing device5is determined on the basis of the response characteristics of the temporal change of the temperature svt (kt1).

In an illustrative exclusion period, to obtain the abnormality diagnosis data dd (kt1) shown in (a3) ofFIG. 6Aand (b3) ofFIG. 6B, the exclusion period τe may be determined as follows in accordance with the response characteristics of the temporal change of the temperature svt (kt1). That is, when setting the exclusion period τe on the basis of the response characteristics of the temporal change of the temperature svt (kt1), the length of the exclusion period τe may be determined as follows. First, a time constant τr indicating the response characteristics of at least one state amount svt (kt) after operation of the soot removing device5is obtained. Next, the time constant τr obtained from the temporal change of the at least one state amount svt (kt1) obtained after operation of the soot removing device5may be applied to a pre-set correlation between the time constant τr and the exclusion period τe, thereby determining the length of the exclusion period τe.

According to this embodiment, in a case where the response characteristic of the at least one state amount svt (kt1) after operation of the soot removing device5indicates a quick response, the time constant τr indicating the response characteristics should be also small. In a case where the response characteristic indicates a slow response, the time constant τr indicating the response characteristic should be also large. Thus, in this embodiment, the correlation between the time constant indicating the response characteristic and the length of the exclusion period τe is set in advance, and the exclusion period τe corresponding to the magnitude of the time constant τr is set on the basis of the correlation. Thus, according to this embodiment, it is possible to set an appropriate length for the exclusion period in accordance with the magnitude of the time constant τr.

From another perspective, the criteria for determining the length of the exclusion period τe according to this embodiment can be described as follows. In this embodiment, the response characteristic is used to estimate a decrease with time in the amplitude of turbulence applied to the state amount svt (kt1) of the carbon containing fuel heat exchanger1due to operation of the soot removing device5from time TB1. Furthermore, in this embodiment, the exclusion period τe is set as a period until the amplitude of the turbulence becomes greater than 63.2% of a stabilized value determined by the temperature variation before operation of the soot removing device5. In (a1) ofFIG. 6A, h1indicates the magnitude of the above described stabilized value determined by the temperature variation before operation of the soot removing device5. Further, inFIG. 6A, tc indicates a period until turbulence applied to the state amount svt (kt) of the carbon containing fuel heat exchanger1due to operation of the soot removing device5after the operation point T1of the soot removing device5attenuates and falls within a predetermined range.

Then, with reference to (a1) inFIG. 6A, at time T2that is 20 minutes after the operation time T1of the soot removing device5, the variation waveform of the temperature, which is actual measurement data of the state amount svt (kt1), becomes greater than the magnitude of 63.2% of the stabilized value indicated by h1. Thus, as shown in (a2) ofFIG. 6A, even if the exclusion period τe is fixed to be from the operation time T1of the soot removing device5to the time T3that is one hour after time T1, it is possible to sufficiently remove influence of turbulence due to operation of the soot removing device5, from the actual measurement data of the state amount svt (kt1). Furthermore, as shown in (a3) ofFIG. 6A, in a case where the exclusion period τe is set as a period having a further margin in addition to the period until the amplitude of the turbulence becomes greater than the magnitude of 63.2% of the stabilized value, the exclusion period τe may be set as a period from the operation time T1to time T3that is one hour after time T1. As a result, as shown in (a2) and (a3) inFIG. 6A, before and after the exclusion period τe, only the temperature data of the state amount svt (k1) exceeding the stabilized value h1remains non-excluded.

Then, as the soot removing device5operates at time T4, as shown in (a1) ofFIG. 6A, at time T5, the amplitude of turbulence slightly exceeds the magnitude of 63.2% of the stabilized value represented by h1. Thus, as shown in (a2) and (a3) ofFIG. 6A, by setting the exclusion period τe as a period from time T4to time T5that is one hour after T4, it is possible to sufficiently exclude influence of turbulence due to operation of the soot removing device5.

