Rotating machinery diagnostic device and rotating machinery diagnostic method

Abstract

To provide a rotary machine diagnostic device and rotary machine diagnostic method capable of identifying a failure occurrence place and inferring a failure cause, regarding an unbalance occurrence event of a rotor.SOLUTION: A rotary machine diagnostic device 100 comprises: an input section 110 that receives calculation condition data including reference data and an influence coefficient matrix, and a measured state value of a rotating section including vibration data, rotation speed, and phase calculation data for phase calculation; a calculation section 130 that performs calculation for identifying a failure occurrence place and inferring the failure cause on the basis of the calculation condition data and the measured state value received by the input section 110; a storage section 120 that stores a calculation result including the calculation condition data, the measured state value, a vibration mode obtained by the calculation section 130; and an output section 140 that outputs and displays contents stored in the storage section 120.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] Embodiments of the present invention relate to a rotating machinery diagnostic device and a rotating machinery diagnostic method. [Background technology]

[0002] To improve the operating rate of equipment within power plants, the development of a fault diagnosis system for power plants using IoT (Internet of Things) technology is being considered. However, in order to properly perform such fault diagnosis, the challenge lies in how to configure the database and algorithms that can accurately detect faults and signs of failure from measurement data.

[0003] A conventional example of a system for condition monitoring involves attaching sensors, such as vibration sensors, to the target equipment and identifying abnormal locations in the equipment based on measurement data obtained from these sensors. For example, an abnormality diagnosis system is known that includes a vibration detection sensor installed on a rotating machine to be diagnosed, a processing unit that converts the detection signal from the vibration detection sensor into vibration data, and an information processing device that performs a diagnosis from the vibration data from the processing unit.

[0004] As a second example, there is a known anomaly detection device that calculates a vibration vector indicating the rotation angle and magnitude at which the vibration of the rotating shaft is maximum, based on measurements of the vibration and rotation angle of the rotating shaft from multiple axial vibration sensors, and estimates the location of the anomaly in the axial direction of the rotating shaft based on its time evolution. [Prior art documents] [Patent Documents]

[0005] [Patent Document 1] Patent No. 3834228 [Patent Document 2] Japanese Patent Publication No. 2019-113364 [Overview of the Initiative] [Problems that the invention aims to solve]

[0006] The shaft vibrations observed during turbine and generator operation are influenced by various factors and have corresponding frequency components. Furthermore, depending on the failure mode occurring in each rotating part, vibrations with rotational synchronization components due to changes in balance state resulting from changes in weight imbalance or changes in shaft bending during operation account for the majority of the vibrations.

[0007] Normally, the unbalance distribution of a rotating part is unknown. However, by measuring the amplitude and phase of the rotating part during operation, it becomes possible to estimate the distribution. By estimating where the unbalance occurs on the rotating part and the failure events and causes that occur, necessary measures can be taken in a short time, leading to an improvement in plant operating efficiency.

[0008] In the first example of the aforementioned anomaly diagnosis system, data related to one type of frequency generated during the operation of rotating machinery is converted to create a single converted data set, and the cause of the anomaly is identified based on this. However, the same frequency can contain multiple anomaly factors, making it difficult to narrow down the cause of the anomaly with this configuration.

[0009] Furthermore, in the second example of the aforementioned anomaly diagnosis system, similar to the first example, the system estimates the location of an anomaly by comparing the vibration vector of a specific location where an anomaly actually occurred in the rotating part of an actual machine or a machine of the same type with the current vector, for a wide variety of anomaly events occurring in the rotating shaft. Therefore, it will be unable to detect anomalies in cases where there is no actual data or in newly installed machines.

[0010] The present invention was made to solve these problems, and aims to provide a rotating machinery diagnostic device and a rotating machinery diagnostic method that enable the identification of the failure location and estimation of the failure cause in relation to the occurrence of unbalance in the rotating parts of a rotating machine during operation. [Means for solving the problem]

[0011] To achieve the above objective, the rotating machine diagnostic device according to an embodiment of the present invention is a rotating machine Included Rotating part Rotating A rotating machinery diagnostic device for identifying the location of a failure and estimating the cause of a failure in relation to an unbalance occurrence event, The rotating machine is equipped with a vibration meter located near each bearing, and a rotation speed meter and a phase detector located opposite the rotating part of the rotating machine, and the rotating machine diagnostic device is equipped with a vibration meter located near each bearing rotation machine diagnostic device is equipped with a vibration meter located near each bearing, and a rotation speed meter and a phase detector located opposite the rotating part of the rotating machine, and the rotating machine diagnostic device is equipped with a vibration meter located near each bearing, Resonance coefficient matrix of data, From the aforementioned vibration meter Vibration data, The rotational part from the rotation speed meter Rotation speed, and the rotating part phase of For calculation From the phase detector Data for phase calculation of The input section that accepts inputs, Based on the phase calculation data received by the input unit, the phase is calculated; a vibration vector is derived using the phase and the vibration data; the moving velocity, moving acceleration, and directional change rate of the vibration vector are calculated based on the vibration vector; the vibration mode of the rotating part for estimating the failure cause is identified from among a plurality of vibration modes based on the moving velocity, moving acceleration, and directional change rate; and the vibration vector and the influence coefficient matrix are used. Based By calculating the unbalance distribution of the rotating part, The aforementioned fault location of identification do A calculation unit that performs calculations, The calculation unit displays the location of the failure, and the calculation unit also displays information related to the failure cause based on the vibration mode. It is characterized by comprising an output unit for displaying information. [Brief explanation of the drawing]

