A method for detecting axial displacement of a high-pressure cylinder of a steam turbine

Abstract

A kind of steam turbine high-pressure cylinder axial displacement detection method belongs to the field of power plant steam turbine operating state monitoring.Solve the existing steam turbine high-pressure cylinder axial displacement detection lacks the problem of accurate monitoring of expansion state.The displacement sensor is used in the present application, the axial displacement of cylinder front bearing seat is collected, and the axial displacement of front bearing seat is determined using the displacement signal;Temperature sensor is used to collect the metal temperature of No.2 bearing, No.3 bearing, No.4 bearing, No.5 bearing, No.6 bearing and No.7 bearing, the steam supply temperature of shaft seal, the metal temperature of regulating stage, the inner wall temperature of steam chamber valve shell and the metal temperature of positive thrust pad;Spearman coefficient analysis method is used to analyze the correlation between the axial displacement of front bearing seat and each temperature, and the theoretical value of the axial displacement of front bearing seat is calculated;Whether the expansion of left and right sides of steam turbine front box is abnormal is judged according to the theoretical value of the axial displacement of front bearing seat.The present application is suitable for steam turbine high-pressure cylinder axial displacement detection.

Description

Technical Field

[0001] This invention belongs to the field of power plant steam turbine operation status monitoring. Background Technology

[0002] With the continuous grid connection of new energy sources, thermal power units face more complex peak-shaving tasks, and frequent start-ups and shutdowns pose significant safety hazards. For example, during unit startup, a warm-up process ensures sufficient expansion of the turbine cylinder and rotor to prevent dynamic and static rubbing. The longitudinal pins, transverse pins, vertical pins, and cat's paw transverse pins on the turbine cylinder ensure controllable expansion in all directions, collectively forming the unit's sliding pin system. However, installation errors or foreign objects on the contact surface can hinder the normal expansion of the sliding pins, leading to misalignment between the cylinder and rotor, resulting in dynamic and static rubbing and increased vibration. Therefore, monitoring of the turbine's various sliding pin devices has gradually attracted attention.

[0003] CN 213932351 U proposes a redundant monitoring method for turbine differential expansion, improving the reliability of turbine differential expansion measurement devices. CN 110261114 A proposes a turbine high-pressure differential expansion measurement device and a method for monitoring the expansion displacement of the front bearing housing. It uses two displacement sensors to measure the displacement of the cylinder's claw to monitor the expansion of the high- and intermediate-pressure cylinder. However, it only uses the absolute displacement of the cylinder body to achieve simple monitoring of cylinder expansion, lacking in-depth analysis of abnormal expansion faults, such as the faults characterized by deviations between the two displacement measuring points. CN 109933048 A, CN 107313816 B, CN 203177799U, CN 205503198U, and CN 107956518B also propose monitoring cylinder expansion by adding displacement sensors, but similarly lack quantitative descriptions of the expansion state (normal / abnormal). The above methods simply involve adding displacement sensors to monitor the displacement of the cylinder or rotor, lacking a quantitative description of the monitoring method. Summary of the Invention

[0004] This invention addresses the problem of insufficient accurate monitoring of expansion state in existing steam turbine intermediate and high pressure cylinder axial displacement detection methods, and proposes a method for detecting the axial displacement of steam turbine intermediate and high pressure cylinders.

[0005] The present invention provides a method for detecting the axial displacement of a steam turbine's intermediate and high-pressure cylinders, specifically comprising:

[0006] Step 1: Two displacement sensors are installed opposite each other on the left and right sides of the turbine front box to measure the axial displacement of the cylinder front bearing housing and obtain two displacement signals L1 and L2; the axial displacement L of the front bearing housing is determined using the displacement signals L1 and L2.

[0007] Temperature sensors were used to collect the metal temperature signals T2, T3, T4, T5, T6, and T7 of bearings 2, 3, 4, 5, 6, and 7, as well as the steam supply temperature signal T for the shaft seal. ss Regulating metal temperature signal T gs Steam chamber valve shell inner wall temperature signal T vs and the positive thrust bearing metal temperature signal T0;

[0008] Step 2: Use Spearman coefficient analysis to perform correlation analysis on the 10 temperature signals collected in Step 1 to obtain correlation combinations;

[0009] Step 3: Calculate the correlation coefficient between the 10 temperature signals collected in Step 1 and the axial displacement L of the front bearing housing. Using the correlation combination described in Step 2, obtain one or more temperature signals related to the axial displacement L of the front bearing housing.

[0010] Step 4: Perform curve fitting on one or more temperature signals mentioned in Step 3 and the axial displacement L of the front bearing housing to obtain the polynomial relating the one or more temperature signals to the axial displacement L of the front bearing housing.