In (b1) ofFIG. 6B, h2indicates the magnitude of the above described stabilized value determined by the temperature variation before operation of the soot removing device5. Further, inFIG. 6B, tc indicates a period until turbulence applied to the state amount svt (kt) of the carbon containing fuel heat exchanger1due to operation of the soot removing device5after the operation point T1of the soot removing device5attenuates and falls within a predetermined range. Then, with reference to (b1) inFIG. 6B, two hours after the operation time T1of the soot removing device5, the variation waveform of the temperature, which is actual measurement data of the state amount svt (kt1), becomes greater than the magnitude of 63.2% of the stabilized value represented by h2.

Thus, as shown in (b2) ofFIG. 6B, if the exclusion period τe is fixed to be from the operation time T1of the soot removing device5to the time T2that is one hour after time T1, influence of turbulence due to operation of the soot removing device5is still present at time T2, which is the termination point of the exclusion period τe. As a result, in a case shown in (b2) ofFIG. 6B, influence of turbulence due to operation of the soot removing device5cannot be fully excluded when performing abnormality diagnosis, only by excluding the temperature data obtained in the exclusion period τe. Thus, as shown in (b3) ofFIG. 6B, in a case where the exclusion period τe is set as a period until the amplitude of the turbulence becomes greater than the magnitude of 63.2% of the stabilized value indicated by h2, the exclusion period τe is set as a period of two hours after the operation time T1of the soot removing device5. As a result, as shown in (b3) ofFIG. 5, at time T3, which is the terminating point of the exclusion period τe, the influence of turbulence due to operation of the soot removing device5is substantially absent, and thus it is possible to perform abnormality diagnosis while fully excluding influence of turbulence due to operation of the soot removing device5.

Further, as shown in (b2) ofFIG. 6B, if the ending time of the exclusion period τe is set at time T5one hour after operation of the soot removing device5at time T4, only an extremely brief time is left at time T5until the next operation time T6of the soot removing device5. If the time between the ending time T5of the exclusion period τe and the next operation time T6of the soot removing device5is extremely short, the response characteristic of the temperature change obtained by actually measuring the state amount svt (kt) becomes unstable. Thus, in an alternative embodiment, as shown in (b3) ofFIG. 6B, the exclusion period τe may be set as a period from time T4of operation of the soot removing device5to time T7of the second next operation of the soot removing device5. As a result, as shown in (b3) inFIG. 6B, before and after the exclusion period τe, only the temperature data of the state amount svt (k1) exceeding the stabilized value h1remains non-excluded.

Accordingly, in this embodiment, the exclusion period τe is set on the basis of the response characteristic shown in the temporal change of the at least one state amount svt (kt) after operation of the soot removing device5. Thus, according to this embodiment, in a case where the response characteristics indicate a quick response, it is possible to set the exclusion period τe to be accordingly short. On the other hand, if the response characteristics indicate a slow response, it is possible to set the exclusion period to be accordingly long.

Further, in the above configuration, in a case where the response characteristic of the at least one state amount svt (kt) after operation of the soot removing device5indicates a quick response, the time constant τr indicating the response characteristics should also be small. In a case where the response characteristic indicates a slow response, the time constant τr indicating the response characteristic should also be large. Thus, in this configuration, the correlation between the time constant τr indicating the response characteristic and the length of the exclusion period τe is set in advance, and the exclusion period τe corresponding to the magnitude of the time constant τr is set on the basis of the correlation. Thus, according to this configuration, it is possible to set an appropriate length for the exclusion period τe in accordance with the magnitude of the time constant τr.

Further, in another illustrative embodiment, in the process of obtaining the abnormality diagnosis data dd (k), the exclusion period setting part121amay set the length of the exclusion period τe so as to reduce the difference between the frequency distribution and the normal distribution related to the time-series data ds (kt) of the at least one state amount svt (kt). In this embodiment, the data de (kt) in the exclusion period τe excluded from abnormality diagnosis is data obtained in a transient time range in which a part of the state amount svt (kt) is affected by operation of the soot removing device5. Thus, even in a case where data collected in a period other than the exclusion period τe from the device for the plurality of state amounts sv (k) is distributed according to the normal distribution, the data of the state amount collected in the exclusion period τe may not necessarily be distributed according to the normal distribution.