[0012] [Figure 1] This is a block diagram showing the configuration of a rotating machine diagnostic device according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of a rotating part of a rotating machine to which the rotating machine diagnostic device according to the first embodiment is applied. [Figure 3] This is a conceptual diagram showing a vibration meter and a phase detector for the rotating part of a rotating machine to which the rotating machine diagnostic device according to the first embodiment is applied. [Figure 4] This is a conceptual diagram showing a vibration meter for the rotating part of a rotating machine to which the rotating machine diagnostic device according to the first embodiment is applied. [Figure 5] This is a conceptual diagram showing a first example of a phase detector for the rotating part of a rotating machine to which the rotating machine diagnostic device according to the first embodiment is applied. [Figure 6] This is a conceptual diagram showing a second example of a phase detector for the rotating part of a rotating machine to which the rotating machine diagnostic device according to the first embodiment is applied. [Figure 7] This is an explanatory diagram of a model in an example of a rotating part of a rotating machine to which the rotating machine diagnostic device according to the first embodiment is applied. [Figure 8] This is a flowchart showing the overall procedure of the rotating machinery diagnostic method according to the first embodiment. [Figure 9] This flowchart shows the details of the step for deriving the unbalanced position in the procedure for the diagnostic method during startup and shutdown of a rotating machine diagnostic method according to the first embodiment. [Figure 10] This is a flowchart showing the procedure for identifying and diagnosing the location of a fault during rated rotational operation, which is part of the rotating machinery diagnostic method according to the first embodiment. [Figure 11] This is an example of a display of a polar diagram in the case of thermal vibration obtained in the procedure for identifying and diagnosing the location of a fault during rated rotational operation, which is part of the rotating machinery diagnostic method according to the first embodiment. [Figure 12] This is an example of a polar diagram display in the case of cyclic vibration caused by a hard rub, obtained in the procedure for identifying and diagnosing the location of a fault during rated rotational operation, which is part of the rotating machinery diagnostic method according to the first embodiment. [Figure 13] This is an example of a polar diagram display in the case of cyclic vibration caused by a soft rubbing, obtained in the procedure for identifying and diagnosing the location of a fault during rated rotational operation, which is part of the rotating machinery diagnostic method according to the first embodiment. [Figure 14] This is a flowchart showing the overall procedure of the rotating machinery diagnostic method according to the second embodiment. [Figure 15] This is a block diagram showing the configuration of a rotating machine diagnostic device according to the third embodiment. [Figure 16] This is an explanatory diagram of the processing by the data processing unit in the rotating machine diagnostic method according to the third embodiment. [Figure 17] This is a first determination table showing the determination conditions in the method for identifying and diagnosing the location of a fault during rated rotational operation, which is part of the rotating machinery diagnostic method according to the third embodiment. [Figure 18] This is a second determination table showing the determination conditions for the method of identifying and diagnosing the location of a fault during rated rotational operation, which is part of the rotating machinery diagnostic method according to the third embodiment. [Figure 19]This is a third determination table showing the determination conditions for the method of identifying and diagnosing the location of a fault during rated rotational operation, which is part of the rotating machinery diagnostic method according to the third embodiment. [Modes for carrying out the invention]

[0013] Hereinafter, an embodiment of the rotating machinery diagnostic device and rotating machinery diagnostic method of the present invention will be described with reference to the drawings. Here, parts that are the same or similar to each other are denoted by the same reference numeral, and redundant explanations will be omitted.

[0014] [First Embodiment] Figure 1 is a block diagram showing the configuration of a rotating machinery diagnostic device 100 according to the first embodiment.

[0015] The rotating machinery diagnostic device 100 includes an input unit 110, a storage unit 120, a calculation unit 130, an output unit 140, and a time counter 150.

[0016] The input unit 110 accepts calculation condition data and state measurement values ​​as input. Here, the calculation condition data is information necessary for the calculation unit 130 to perform calculations. The calculation condition data includes, for example, reference data and influence coefficient matrices, which will be described later, as well as various judgment criterion values ​​necessary for making decisions in the progress of the diagnosis of the rotating machine diagnostic device 100. The state measurement values ​​are the outputs of each detector that measures the state of the rotating part 10a (Figure 2) of the rotating machine 10, including vibration data, rotational speed, phase calculation data for phase calculation, and the output and load of the rotating machine 10. Here, the vibration data is, for example, the amplitude value for each sampling time interval. These details will be explained later with reference to Figure 2.

[0017] The storage unit 120 includes a reference data storage unit 121, an influence coefficient matrix storage unit 122, a vibration data storage unit 123, a phase storage unit 124, and a calculation result storage unit 125.

[0018] The reference data storage unit 121 and the influence coefficient matrix storage unit 122 store the reference data and influence coefficient matrix received by the input unit 110, respectively. The vibration data storage unit 123 stores the vibration data received by the input unit 110. The phase storage unit 124 stores the phase calculation data for phase calculation and the rotational speed received by the input unit 110. The calculation result storage unit 125 stores the calculation results performed by the calculation unit 130, namely the vibration vector, vibration vector difference, polar diagram data, unbalance distribution, vibration mode, etc., which will be described later.

[0019] The calculation unit 130 performs calculations to identify the location of a failure and estimate the cause of the failure in the rotating machine 10 based on the calculation condition data and state measurement values ​​received by the input unit 110. The calculation unit 130 includes a phase calculation unit 131, a vibration vector derivation unit 132, a difference vector calculation unit 133, a polar diagram data creation unit 134, an imbalance calculation unit 135, a failure cause estimation unit 136, and a progress control unit 137.

[0020] The phase calculation unit 131 calculates the phase of the rotating unit 10a based on the phase calculation data. Details will be explained later with reference to Figure 7.

[0021] The vibration vector derivation unit 132 derives vibration vectors based on vibration data. The derived vibration vectors are stored in the calculation result storage unit 125.

[0022] The difference vector calculation unit 133 calculates the vibration vector difference from the vibration vector. The calculated vibration vector difference is stored in the calculation result storage unit 125.

[0023] The polar diagram data creation unit 134 creates polar diagram data using the phase calculated by the phase calculation unit 131 and the vibration vector created by the vibration vector derivation unit 132. The created polar diagram data is stored in the calculation result storage unit 125.

[0024] The unbalance calculation unit 135 calculates the unbalance distribution of the rotation unit 10a based on the vibration vector created by the vibration vector derivation unit 132 and the influence coefficient matrix stored in the influence coefficient matrix storage unit 122. The calculated unbalance distribution is stored in the calculation result storage unit 125.

[0025] The failure cause estimation unit 136 identifies and estimates the failure cause that is causing vibration in the rotating part 10a based on the calculation results stored in the storage unit 120. The failure cause estimation unit 136 stores the determination value for discrimination included in the calculation condition data accepted by the input unit 110.

[0026] The progress control unit 137 controls the sequence of operations of each element of the rotating machinery diagnostic device 100. In other words, it makes the necessary decisions for the progress of the rotating machinery diagnostic method and gives instructions to each element for moving to the next step. The progress control unit 137 stores the decision criteria values ​​necessary for the decisions read by the input unit 110 and uses them for the decisions made by the progress control unit 137.

[0027] Figure 2 is a conceptual diagram showing an example of a rotating part 10a of a rotating machine 10 to which the rotating machine diagnostic device 100 according to the first embodiment is applied.

[0028] Figure 2 shows examples of a steam turbine and a generator as the rotating machine 10. The rotating part 10a has two low-pressure turbines 12, a generator 13, and an excitation device 14, and a rotor shaft 11 that connects them in series. However, the rotating machine 10 is not limited to this. The rotating part 10a is rotatably supported by a plurality of bearings 15.

[0029] A vibration meter 16 is provided near each bearing 15. The vibration meter 16 is VS i The values ​​are displayed as (i=1~M). In addition, a phase detector 17 and a tachometer 18 are provided facing the rotating part 10a. The rotating machine diagnostic device 100, vibration meter 16, phase detector 17, and tachometer 18 constitute the rotating machine diagnostic system 200.