[0011] Step 5: Using the relational polynomial described in Step 4, calculate the theoretical value L of the axial displacement L of the front bearing housing. pr ;

[0012] Step 6: Utilize the theoretical value L of the axial displacement L of the front bearing housing. pr The system uses the current displacement signals L1 and L2, the real-time axial displacement, and the real-time changes in displacement signals L1 and L2 to determine whether the turbine axial displacement and front box expansion are abnormal, thus enabling the monitoring of the axial displacement of the turbine's high and medium pressure cylinders.

[0013] Furthermore, in this invention, the specific method for determining whether the axial expansion of the turbine and the front casing is abnormal in step six is ​​as follows:

[0014] Step 61: Record the results within 1 minute | L i |>|L pr The number of displacement data m of |+3σ is used. When m / M≥0.75, the axial expansion of the unit is determined to be abnormal; otherwise, the axial expansion of the unit is normal, and step six-two is executed.

[0015] Among them, L i σ represents the axial displacement of the bearing housing at time i; σ represents the standard deviation of the deviation sequences ε of displacement signals L1 and L2; and M is the total number of displacement data obtained within 1 minute.

[0016] Step 62: Record the results within 1 minute. The number of data points t; when t / M≥0.75, the expansion of the measuring point on the left side of the turbine front box is determined to be abnormal;

[0017] At the same time, record The number of data points s; when s / M≥0.75, the expansion of the measuring point on the right side of the turbine front box is determined to be abnormal; among which, and These represent the displacement changes measured by two displacement sensors on the left and right sides of the turbine front casing at time i. This represents the value of the displacement signal L1 at time i. ζ1 and ζ2 represent the value of displacement signal L2 at time i; ζ1 and ζ2 represent the standard deviations of the measured displacement change sequences at the two measuring points, respectively.

[0018] Otherwise, the turbine front box expansion is considered normal.

[0019] Furthermore, in this invention, in step six-one, at time i, the axial displacement L of the bearing housing... i for:

[0020]

[0021]

[0022]

[0023] in, This represents the value of the displacement signal L1 at time i-1. The value of displacement signal L2 at time i-1.

[0024] Furthermore, in this invention, in step six-one, the deviation sequence ε of displacement signals L1 and L2 is:

[0025]

[0026] In this context, the subscripts 1 and 2 of L represent the displacement sensor numbers on the left and right sides of the turbine front casing, respectively; the superscript N represents time; and ε 0 ,ε 1 ,…,ε j ,…ε N These represent the deviations of the left and right displacement sensors in the turbine front box from time 0 to time N.

[0027] Furthermore, in this invention, the method for determining the axial displacement L of the front bearing housing using L1 and L2 in step one is as follows:

[0028] L = (L1 + L2) / 2.

[0029] Furthermore, in this invention, in step two, the method for performing correlation analysis on the 10 temperature signals acquired in step one to obtain the correlation combination is as follows:

[0030] Using the Spearman coefficient analysis method, the correlation coefficients between the 10 temperature signals in step one are calculated. Nine correlation coefficients are obtained for each temperature signal. The correlation coefficient with the largest absolute value is selected from the nine correlation coefficients corresponding to each temperature signal, and ten correlation coefficients with the largest absolute values ​​are obtained. Values ​​greater than 0.95 are extracted from the ten correlation coefficients with the largest absolute values, and the two temperature signals corresponding to the values ​​greater than 0.95 are considered as a correlation combination.

[0031] Furthermore, in this invention, the specific method for obtaining one or more temperature signals related to the axial displacement L of the front bearing housing in step three is as follows:

[0032] Step 3: 1. Use Spearman coefficient analysis to calculate the correlation coefficient between the axial displacement L of the front bearing housing and the 10 temperature signals from Step 1; filter out temperature signals with an absolute correlation coefficient greater than 0.8.

[0033] Step 32: From the temperature signals with an absolute correlation coefficient greater than 0.8 mentioned in Step 31, search for temperature signals that are mutually correlated. If they are, delete the temperature signals with a smaller correlation coefficient with the axial displacement L of the front bearing housing from the correlation combination, and obtain one or more temperature signals related to the axial displacement L of the front bearing housing.

[0034] Furthermore, in this invention, in step three-one, the Spearman coefficient analysis method is used to calculate the correlation coefficient between the axial displacement L of the front bearing housing and the 10 temperature signals in step two, which is the same method.

[0035] The method for calculating the correlation coefficient between the axial displacement L of the front bearing housing and the temperature T2 of bearing No. 2 is as follows:

[0036] Step 3: Sample the temperature signal T2 of bearing No. 2 at n time points to obtain n temperature sample values; arrange the n sample values ​​in descending order to obtain a sequence A of n temperature values.