Thus, in this embodiment, the length of the exclusion period τe is determined so as to reduce the difference between the frequency distribution and the normal distribution related to the data ds (kt) obtained in relation to the at least one state amount svt (kt). Thus, according to this embodiment, it is possible to set the exclusion period τe as a transient time range in which the state amount svt (kt) is affected by operation of the soot removing device5(a period in which the frequency distribution is offset from the normal distribution), and the data de (kt) obtained in this period is not used in the abnormality diagnosis, and thus abnormality diagnosis can be performed accurately. Hereinafter, this embodiment will be described in detail with reference toFIGS. 5 and 7.

The frequency distribution92shown on the right side ofFIG. 5is a frequency distribution of temperature measurement values included in the second section82, which follows the normal distribution. Furthermore, as described in detail below, the frequency distribution of temperature measurement values included in the first section81is a frequency distribution combining the frequency distribution91and the frequency distribution92shown on the right side ofFIG. 5. With the frequency distribution91added to the frequency distribution92, the frequency distribution does no longer follow the normal distribution. Furthermore, at time TB1and time TB2shown inFIG. 5, the soot removing device5is in operation. Thus, in the first section81positioned in a time range from time TB1to time TS2, a component of the above turbulence due to operation of the soot removing device5is added to the temperature measurement values having the same frequency distribution as those measured in the second section82. Furthermore, as the temperature variation corresponding to a component of temperature due to operation of the soot removing device5is added to the temperature variation similar to the temperature variation in the second section82, the temperature distribution in the first section81is a distribution where the frequency distribution91corresponding to a component of temperature is added to a frequency distribution similar to the frequency distribution92. As a result, the frequency distribution of temperature measurement values included in the first section81is a non-normal distribution where the frequency distribution91is added to the frequency distribution92.

Accordingly, in the time range from time TB1to time TS2, due to addition of the temperature variation corresponding to a component of turbulence due to operation of the soot removing device5, the temperature distribution in the first section81becomes a non-normal distribution, while the temperature distribution in the second section82not affected by operation of the soot removing device5becomes a normal distribution. From another perspective, if the length of the exclusion period τe is sufficient to exclude the influence of operation of the soot removing device5on the state amount svt (kt), the difference between the temperature data distribution of the state amount svt (kt) and the normal distribution should become smaller. In contrast, if the length of the exclusion period τe is not sufficiently long to exclude the influence of operation of the soot removing device5on the state amount svt (kt), the difference between the temperature data distribution of the state amount svt (kt) and the normal distribution should become greater.

Thus, in this embodiment, the exclusion period setting part121amay determine the length of the exclusion period τe so as to reduce the difference between the frequency distribution and the normal distribution related to the data ds (kt) obtained in relation to the at least one state amount svt (kt) affected by operation of the soot removing device5, as described below with reference toFIG. 7. Furthermore, in an illustrative embodiment, when determining the length of the exclusion period τe, so as to reduce the difference between the frequency distribution and the normal distribution related to the data ds (kt), the length of the exclusion period τe may be determined on the basis of an index of consistency between the frequency distribution and the normal distribution.

According to this embodiment, when setting the length of the exclusion period τe, an index of consistency between the frequency distribution and the normal distribution is taken into account, and thus it is possible to more accurately identify a transient time range during which the state amount svt (kt) is affected by operation of the soot removing device5by using the index. Thus, it is possible to set the exclusion period τe more appropriately, and to improve the accuracy of abnormal diagnosis. That is, the length of the exclusion period τe may be determined so as to increase an index used to evaluate the consistency between the frequency distribution and the normal distribution related to the data ds (kt) obtained in relation to the state amount svt (kt). Hereinafter, the index of the consistency between the frequency distribution and the normal distribution related to the data ds (kt) will be described with reference toFIG. 7.