[0030] Figure 3 is a conceptual diagram showing a vibration meter 16 and a phase detector 17 for the rotating part 10a of a rotating machine 10 to which the rotating machine diagnostic device 100 according to the first embodiment is applied. Figure 4 is a conceptual diagram showing a vibration meter 16 for the rotating part of a rotating machine to which the rotating machine diagnostic device according to the first embodiment is applied.

[0031] The vibration meter 16 outputs the time variation of vibration at each detection position. The vibration meter 16 is a non-contact type displacement sensor, such as an eddy current type non-contact sensor that measures the gap between the rotating part 10a and the vibration meter 16.

[0032] Figure 4 illustrates a case where two vibration meters 16 are installed at the same location in the axial direction, at an angle of 90 degrees to each other. Note that the direction may be horizontal and vertical, and the angle is not limited to 90 degrees. In this way, multiple vibration meters 16 may be installed at the same location in the axial direction, but with different directions (direction toward the axis center, angle). In this case, vs i Each of these shall be assigned a different number (i).

[0033] Figure 5 is a conceptual diagram showing a first example of a phase detector 17 for the rotating part 10a of a rotating machine 10 to which the rotating machine diagnostic device 100 according to the first embodiment is applied. Figure 6 is a conceptual diagram showing a second example of a phase detector 17 for the rotating part 10a of a rotating machine 10 to which the rotating machine diagnostic device 100 according to the first embodiment is applied.

[0034] As shown in Figure 3, the phase detector 17 is generally positioned slightly outside the axial position of the rotating part 10a compared to the vibration meter 16. The phase detector 17 is provided to detect the rotation angle, or in other words, the phase, of the rotating part 10a.

[0035] The phase detector 17 is a collective term for a reference marker 17a provided at one circumferential point on the outer surface of the rotor shaft 11 of the rotating part 10a, and a pulse detector 17b provided near the reference marker 17a. Here, as the reference marker 17a, for example, in the first example shown in Figure 5, a slit 10b formed on the surface of the rotating part 10a is used, and in the second example shown in Figure 6, a reflective tape 10c attached to the surface of the rotating part 10a is used.

[0036] The pulse detector 17b detects one pulse for each rotation of the rotating part 10a. That is, the phase detector 17 outputs one pulse for each rotation of the rotating part 10a. If the pulse generation time interval is ΔT and the time counter 150 counts J times during this time, the phase α (degrees) at the jth count after the previous pulse generation, after time Δt has elapsed, is calculated by the following equation (1). α=360·(Δt / ΔT)=360·(j / J) …(1)

[0037] The phase calculation unit 131 performs the above calculations and calculates the phase at the time of peak value generation for the output (time variation of amplitude) of each vibration meter 16. Here, the phase calculation data refers to the pulse signal in this example, but is not limited to this as long as similar calculations can be performed.

[0038] A tachometer 18 is provided at the end of the rotor shaft 11. The tachometer 18 is a collective term for a gear (not shown) provided on the rotor shaft 11 and a rotation speed detector (not shown) provided on the stationary side near the gear. The rotation speed detector converts changes in magnetic transmittance due to irregularities in the gear into a pulse signal and outputs it, for example.

[0039] Figure 7 is an explanatory diagram of a model in an example of a rotating part 10a of a rotating machine 10 to which the rotating machine diagnostic device 100 according to the first embodiment is applied. In Figure 7, the rotating part 10a is simulated by an out-of-plane bending stiffness Gb(x) that depends on x and extends along the axial direction x, and a plurality of nodes n (n=1 to N) distributed along the axial direction x associated with it.

[0040] Figure 8 is a flowchart showing the overall procedure of the rotating machinery diagnostic method according to the first embodiment.

[0041] First, the rotating machine diagnostic device 100 reads the calculation condition data (step S11). Specifically, the input unit 110 accepts the calculation condition data, which includes reference data, the influence coefficient matrix A, and various judgment criterion values, as input. The reference data and the influence coefficient matrix are stored in the reference data storage unit 121 and the influence coefficient matrix storage unit 122, respectively. The various judgment criterion values ​​are stored in the progress control unit 137.

[0042] Next, the rotating machine diagnostic device 100 reads the state measurement values ​​(step S12). Specifically, the input unit 110 receives the output of each detector that measures the state of the rotating part 10a of the rotating machine 10 as input, including vibration data, rotational speed, and phase calculation data for phase calculation. The phase calculation unit 131 calculates the phase of the rotating part 10a based on the phase calculation data and the output of the time counter 150. The vibration data received by the input unit 110 is stored in the vibration data storage unit 123. The rotational speed received by the input unit 110 and the phase calculated by the phase calculation unit 131 are stored in the phase storage unit 124.

[0043] Next, the progress control unit 137 determines whether the rotating machine 10 to be diagnosed is in a rated rotational speed operation state (step S13). Here, rated rotational speed operation refers to operation at the rated rotational speed other than during rotational speed increase or decrease. That is, it refers to the state in which the rated rotational speed has been reached after the rotational speed has increased, the state at the rated rotational speed before the rotational speed decreases, or, if the rotating machine 10 is a power generation device, for example, the state in which it is connected to a power grid and is carrying a load in synchronization with the power grid. Alternatively, if it is an electric motor, it refers to the state in which it is coupled with a load such as a pump or blower and is driving them.

[0044] When it is determined by the progress control unit 137 that the rotary machine 10 is not in the rated rotation operation state (step S13 NO), the process proceeds to the startup / shutdown diagnosis step S20. Also, when it is determined by the progress control unit 137 that the rotary machine 10 is in the rated rotation operation state (step S13 YES), the process proceeds to the rated rotation operation diagnosis step S30.

[0045] First, the startup / shutdown diagnosis step S20 will be described below.

[0046] In the startup / shutdown diagnosis step S20, first, the difference vector, which is the temporal change of the vibration vector, is calculated (step S21). Below, the derivation of the vibration vector by the vibration vector derivation unit 132, which is the first step of calculating the change in the vibration vector, and the calculation of the difference in the vibration vector by the difference vector calculation unit 133, which is the second step, will be sequentially described.

[0047] First, the derivation of the vibration vector, which is the first step, will be described.

[0048] The vibration vector derivation unit 132 calculates each vibration value z k (k = 1 to K) based on the amplitude value obtained from the output of each vibration meter 16 and the respective phases obtained by the phase calculation unit 131, and derives each vibration value z mk (m = 1 to M) as elements of the partial vibration vector Z k in the polar coordinates of the following equation (2) and the x, y coordinate forms shown in (3) and (4). z mk = A mk · exp[j·(Θ mk × π / 180)] = x mk + j·y mk …(2) x mk = A mk · cos(Θ mk × π / 180) …(3) y [[ID=A5]] mk = A mk · sin(Θ mk × π / 18​​m k is the amplitude of the mth vibration at rotational speed n·k, Θ mk is the rotational speed n k The phase [degrees] of the mth vibration value in this case, where j is the imaginary unit.