FIG. 7Ashows a case where the consistency between the frequency distribution61related to data ds (kt) obtained in relation to the state amount svt (kt) and the normal distribution curve71is high.FIG. 7Bshows a case where the consistency between the frequency distribution62related to data ds (kt) obtained in relation to the state amount svt (kt) and the normal distribution curve72is low. InFIGS. 7A and 7B, x-axis is class value th of temperature data obtained by actually measuring the state amount svt (kt). To simplify the description, inFIGS. 7A and 7B, in an embodiment, the frequency distribution related to data ds (kt) and the normal distribution are standardized so as to have the same average and standard variation. In the example shown inFIG. 7, the difference between the frequency distribution and the normal distribution related to data ds (kt) may be evaluated numerically with the following scale, for instance.

In the example shown inFIG. 7, if the frequency distribution and the normal distribution are standardized so as to have the same average and standard variation, the consistency φ between the frequency distribution and the normal distribution curve can be calculated by the following expression. Herein, deg (th) is the degree of the actual measurement data at a temperature class value th, and nd (th) is the height of the normal distribution curve at a temperature class value th.

Furthermore, if the frequency distribution related to data ds (kt) obtained for the state amount svt (kt) and the normal distribution are not standardized to have the same average and standard variation, firstly, the average μ and the standard variation σ of the frequency distribution may be obtained, and a normal distribution with distribution parameters being the average μ and the standard variation σ may be used.

As described above, in some embodiments described above with reference toFIGS. 5 to 7, provided that the carbon containing fuel heat exchanger1E shown inFIG. 1is the device1to be diagnosed, only the influence of operation of the soot removing device5E disposed directly above the carbon containing fuel heat exchanger1E on the state amount of the carbon containing fuel heat exchanger1is taken into account. Next, in the following embodiment described with reference toFIG. 8, as influence from another device that causes a state change of the carbon containing fuel heat exchanger1, influence of operation of the soot removing device5′ of the preceding device1′ positioned upstream of the carbon containing fuel heat exchanger1will be also taken into account. For instance, provided that the carbon containing fuel heat exchanger1E shown inFIG. 1is the device1to be diagnosed, in addition to influence due to operation of the soot removing device5E, influence due to operation of the soot removing device5D and the soot removing device5C is also taken into account. Herein, the soot removing device5D and the soot removing device5C are soot removing devices5′ provided to prevent abnormality of the carbon containing fuel heat exchangers1D and1C corresponding to the preceding devices1′ positioned upstream of the carbon containing fuel heat exchanger1E.

Hereinafter, with reference toFIG. 8, described is an example of setting of the exclusion period τe in a case where influence of the above three soot removing devices5E,5D, and5C is applied as turbulence to the carbon containing fuel heat exchanger1E shown inFIG. 1.FIG. 8Ais a diagram showing the time of operation of the three soot removing devices5, where x-axis is time scale. InFIG. 8B, the curve graph84shows the temporal change of the actual measurement value of the state amount svt (kt1), where x-axis is time scale. Herein, the state amount svt (kt1) corresponds to the temperature at the inlet portion for fuel gas, positioned in the top section of the carbon containing fuel heat exchanger1E, of the temperatures svt (kt) (kt=kt1, kt2, kt3, . . . , ktm) at a plurality of positions in the flow direction G on the primary side of the carbon containing fuel heat exchanger1E.

FIG. 8Cis a diagram showing the time of operation of three soot removing devices5E and5D. InFIG. 8D, the curve graph85shows the temporal change of the actual measurement value of the state amount svt (ktm). Herein, among the temperatures svt (kt) (kt=kt1, kt2, kt3, . . . , ktm) at a plurality of positions in the flow direction G on the primary side of the carbon containing fuel heat exchanger1E, the state amount svt (ktm) corresponds to the temperature at the outlet portion for fuel gas, positioned in the bottom section of the carbon containing fuel heat exchanger1E. InFIG. 8E, the curve graph86shows the temporal change of a temperature difference obtained by subtracting the actual measurement value of the state amount svt (ktm) from the actual measurement value of the state amount svt (kt1). That is, the curve graph86shown inFIG. 8Eis a graph plotting the temperature difference obtained by subtracting the value at each time of the curve graph85inFIG. 8Dfrom the value at each time on the curve graph84inFIG. 8B, along the time scale.

With reference toFIG. 8, the soot removing device5C operates at time T1, and influence Br11of turbulence due to operation of the soot removing device5C is shown as a rapid temperature decrease of the temperature svt (kt1) shown inFIG. 8B. Further, influence Br21of turbulence due to operation of the soot removing device5C is shown as a rapid temperature decrease of the temperature svt (ktm) shown inFIG. 8D. That is, while the soot removing device5C removes soot from the heat exchanger1C disposed upstream of the heat exchanger1E, influences Br11and Br21of turbulence due to operation of the soot removing device5C are on not only the heat exchanger1C but also the heat exchangers1D and1E. This is because, when the heat exchange efficiency of the heat exchanger1C disposed upstream of the heat exchanger1E is rapidly improved by operation of the soot removing device5, the temperature decrease of fuel gas passing the heat exchanger1C also becomes greater rapidly. That is, for the temperature of fuel gas supplied to the heat exchangers1D and1E decreases rapidly due to improvement of the heat change efficiency at the heat exchanger1C, such a temperature decrease acts as turbulence that causes a non-stationary state change on the heat exchangers1D and1E.

Subsequently, the soot removing device5D operates at time T2, and influence Br31of turbulence due to operation of the soot removing device5D is shown as a rapid temperature decrease of the temperature svt (ktm) shown inFIG. 8D. Herein, with reference toFIG. 8D, the rapid decrease of the temperature svt (ktm) caused by the influence Br31of operation of the soot removing device5D at time T2appears as being added to the rapid decrease of the temperature svt (ktm) caused by the influence Br21of operation of the soot removing device5C at time T1. Further, since time T2is the point of time immediately after time T1of operation of the soot removing device5C, the influence of turbulence due to operation of the soot removing device5D is not present in the temperature svt (kt1) shown inFIG. 8B. Furthermore, the soot removing device5E for removing soot from the heat exchanger1E operates at time T3, and influence Br32due to operation of the soot removing device5E is shown as a rapid temperature decrease of the temperature svt (ktm) shown inFIG. 8D. Further, at time T4and time T5, operation of the soot removing devices5C and5D causes a phenomenon similar to that at time T1and T2.

Accordingly, at time T1and time T2, from time T1, the transient state period τt starts, in which a non-stationary state change is applied to the heat exchanger1E due to the influence Br21and the influence Br31of turbulence due to operation of the soot removing device5C and the soot removing device5D on the temperature actual measurement values of the state amount svt (ktm). As described above, even if one of the three soot removing devices5C,5D, and5E is operated, the rapid decrease in the temperature svt (ktm) is applied to the heat exchanger1E as a non-stationary state change. Thus, in an illustrative embodiment, in the example shown inFIG. 8, the transient state detection part120may detect start of the transient state period τt as follows. That is, a logical add output signal of a logical add (OR) of three trigger signals for switching each of the three soot removing devices5C,5D, and5E from a stop state to an operation state may be monitored, and it may be determined that the transient state period τt is started at the time when the logical add signal becomes active.