[0049] Here, the partial vibration vector Z k is z mk This is an M-th order column vector with elements (m=1~M). The vibration vector [Z] is the rotational speed n k Partial vibration vector [Z k The elements of ] are arranged vertically for k=1 to K, forming an M·K order column vector. The vibration vectors [Z] derived by the vibration vector derivation unit 132 are sequentially stored in the calculation result storage unit 125.

[0050] Next, we will explain the calculation of the difference vector ΔZ, which is the difference of the vibration vector Z, by the difference vector calculation unit 133, which is the second step.

[0051] Each vibration value z is an element of the vibration vector Z. mk The temporal changes in xmk, the x-coordinate component, and ymk, the y-coordinate component, of (m=1 to M, k=1 to K) over a certain time interval are calculated using the following equations (5) and (6). Δx mk =x mk (t+Δt)-x mk (t) …(5) Δy mk =y mk (t+Δt)-y mk (t) …(6)

[0052] Furthermore, in polar coordinate form, it is given by the following equations (7) and (8) through (10). ΔA mk =√[(Δx mk ) 2 +(Δy mk ) 2 ] …(7) Here, √[x] represents the square root of x. Φ m k = 90 - tan[(Δx mk / Δymk )·(180 / π)] (Δy mk (If >0) ... (8) Φ mk =0 (Δy mk (If = 0) ... (9) Φ mk =270-tan[(Δx mk / Δy mk )·(180 / π)] (Δy mk (If <0) ... (10)

[0053] The above describes the calculation of the difference vector, which is the temporal change in the vibration vector in step S21. The difference vector [ΔZ] calculated by the difference vector calculation unit 133 is sequentially stored in the calculation result storage unit 125.

[0054] Next, the progress control unit 137 controls ΔA mk (m=1~M, k=1~K) Criterion value ΔA J Compared to the judgment criterion value ΔA J Determine whether there is anything that exceeds that (step S22).

[0055] The progress control unit 137 controls ΔA mk Judgment criterion value ΔA J If it is not determined that there is anything exceeding the limit (step S22 NO), then steps S12 and S13 are repeated.

[0056] The progress control unit 137 controls ΔA mk Judgment criterion value ΔA J If it is determined that there is a value exceeding this (step S22 YES), the unbalance calculation unit 135 derives the unbalance position (step S23).

[0057] Following step S23, the progress control unit 137 determines whether to continue driving or not (step S53). If it is determined that driving should continue (step S53 YES), the process proceeds to step S11. If it is determined that driving should continue (step S53 NO), the progress control unit 137 takes action to stop driving, such as issuing a stop instruction alarm.

[0058] Figure 9 is a flowchart showing the details of the step S23 for deriving the unbalanced position in the procedure for the diagnostic method during startup and shutdown of the rotating machine diagnostic method according to the first embodiment.

[0059] In step S23, which is the derivation step of the unbalanced position, first, the unbalanced calculation unit 135 calculates the unbalanced vector U (step S23a). Specifically, the unbalanced calculation unit 135 calculates the unbalanced vector U based on the influence coefficient matrix A, which is read by the input unit 110 and stored in the influence coefficient matrix storage unit 122, and the vibration vector Z stored in the calculation result storage unit 125.

[0060] First, we will explain the imbalance vector U and the influence coefficient matrix A.

[0061] The unbalance vector U is the unbalance quantity u at each node n (N=1~N). n This is an N-th column vector whose elements are [elements].

[0062] The influence coefficient matrix A is an influence coefficient a whose value is the vibration value that occurs at the m-th vibration meter (m=1 to M) when there is unit imbalance at node n (n=1 to N). mn This is an (M×N matrix) whose elements are .

[0063] From this definition, the vibration vector Z, the influence coefficient matrix A, and the imbalance vector U are related by the following equation (11). Z = A·U …(11)

[0064] The estimation of the unbalance vector U is described below. In estimating the unbalance vector U, there are two cases depending on the relationship between the number of vibration values ​​M obtained by the vibration meter 16 and the number of nodes n, i.e., the number of unbalance positions N.

[0065] The first case is when the number M of vibration values ​​is smaller than the number N of unbalanced positions.

[0066] First, we define the M-th order error vector E by the following equation (12). E=Zt-Z m =A·UZ m …(12)

[0067] Here, the vibration vector Zt represents the vector of the true vibration value that should be obtained by equation (11) resulting from the imbalance vector U, and the vibration vector Zm represents the vector of the vibration value obtained by the vibration meter 16.

[0068] In this case, the least squares method is applied to minimize the error value EE given by equation (13) below. EE=E * W1E+U * W2U …(13)

[0069] W1 is, for example, a diagonal matrix, and the identity matrix may also be used. The second term is a term to avoid unrealistic solutions (distribution of U), and matrix W2 is a diagonal matrix, and the identity matrix may be used first, with the element values ​​adjusted based on the result. Note that each element of the diagonal matrix W2 should be small enough to ensure the accuracy of the calculation of the imbalance vector U, and should also be large enough to stabilize the operation.

[0070] As a result, the imbalance vector U is obtained by the following equation (14). U=(A * W1A+W2) -1 A * W1Z m …(14) Here, A * This is the conjugate transpose (N×M matrix) of the influence coefficient matrix A, which is an (M×N matrix).

[0071] The second case is when the number M of vibration values is greater than the number N of unbalance positions.

[0072] In this case, the Lagrange multiplier λ is introduced to minimize the error value EE by the following equation (15). EE = U * W2U + λ T (AU - Z m ) …(15) <0***0530> As a result, the unbalance vector U is obtained by the following equation (16). <***0532>U = W2 -1 A * (AW2 ―1 A * ) -1 Z<00***072>…(16)

[0074] Next, coordinate transformation is performed on each element u of the calculated unbalance vector U. That is, the values (u n of each element u of the calculated unbalance vector U in the x and Y coordinates (u n are converted to polar coordinates (u nx , u ny ) by the following equations (17) and (18). Here, when the unbalance position is concentrated at one point, u nr is the magnitude of the unbalance, and u nΘ is the circumferential angle of the unbalance location. nr is the magnitude of the unbalance, and u nΘ is the circumferential angle of the unbalance location. u nr = √(u nx 2 + u ny 2 ) …(17) u nΘ = 90 - tan -1 [(u ny / u nx ) × 180 / π] (when u ny > 0) = 270 - tan -1 [(u ny / u nx ) × 180 / π] (when u ny<When < 0) = 0 (u ny = 0, u nx > 0 case) = 180 (u ny = 0, u nx <When < 0) …(18)

[0075] Next, the imbalance calculation unit 135 calculates each element u of the calculated vector U n in the polar coordinate representation of the magnitude u nr and sorts them in descending order (step S23b). Each element u of the vector U sorted and rearranged by the imbalance calculation unit 135 n is stored and remembered in the operation result storage unit 125. The imbalance calculation unit 135, in this order, determines the element number n of the element u of the vector U n and its magnitude u nr and the circumferential direction angle u nΘ and outputs them to the output unit 140.