In an illustrative embodiment, in the example shown inFIG. 8, the exclusion period setting part121amay set an exclusion period τe, which is at least a part of the transient state period τt, on the basis of the response characteristic of the state amount svt (kt) after operation of one of the soot removing devices5C,5D,5E. Specifically, the exclusion period setting part121amay select an appropriate exclusion period τe from the transient state period τt, which is a period until turbulence applied to the state amount svt (kt) of the carbon containing fuel heat exchanger1due to operation of one of the soot removing devices5C,5D,5E after the operation point of one of the soot removing devices5C,5D,5E attenuates and falls within a predetermined range. For instance, the duration of the transient state until the turbulence applied to the state amount svt (kt) of the carbon containing fuel heat exchanger1due to operation of one of the soot removing devices5C,5D, and5E attenuates and falls within a predetermined range can be estimated focusing on the response characteristic shown by the change of the state amount svt (kt1) described above with reference toFIGS. 5 and 6. As a result, inFIG. 8D, the exclusion period τe for excluding influence of operation of the soot removing devices5C and5D at time T1and T2is set as tc(1), and the exclusion period τe for excluding influence of operation of the soot removing device5E at time T3is set as tc(2). Furthermore, inFIG. 8D, the exclusion period τe for excluding influence of operation of the soot removing devices5C and5D at time T4and T5is set as tc(3).

Further, in another embodiment, in the transient state period τt started by operation of one of the soot removing devices5C,5D, or5E, the transient state period svt (kt) is distributed according to the non-normal distribution. In contrast, after the transient state period τt elapses, the temperature svt (kt) is distributed according to the normal distribution. Accordingly, the exclusion period τe included in the transient state period τt started by operation of one of the soot removing devices5C,5D, or5E can be determined by the exclusion period setting part121aaccording to the above method described with reference toFIG. 7.

Furthermore, in yet another embodiment, the exclusion period τe included in the transient state period τt started by operation of one of the soot removing devices5C,5D, or5E can be determined as follows. That is, the temporal change86shown inFIG. 8Eis monitored as a temperature difference between the inlet side and the outlet side of the fuel of the heat exchanger1E, assuming that the exclusion period τe starts from the starting point of the transient state period τt. Further, a point of time at which the temperature difference is within ±37.8% of the stabilized value h3shown inFIG. 8Eis set to be the ending point of the exclusion period τe. That is, this method corresponds to the technique for determining the length of the exclusion period τe focusing on the response characteristic shown by the temporal change86of the temperature difference.

As described above, in the embodiment described above with reference toFIGS. 5 to 8, only the influence of operation of one of the soot removing devices5C to5X disposed inside the gas cooler32bto remove soot from one of the carbon containing fuel heat exchangers1C to1X is considered as turbulence that causes a non-stationary state change. However, for instance, even in a case where a rapid change occurs in the generated gas flow amount in the coal gasification part32aconnected to the preceding stage of the gas cooler32b, there may be influence of turbulence that causes a non-stationary state change of the carbon containing fuel heat exchangers1C to1X. In this case, a rapid change of the generated gas flow rate transmits as a response delay of the state change, which affects “the temperatures at a plurality of locations in the gas flow passage direction of the primary side”, which are the state amounts of the carbon containing fuel heat exchanger1. Further, influence of the turbulence may cause a non-stationary state change that is similar to influence of operation of the soot removing devices5C to5J. As a result, if actual measurement data of the state amount in the carbon containing fuel heat exchanger1is obtained within the transient state period in which a rapid change of the above generated gas flow amount is transmitted as a response delay of the state change, the actual measurement data deviates from the unit space for obtaining the Mahalanobis distance, which may lead to generation of wrong abnormal diagnosis results.

Thus, in yet another embodiment, even in the above case, the transient state detection part120may detect start of the transient state period τt in which a non-stationary state change of the carbon containing fuel heat exchanger1disposed inside the gas cooler32boccurs. For instance, the transient state detection part120may detect start of the transient state period τt as follows, by modeling the rapid change of the generated gas flow rate at the coal gasification part32aas a step input of the state change corresponding to the generated gas flow rate. First, of the state amounts of the carbon containing fuel heat exchanger1, the first response waveform of the state change is obtained continuously for a part of state amounts svx (kx) related to the generated gas flow rate. Next, a step response waveform obtained as a result of input of the step input of the state amount svx (kx) into the response characteristic function of the carbon containing fuel heat exchanger1is calculated. Finally, the step response waveform is compared to the first response waveform, and start of the transient state period τt may be detected on the basis of the comparison result. For instance, the transient state detection part120may determine that the transient state period τt starts when the first response waveform that is offset from the step response waveform by a difference smaller than a predetermined reference value is obtained.

Once start of the transient state period τt is detected as described above, the diagnosis data generation part121bmay perform pre-processing of excluding the data de (kt) of at least one state amount svt (kt) obtained in an exclusion period τe which is at least a part of the transient state period τt, from the time-series data ds (k) of the state amount. Finally, the diagnosis data generation part121bmay obtain the abnormality diagnosis data dd (k) excluding the data de (kt) in the exclusion period τe from the time-series data ds (k) for the plurality of state amounts sv (k).

Next, described below is a method by which the abnormality diagnosis part122having received the abnormality diagnosis data dd (k) (1≤k≤K) from the diagnosis data acquisition part121performs abnormality diagnosis on the carbon containing fuel heat exchanger1on the basis of the Mahalanobis distance. First, the concept of the Mahalanobis distance calculated by the abnormality diagnosis part122will be described with reference toFIG. 9.FIG. 9is a diagram showing a correlation of two parameters, where x-axis is a difference between the inlet temperature svt (kt1) and the outlet temperature svt (kt2) in the flow direction G on the primary side of the heat exchanger2, and y-axis is the temperature svu (ku1) at a point in the flow direction W on the secondary side of the heat exchanger2. That is, as soot accumulates on the heat transfer surface6, the efficiency of heat exchange between the fuel and the heat exchange medium decreases, and thus the temperature svu (ku1) of a point on the secondary side of the heat exchanger2decreases. While each measurement data has variability due to variation in the atmosphere condition and the operation state, there is a correlation between the temperature difference between the inlet temperature svt (kt1) and the outlet temperature svt (kt2) and the temperature svu (ku1) of a point on the secondary side of the heat exchanger2, and each data is within a particular range. By using this data as reference data, a reference unit space is generated. Also for other state amounts, it is possible to obtain a correlation similar to the correlation between the temperature on the primary side and the temperature on the secondary side. Further, for the unit space, it is determined whether the data to be determined is normal or abnormal, using the Mahalanobis distance.

Accordingly, in an illustrative embodiment, the abnormality diagnosis part122of the abnormality diagnosis device10may perform abnormality diagnosis of the carbon containing fuel heat exchanger1as follows. First, the Mahalanobis distance MD (k) of the abnormality diagnosis data dd (k) (1≤k≤K) is calculated with reference to a unit space including a plurality of state amounts svn (k) (1≤k≤K) at the time when the carbon containing fuel heat exchanger1is normal. Next, if the Mahalanobis distance MD (k) is greater than a threshold, it is determined that the carbon containing fuel heat exchanger1is abnormal.

That is, in this embodiment, the Mahalanobis distance MD (k) of the abnormality diagnosis data dd (k) (1≤k≤K) is calculated with reference to a unit space including a plurality of state amounts svn (k) (1≤k≤K) at the time when the carbon containing fuel heat exchanger1is normal. Thus, according to this embodiment, it is possible to evaluate quantitatively the extent of deviation of the abnormality diagnosis data dd (k) not affected by operation of the soot removing device5from the unit space representing the state group svn (k) (1≤k≤K) at the time when the device is normal. As a result, according to this embodiment, it is possible to diagnose an abnormality of the device with a high accuracy on the basis of the abnormality diagnosis data dd (k) not affected by operation of the soot removing device5.

Herein, the carbon containing fuel heat exchanger1shown inFIG. 1is provided for an integrated gasification combined cycle plant (hereinafter, referred to as “IGCC plant”) shown inFIG. 10, for instance. As shown inFIG. 10, the IGCC plant30mainly includes a coal gasification furnace32, a gas turbine facility34, a steam turbine facility36, and a heat recovery steam generator (hereinafter, referred to as “HRSG”)38. On the upstream side of the coal gasification furnace32, a coal supplying facility40for supplying powdered fuel to the coal gasification furnace32is provided. The coal supplying facility40includes a pulverizer (not shown) configured to pulverize coal into powdered coal of a few μm to a few hundreds of μm. The powdered fuel pulverized by the pulverizer is stored in a plurality of hoppers42. The powdered fuel stored in each hopper42is transported to the coal gasification furnace32with nitrogen gas supplied from an air separation facility44by a constant flow rate. The air separation facility44is a device to separate nitrogen gas and oxygen gas from air and supply these gases to the coal gasification furnace32.