[0076] In response to this, the output unit 140 alerts the driver or the like (step S23c). Specifically, the output unit 140, for the element number n with the largest magnitude, displays the position of the node n of that element, the magnitude u nr and the circumferential direction angle u nΘ along with an alarm.

[0077] The above is the procedure of the start / stop diagnosis step S20. Next, the rated rotation operation diagnosis step S30 will be described while referring to FIG. 8.

[0078] When it is determined that the rotating machine 10 is in the rated rotation operation state (step S13 YES), the input unit 110 reads the output, that is, the load, of the rotating machine 10 (step S31). Here, the output can be, for example, the output of the power meter for reference or the first-stage pressure of the steam turbine if the rotating machine 10 is a steam turbine and a generator shown in FIG. 2.

[0079] Next, the rotating machine diagnostic device 100 calculates the vibration vector change (step S32). Specifically, the vibration vector derivation unit 132 derives the vibration vector, and the difference vector calculation unit 133 calculates the difference in vibration vectors. The specific details of these steps are the same as those performed in step S21, so a detailed explanation is omitted.

[0080] Next, the rotating machinery diagnostic device 100 identifies the location of the failure and estimates the cause of the failure (step S40). The details of step S40 will be explained below with reference to Figures 10 to 13.

[0081] Figure 10 is a flowchart showing the procedure for identifying and diagnosing the location of a fault during rated rotational operation (step S40) in the rotating machinery diagnostic method according to the first embodiment.

[0082] First, the rotating machine diagnostic device 100 determines whether the vibration vector change is large or not (step S41). Specifically, the progress control unit 137 determines ΔA mk (m=1~M, k=1~K) Criterion value ΔA LJ Compared to the judgment criterion value ΔA LJ Determine whether or not there is something that exceeds this.

[0083] In step S41, the progress control unit 137 controls ΔA mk (m=1~M, k=1~K) within the judgment criterion value ΔA LJ If it is determined that there is something exceeding the limit (step S41 YES), the process proceeds to the next step, S44, which determines whether or not it is an instantaneous vibration change. Step S44, which determines whether or not it is an instantaneous vibration change, will be described later.

[0084] In step S41, the progress control unit 137 controls ΔA mk (m=1~M, k=1~K) within the judgment criterion value ΔA LJIf it is not determined that there is anything exceeding the threshold (step S41 NO), the progress control unit 137 determines whether or not it is continuing the evaluation of thermal cyclic vibration (step S42). The determination of thermal cyclic vibration will be explained in step S49 below.

[0085] If, in step S42, the progress control unit 137 determines that it is continuing to evaluate thermal cyclic vibration (step S42 YES), the process proceeds to step S47, which will be described later.

[0086] In step S42, if the progress control unit 137 does not determine that it is continuing to evaluate thermal cyclic vibration (step S42 NO), the number of processing steps in step S47 (described later) is reset, and since there is no problem, the END process is performed.

[0087] The details of the aforementioned step S44 for determining whether or not the fluctuation is instantaneous will now be explained. First, the difference vector calculation unit 133 calculates a difference vector based on the vibration vectors for each sampling interval derived by the vibration vector derivation unit 132. This is the same as in step S21, so a detailed explanation will be omitted. Next, the progression control unit 137 uses a threshold for determining the instantaneous change amount of the vector to determine whether or not the amount of change is greater than or equal to the threshold.

[0088] In step S44, if the progress control unit 137 determines that the amount of change is greater than or equal to the threshold used to determine the instantaneous change in the vector (step S44 YES), then it performs unbalanced position estimation (step S46). The specific details are the same as in step S23, so the explanation is omitted.

[0089] In step S44, if the progress control unit 137 does not determine that the amount of change is greater than or equal to the threshold used to determine the instantaneous change amount of the vector (step S44 NO), it proceeds to determine whether or not either thermal vibration or cyclic vibration is occurring in the rotating part 10a of the rotating machine 10, and to identify the vibration mode.

[0090] First, it is determined whether the number of processing steps is equal to or greater than a specified value (step S47). Specifically, the progress control unit 137 determines whether the number of processing steps is equal to or greater than a specified value for determination.

[0091] If the progress control unit 137 does not determine that the number of processing steps is equal to or greater than the specified value for determination (step S47 NO), it returns to step S52 and repeats the process. The progress control unit 137 counts the number of times step S47 has been reached as the number of processing steps.

[0092] Thermal and cyclic vibrations are characterized by a continuous change in amplitude and phase values ​​over time. Therefore, continuous measurement data spanning approximately one hour is required to determine whether a vibration is thermal or cyclic. If the specified value for this determination is too small, sufficient data cannot be obtained. Conversely, if the specified value is too large, the time required for determination increases, potentially exacerbating the problem. Therefore, the value of this specified value should be selected within an appropriate range, taking both factors into consideration.

[0093] If the progress control unit 137 determines that the number of processing steps is equal to or greater than a specified value for determination (step S47 YES), it performs thermal cyclic vibration determination preprocessing (step S48). Specifically, the polar diagram data creation unit 134 creates data for creating a polar diagram, and the created polar diagram data is stored in the calculation result storage unit 125 as vibration mode data. In addition, the output unit 140 outputs numerical data for displaying or illustrating the polar diagram.

[0094] Next, a determination is made as to whether or not the cause can be identified (step S49). Specifically, based on the vibration modes identified and stored in the calculation result storage unit 125, the failure cause estimation unit 136 performs a cause evaluation and identification, for example, based on the polar diagram obtained in step S48. The following describes the case based on the polar diagram.

[0095] Figure 11 shows an example of a polar diagram display for thermal vibration obtained in the procedure for identifying and diagnosing the location of a fault during rated rotational operation, which is part of the rotating machinery diagnostic method according to the first embodiment. Each black circle corresponds to the vibration vector at each point in time derived by the vibration vector derivation unit 132. The arrows correspond to the difference vector calculated by the difference vector calculation unit 133. Each point in time may be the value of the vibration value at each sampling interval, or it may be at a predetermined interval. That is, the change in the vector is the difference vector ΔZ or the combination of a predetermined number of difference vectors. These are collectively represented as the difference vector per unit time, which is the vector change v. The rate of change in the direction of the vector change v is represented as the rate of change in direction β. The same applies to Figures 12 and 13.

[0096] The thermal oscillation shown in the polar diagram of Figure 11 is characterized by a vector change v of a magnitude greater than or equal to a predetermined value, and a vector direction change rate β less than or equal to a predetermined value. Furthermore, the trajectory on the polar diagram is not one in which the phase changes continuously and the plot rotates. The failure cause estimation unit 136 uses a stored discrimination value to determine whether or not it is a thermal oscillation.