The coal gasification furnace32includes a coal gasification part32aformed so that the gas flows from bottom toward top, and a gas cooler (SGC)32bconnected to the downstream side of the coal gasification part32aand formed so that the gas flows from top to bottom. In the coal gasification part32a, a combustor and a reductor are disposed from below. The combustor combusts a part of powdered fuel and char, and discharges the rest through thermal decomposition as volatile portions (CO, H2, lower class carbon hydride). The combustor and the reductor are provided with a combustor burner and a reductor burner, respectively, to which powdered fuel is supplied from the coal supplying facility40. The combustor burner is supplied with air extracted from the air compressor34cof the gas turbine facility34via an air pressure-increasing unit46and an oxidizing-agent supplying passage48, as an oxidizing agent with oxygen gas separated by the air separation facility44. In the reductor, powdered coal is gasified by high-temperature combustion gas from the combustor. Accordingly, a combustible gas that serves as a gas fuel such as CO and H2(hereinafter, referred to as “fuel gas”) is generated from coal.

The gas cooler32bincludes a plurality of carbon containing fuel heat exchangers1described above. The gas cooler32bobtains sensible heat from fuel gas introduced from the reductor to produce steam, and cools the fuel gas generated in the gasification furnace32. The steam generated in the carbon containing fuel heat exchanger1is mainly used as steam for driving the steam turbine36b. The fuel gas having passed through the gas cooler32bis introduced to a dirt removing facility50. The dirt removing facility50includes a porous filter, and captures and recovers char including non-combusted matters mixed in fuel gas, by making the char pass through the porous filter. The char accordingly recovered is returned to a char burner of the coal gasification furnace32to be recycled.

The fuel gas having passed through the dirt removing facility50is purified by the gas purifying facility22, and is sent to the combustor34aof the gas turbine facility34. The gas turbine facility34includes a combustor34afor combusting fuel gas, a gas turbine34bdriven by fuel gas, and an air compressor34cfor sending high-pressure air to the combustor34a. The gas turbine34band the air compressor34care connected by the same rotational shaft34d. The air compressed by the air compressor34cis extracted and also introduced to the air pressure-increasing unit46, separately from the combustor34a.

The combustion exhaust gas having passed through the gas turbine34bis introduced to the HRSG38. The steam turbine36bis supplied with high-pressure steam from the coal gasification furnace32and the HRSG38. As an example, a gas turbine34band a steam turbine36bare connected to the rotational shaft34d, and a generator52for outputting electric power is disposed opposite to the gas turbine34across the steam turbine facility36. Further, the HRSG38produces steam from combustion exhaust gas from the gas turbine34b, and discharges the combustion exhaust gas to the ambient air through a stack54.

As described above, the carbon containing fuel heat exchanger1shown inFIG. 2is provided for the gas cooler32bin the coal gasification furnace32of the IGCC plant30, for instance. Further, the carbon containing fuel heat exchanger1exchanges heat between fuel gas, which is a fuel containing carbon, and the heat exchange medium. If carbon adheres to the heat transfer surface6and cannot be removed by the soot removing device5, an abnormality may occur in the carbon containing fuel heat exchanger1through which a carbon containing fuel flows, such as failure to perform heat exchange sufficiently due to clogging of the heat transfer surface6. Thus, the abnormality diagnosis device10determines presence or absence of an abnormality of the carbon containing fuel heat exchanger1by using the Mahalanobis distance.

DESCRIPTION OF REFERENCE NUMERALS