[0097] Figure 12 shows an example of a polar diagram display in the case of cyclic vibration caused by a hard rub, obtained in the procedure for identifying and diagnosing the location of a fault during rated rotational operation, which is part of the rotating machinery diagnostic method according to the first embodiment.

[0098] The characteristic of cyclic oscillation due to hard labs is that, as shown in Figure 12, the phase changes on the polar diagram, and the plot rotates in an orbit where the magnitude of the vector change changes over time, meaning the plot becomes larger or smaller in the radial direction in a spiral pattern.

[0099] The failure cause estimation unit 136 determines whether the following conditions are met: the magnitude of the vector change v is greater than or equal to a predetermined value, the rate of change of direction β of the vector is greater than or equal to a predetermined value, and the time derivative of the acceleration of the vector's direction change α, i.e., the rate of change of direction β of the vector, is greater than or equal to a predetermined value.

[0100] Figure 13 shows an example of a polar diagram display in the case of cyclic vibration caused by a soft rubbing, obtained in the procedure for identifying and diagnosing the location of a fault during rated rotational operation, which is part of the rotating machinery diagnostic method according to the first embodiment.

[0101] The characteristic of cyclic oscillation due to a soft lab is that, as shown in Figure 13, the phase changes on the polar diagram as the plot rotates, and the magnitude of the vector change over time is small, meaning the plot traces the same circular orbit.

[0102] The failure cause estimation unit 136 determines whether the following conditions are met: the magnitude of the vector change v is greater than or equal to a predetermined value, the rate of change of direction of the vector β is greater than or equal to a predetermined value, and the acceleration of the direction change of the vector α is less than or equal to a predetermined value.

[0103] The failure cause estimation unit 136 makes determinations as needed, in addition to the three cases described above.

[0104] For the sake of explanation, the determination by the failure cause estimation unit 136 has been described in comparison with the polar diagrams shown in Figures 11 to 13. The polar diagrams are displayed by the output unit 140 and help operators understand the system, but they are not necessarily required for determination by the failure cause estimation unit 136. The failure cause estimation unit 136 can directly determine the cause using the vibration vectors derived by the vibration vector derivation unit 132 and stored in the calculation result storage unit 125, and the difference vectors calculated by the difference vector calculation unit 133 and stored in the calculation result storage unit 125.

[0105] The progress control unit 137 determines that the cause can be identified if it matches any of the criteria used by the failure cause estimation unit 136. If it does not match any of the criteria used by the failure cause estimation unit 136, the progress control unit 137 does not determine that the cause can be identified.

[0106] If, in step S49, the progress control unit 137 does not determine whether the cause can be identified (step S49 NO), it outputs "Unable to determine" to the output unit 140, and the output unit 140 displays a message to that effect (step S50). After that, the process proceeds to step S53. Alternatively, the process may proceed directly from step S49 to step S53 to continue the identification process.

[0107] In step S49, if the progress control unit 137 determines that the cause can be identified (step S49 YES), it determines whether or not the cause falls under one of the causal events (step S51). That is, the progress control unit 137 determines whether or not the result of the fault cause estimation unit 136 determines whether or not the case falls under one of the cases shown in Figures 11 to 13.

[0108] If the progress control unit 137 determines in step S51 that one of the causative events has occurred (step S51 YES), the unbalanced position is estimated (step S52). The estimation of the unbalanced position is the same as the calculation of the unbalanced position in step S23, so the explanation is omitted. After the unbalanced position estimation step S52, the process proceeds to step S53.

[0109] If the progress control unit 137 does not determine in step S51 that any of the causative events are present (step S51 NO), the process proceeds directly to step S53.

[0110] In step S53, it is determined whether to continue operation or not. Normally, if an abnormality occurs, operation of the rotating machine 10 is stopped without continuing, but a determination step is provided as a precaution. In this step, the operator may perform the stop operation based on the status displayed using the user interface. Alternatively, the system may automatically decide whether to continue operation or automatically stop operation depending on the cause. If operation is to be continued (step S53 YES), the system proceeds to step S12 in Figure 8.

[0111] As described above, in both cases—during changes in the rotational speed of the rotating machine 10 and during rated rotational speed operation—it becomes possible to identify the location of the failure and estimate the cause of the failure in relation to unbalance events in the rotating part 10a.

[0112] [Second Embodiment] Figure 14 is a flowchart showing the overall procedure of the rotating machinery diagnostic method according to the second embodiment.

[0113] In this embodiment, the vibration vector at critical speeds when the rotational speed of the rotating part 10a changes is extracted and evaluated. For this reason, a step (S24) is added before step S21 to determine whether the rotational speed of the rotating part 10a is at a critical speed.

[0114] When increasing rotational speed, the rotational speed increase is not stopped at the critical speed of the rotating part 10a; it is common practice to pass through the critical speed. On the other hand, if the sampling interval is coarse, it is difficult to collect data at the exact moment when the rotational speed matches. Also, the amplitude and phase values ​​change significantly at the critical speed, and there is a concern that substituting data from before and after this point will worsen the final detection accuracy.

[0115] Given this background, the progress control unit 137 determines that the rotational speed of the rotating unit 10a received by the input unit 110 has passed the critical speed threshold, and the failure factor estimation unit 136 interpolates using the results obtained by the vibration vector derivation unit 132 before and after the critical speed threshold to calculate the amplitude and phase at the critical speed rotational speed.

[0116] In this embodiment, since evaluation is performed at a critical speed where the amplitude value becomes large, the sensitivity to vibration changes when an unbalanced failure event occurs in the rotating part 10a is high, and the accuracy of detection is improved. In addition, the amount of measurement data stored in the memory unit 120 can be reduced.

[0117] [Third Embodiment] Figure 15 is a block diagram showing the configuration of a rotating machine diagnostic device 100a according to a third embodiment. This embodiment is a variation of the first embodiment, and instead of relying on a polar diagram as in the first embodiment, it uses the moving velocity, moving acceleration, and direction change rate of the vibration vector to identify vibration modes and estimate the cause of failure.

[0118] In the rotating machinery diagnostic device 100a, the storage unit 120 further has a determination table storage unit 126, and the calculation unit 130 further has a data processing unit 138, a failure cause estimation unit 136a instead of a failure cause estimation unit 136, and a progress control unit 137a instead of a progress control unit 137. The determination table storage unit 126 stores the determination table. The determination table includes a first determination table 126a, a second determination table 126b, and a third determination table 126c, which will be described later.

[0119] First, the processing performed by the data processing unit 138 will be explained below.

[0120] (1) Before calculating the average value of the phase data, the following preprocessing is performed to avoid the average value becoming an outlier when the phase crosses 0 degrees (360 degrees).

[0121] The phase data to be averaged is Θ1, Θ2, ..., Θ in time series. N For i=2 to N, the following processes are performed in order.

[0122] Θ i -Θ i-1 >180 degrees, Θ i Replace the following: Θ i -360 × int((Θi -Θ i-1 +180) / 360) Θ i -Θ i-1 For a degree less than 180 degrees, Θ i Replace the following: Θ i +360 × int((Θ i -Θ i-1 +180) / 360) If neither applies, then Θ i Leave it as is. However, int(x) represents the largest integer not exceeding x.

[0123] (2) Calculate the average value of amplitude a and phase Θ from the past N data points. Here, m represents the data number for the current time. The phase Θ is the value preprocessed in (1).

[0124] The average number of repetitions is the value of Ave_Count, which will be denoted as N in the following equations (19) and (20). A = Σa i / N …(19) Θ=ΣΘ i / N …(20)

[0125] Here, Σ represents the sum from i=m-N+1 to i=m.

[0126] (3) The averaged amplitude a and phase Θ are converted to Cartesian coordinate values ​​X and Y using the following equations (21) and (22). X = Acos(Θ × π / 180) …(21) Y = Asin(Θ × π / 180) …(22)

[0127] (4) From the obtained average value, the change in the vibration vector is calculated using the following equations (23) and (24). ΔX i =X i -X i-1 …(twenty three) ΔY i =Y i -Y i-1 …(twenty four)

[0128] Figure 16 is an explanatory diagram of the processing by the data processing unit in the rotating machine diagnostic method according to the third embodiment.

[0129] For example, if N=20, X i , Y i X is the average value of the 20 trials from 19 trials ago to the present. i―1 , Y i―1 X is the average value of the last 20 trials, from 39 trials ago to 20 trials ago. i―2 , Y i―2 ΔX is the average value over the last 20 trials, from 59 trials ago to 40 trials ago, while ΔX and ΔY are the differences between them.

[0130] (5) Movement speed v on the polar diagram j (Magnitude of vector change) and acceleration α i The calculation is performed using the following formulas (25) to (27). v i =(√[(ΔX i ) 2 +(ΔY i ) 2 〕) / (N·Δt) …(25) v i―1 =(√[(ΔX i―1 ) 2 +(ΔY i―1 ) 2 〕) / (N·Δt) …(26) α i =(v i -v i-1 ) / (N·Δt) …(27) However, Δt is the data sampling period (in minutes), and N is the average count.

[0131] (6) Direction of vector change Φ i , and the rate of change of direction β i Perform the calculation.

[0132] ΔX obtained in (4) above i ΔY i Therefore, the direction of vector change Φ is given by the following equation (28). j Calculate. Φ i= 90 - tan ―1 [(ΔX i / ΔY i ) × (180 / π) (ΔY i (If >0) = 270 - tan ―1 [(ΔX i / ΔY i ) × (180 / π) (ΔY i (If <0) =0 Y i =0, ΔX i (If >0) =180 Y i =0, ΔX i (If <0) ... (28)

[0133] Change of direction ΔΦ i This is calculated using the following formula (29), taking into account cases where the angle crosses 0 degrees (360 degrees) before and after. ΔΦ i =Φ i -Φ i-1 (-180≦Φ i -Φ i-1 (If ≤ 180) =Φ i -Φ i-1 -360 (Φ i -Φ i-1 (If >180) =Φ i -Φ i-1 +360 (Φ i -Φ i-1 (If <180) ... (29)

[0134] The obtained ΔΦ i Therefore, the rate of change of direction β per unit time (e.g., 1 minute) i This is calculated using the following formula (30). β i =ΔΦ i / (N·Δt) …(30)

[0135] Figure 17 shows a first determination table 126a, which indicates the determination conditions in the method for identifying and diagnosing the location of a fault during rated rotational operation, which is part of the rotating machinery diagnostic method according to the third embodiment. The first determination table 126a is housed in the determination table storage unit 126. This determination table is used in the step corresponding to step S49 in Figure 10.

[0136] The velocity v of the oscillation vector on the polar diagram obtained as described above i , moving acceleration α i , direction change rate β i Using this, a determination is made according to the first determination table 126a.

[0137] The first column of the first judgment table 126a, which includes items for thermal vibration, cyclic vibration (hard rubbing, soft rubbing), and neither, is the classification of the cause. Columns 2 through 4 are the judgment parameters, specifically the movement speed v. i , moving acceleration α i , direction change rate β i The fifth to seventh columns show the judgment results. In the first judgment table 126a, "add 1" means that 1 is added to the previous judgment result read as the input value. In other words, the numerical value of the judgment result indicates the number of times the judgment has been performed consecutively.

[0138] The threshold values ​​(v0, β0, α0) used for determination can be set individually for each vibration meter 16.

[0139] The failure cause estimation unit 136a performs the following determination process according to the first determination table 126a shown in Figure 17. Here, the failure cause estimation unit 136a stores counters for thermal vibration determination, cyclic vibration (hard lab) determination, and cyclic vibration (soft lab) determination for each vibration meter 16. (1)v i ≧v t0 , β i If β0 is ≤ β0, add 1 to the thermal vibration determination. (2)v i ≧v c0 , βi ≥β0, α i If ≥α0, add 1 to the cyclic vibration (hard rub) determination. (3)v i ≧v c0 , β i ≥β0, α i If α0 is true, add 1 to the cyclic vibration (soft rubbing) detection result. (4)(v i <v t0 , β i <β0) or (v i <v c0 , β i If ≥β0, then none of the above applies.

[0140] Figure 18 is a second determination table showing the determination conditions in the method for identifying and diagnosing the location of a fault during rated rotational operation, which is part of the rotating machinery diagnostic method according to the third embodiment.

[0141] The second determination table 126b supplements the conditions used when making a determination using the first determination table 126a in step S49. Specifically, in thermal vibration determination, the maximum value of the thermal vibration determination result for each bearing and direction is used. In cyclic vibration (hard lab) determination, the maximum value of the cyclic vibration (hard lab) determination result for each bearing and direction is used. In cyclic vibration (soft lab) determination, the maximum value of the cyclic vibration (soft lab) determination result for each bearing and direction is used. Furthermore, the processing for each of the above determinations is as follows (a) and (b).

[0142] (a) If the number of judgment results of the above three items that are judged as 0 is 0 or 1, the judgment result indicates that two or all of the vibration events from thermal vibration, cyclic vibration hard lab, and cyclic vibration soft lab are occurring. In this case, 1 is added to the judgment that the cause is being judged. Note that the "Judgment result in the process of evaluating the cause" in the third judgment table shown in Figure 19 will be 1 or 2, and "Do nothing" will be output for observation. After that, the evaluation cycle is run again, and if this judgment is made three times, the process of outputting "Not judgeable" will be initiated.

[0143] (b) If the number of judgment results of the above three items that are judged as 0 is 2 or 3, the judgment result will be 0. In this case, the judgment will be either that only one of the thermal vibration, cyclic vibration hard lab, or cyclic vibration soft lab is occurring, or that none of them are occurring. In this case, the "Judgment result during factor evaluation" in the third judgment table shown in Figure 19 will be 0, and a new judgment will be made.

[0144] Figure 19 is a third determination table showing the determination conditions in the method for identifying and diagnosing the location of a fault during rated rotational operation, which is part of the rotating machinery diagnostic method according to the third embodiment.

[0145] The progress control unit 137a performs an overall determination based on the determination results from the first determination table 126a and the second determination table 126b, according to the third determination table 126c shown in Figure 18, as follows. That is, the third determination table 126c is used at the stage corresponding to step S51 in Figure 10.

[0146] The third judgment table 126c shown in Figure 19 provides an example for thermal vibration. Similar tables are also available for cyclic vibration (soft lab) and cyclic vibration (hard lab), but their explanations will be omitted. The following describes an example for thermal vibration.

[0147] The first column of the third determination table 126c is the determination result during the factor evaluation for thermal vibration in step S49, with each row being the classification of the number of occurrences. The second column is the determination result for other factors besides thermal vibration. The third column shows the processing for each case.

[0148] In all bearing directions, if the thermal vibration determination result is 0, and the cyclic vibration (hard lab) determination is 0, the cyclic vibration (soft lab) determination is 0, and the thermal vibration determination is 0, then output a message indicating that nothing will be done. If the cyclic vibration (hard lab) determination is greater than 0, the cyclic vibration (soft lab) determination is greater than 0, or the thermal vibration determination is greater than 0, then proceed to the unbalanced position estimation step.

[0149] For all bearings and directions, if the number of thermal vibration checks is 1 or 2, output a message indicating that no action will be taken.

[0150] If the number of thermal vibration assessments for any bearing and direction is 3 or more, a message indicating "assessment impossible" will be output.

[0151] For all bearings and directions, if the number of times the event has been judged is 1 or 2, output a message indicating that nothing will be done.

[0152] If the number of attempts to determine the event is 3 or more for any bearing and direction, a message indicating that determination is not possible will be output.

[0153] The vibration mode determination results from the first determination table 126a, the second determination table 126b, and the third determination table 126c, as described above, are stored in the calculation result storage unit 125 and can be output and displayed by the output unit 140.

[0154] As described above, in this embodiment, the judgment table is stored, enabling efficient judgment.

[0155] According to the embodiments described above, it is possible to provide a rotating machinery diagnostic device and a rotating machinery diagnostic method that enable the identification of the failure location and estimation of the failure cause in relation to rotor unbalance events. [Other embodiments]

[0156] Although several embodiments of the present invention have been described above, these embodiments are presented as examples and are not intended to limit the scope of the invention. Furthermore, since each embodiment is not mutually exclusive, features of multiple or all embodiments may be combined. These novel embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims of the invention and its equivalents. [Explanation of Symbols]

[0157] 10...Rotating machinery, 10a...Rotating part, 10b...Slit, 10c...Reflective tape, 11...Rotor shaft, 12...Low-pressure turbine, 13...Generator, 14...Excitation device, 15...Bearing, 16...Vibration meter, 17...Phase detector, 17a...Reference marker, 17b...Pulse detector, 18...Tachometer, 100...Rotating machinery diagnostic device, 110...Input unit, 120...Storage unit, 121...Reference data storage unit, 121a...Decision table, 122...Influence coefficient Matrix storage unit, 123... Vibration data storage unit, 124... Phase storage unit, 125... Calculation result storage unit, 126... Decision table storage unit, 130... Calculation unit, 131... Phase calculation unit, 132... Vibration vector derivation unit, 133... Difference vector calculation unit, 134... Polar diagram data creation unit, 135... Imbalance calculation unit, 136... Failure factor estimation unit, 137... Progress control unit, 138... Data processing unit, 140... Output unit, 150... Time counter

Claims

1. A rotating machine diagnostic device for identifying the location of a failure and estimating the cause of a failure in relation to an unbalance event occurring during the rotation of a rotating part included in a rotating machine, The rotating machine is equipped with a vibration meter located near each bearing, and a rotation speed meter and a phase detector located opposite the rotating part of the rotating machine. The aforementioned rotating machine diagnostic device is An input unit that accepts data of the influence coefficient matrix, vibration data from the vibration meter, rotational speed of the rotating part from the tachometer, and phase calculation data from the phase detector for calculating the phase of the rotating part, Based on the phase calculation data received in the input unit, the phase is calculated. Using the phase and vibration data, the vibration vector is derived. Based on the vibration vector, the velocity, acceleration, and rate of change of direction of the vibration vector are calculated. Based on the moving speed, the moving acceleration, and the rate of change in direction, the vibration mode of the rotating part for estimating the cause of failure is identified from among a plurality of vibration modes, A calculation unit that performs calculations to identify the location of the failure by calculating the unbalance distribution of the rotating part based on the vibration vector and the influence coefficient matrix, The calculation unit displays the location of the fault, An output unit that displays information related to the failure cause based on the vibration mode obtained by the calculation unit, A rotating machinery diagnostic device characterized by comprising the following features.

2. The rotating machine diagnostic device according to claim 1, characterized in that during the operation of the rotating machine, the rotational speed of the rotating part is passing through a critical speed.

3. A method for diagnosing a rotating machine, which involves identifying the location of a failure and estimating the cause of a failure in relation to an unbalance event occurring during the rotation of a rotating part included in a rotating machine, The rotating machine is equipped with a vibration meter located near each bearing, and a rotation speed meter and a phase detector located opposite the rotating part of the rotating machine. The aforementioned rotating machinery diagnostic method is, The input unit has a first input step in which it accepts the data of the influence coefficient matrix, The input unit includes a second input step in which it receives vibration data from the vibration meter, the rotational speed of the rotating part from the tachometer, and phase calculation data from the phase detector for calculating the phase of the rotating part, Based on the phase calculation data received in the input unit, the phase is calculated. Using the phase and vibration data, the vibration vector is derived. Based on the vibration vector, the velocity, acceleration, and rate of change of direction of the vibration vector are calculated. Based on the moving speed, the moving acceleration, and the rate of change in direction, the vibration mode of the rotating part for estimating the cause of failure is identified from among a plurality of vibration modes, A calculation step in which a calculation is performed to identify the location of the failure by calculating the unbalance distribution of the rotating part based on the vibration vector and the influence coefficient matrix, The location of the fault obtained in the calculation step is displayed, A display step that displays information related to the failure cause based on the vibration mode obtained in the calculation step, A method for diagnosing rotating machinery, characterized by having the following features.

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