[0037] Samples are taken from two displacement sensors on the left and right sides of the turbine front box at n time points. Each displacement sensor acquires n displacement sample values ​​and calculates n axial displacement L values ​​of the front bearing housing. The n axial displacement L values ​​of the front bearing housing are arranged in descending order to obtain the sequence B of n displacement values.

[0038] Step 3.12: Using the sequence of n temperature values ​​A and the sequence of n displacement values ​​B, calculate the correlation coefficient between the axial displacement L of the front bearing housing and the temperature T2 of bearing No. 2.

[0039] d i =T i '-L' i

[0040]

[0041] Where, ρ s This represents the correlation coefficient between the axial displacement L of the front bearing housing and the temperature T2 of bearing No. 2. i ' represents the position of the temperature value at time i in sequence A, L' i This indicates the position of the displacement value at time i in sequence B.

[0042] The method described in this invention can realize real-time monitoring of the axial displacement of the high-pressure cylinder of a steam turbine. Furthermore, based on the real-time monitoring data, an abnormal axial displacement monitoring method is proposed to accurately locate the abnormal displacement point, which facilitates fault diagnosis by maintenance personnel in the later stage. Moreover, it is easy to implement in actual production and has great value for large-scale practical application. Attached Figure Description

[0043] Figure 1 This is a flowchart of the axial displacement detection method for the high-pressure and intermediate-pressure cylinder of a steam turbine according to the present invention;

[0044] Figure 2 This is a top view of the positions where the two displacement sensors of the present invention are set. In the figure, measuring point 1 and measuring point 2 respectively represent the positions where the two displacement sensors are set.

[0045] Figure 3 This is a front view of the positions where the two displacement sensors of the present invention are set. In the figure, measuring point 1 and measuring point 2 respectively represent the positions where the two displacement sensors are set. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0047] Specific implementation method one: Refer to Figures 1 to 3 This embodiment specifically describes a method for detecting the axial displacement of a steam turbine's intermediate and high-pressure cylinders, comprising:

[0048] Step 1: Two displacement sensors are installed opposite each other on the left and right sides of the turbine front box to measure the axial displacement of the cylinder front bearing housing and obtain two displacement signals L1 and L2; the axial displacement L of the front bearing housing is determined using the displacement signals L1 and L2.

[0049] Temperature sensors were used to collect the metal temperature signals T2, T3, T4, T5, T6, and T7 of bearings 2, 3, 4, 5, 6, and 7, as well as the steam supply temperature signal T for the shaft seal. ss Regulating metal temperature signal T gs Steam chamber valve shell inner wall temperature signal T vs and the positive thrust bearing metal temperature signal T0;

[0050] Step 2: Use Spearman coefficient analysis to perform correlation analysis on the 10 temperature signals collected in Step 1 to obtain correlation combinations;

[0051] Step 3: Calculate the correlation coefficient between the 10 temperature signals collected in Step 1 and the axial displacement L of the front bearing housing. Using the correlation combination described in Step 2, obtain one or more temperature signals related to the axial displacement L of the front bearing housing.

[0052] Step 4: Perform curve fitting on one or more temperature signals mentioned in Step 3 and the axial displacement L of the front bearing housing to obtain the polynomial relating the one or more temperature signals to the axial displacement L of the front bearing housing.

[0053] Step 5: Using the relational polynomial described in Step 4, calculate the theoretical value L of the axial displacement L of the front bearing housing. pr ;

[0054] Step 6: Utilize the theoretical value L of the axial displacement L of the front bearing housing. pr The system uses the current displacement signals L1 and L2, the real-time axial displacement, and the real-time changes in displacement signals L1 and L2 to determine whether the turbine axial displacement and front box expansion are abnormal, thus enabling the monitoring of the axial displacement of the turbine's high and medium pressure cylinders.

[0055] Furthermore, in this invention, the specific method for determining whether the axial expansion of the turbine and the front casing is abnormal in step six is ​​as follows:

[0056] Step 61: Record the results within 1 minute | L i |>|L pr The number of displacement data m of |+3σ is used. When m / M≥0.75, the axial expansion of the unit is determined to be abnormal; otherwise, the axial expansion of the unit is normal, and step six-two is executed.

[0057] Among them, Li σ represents the axial displacement of the bearing housing at time i; σ represents the standard deviation of the deviation sequences ε of displacement signals L1 and L2; and M is the total number of displacement data obtained within 1 minute.

[0058] Step 62: Record the results within 1 minute. The number of data points t; when t / M≥0.75, the expansion of the measuring point on the left side of the turbine front box is determined to be abnormal;

[0059] At the same time, record The number of data points s; when s / M≥0.75, the expansion of the measuring point on the right side of the turbine front box is determined to be abnormal; among which, and These represent the displacement changes measured by two displacement sensors on the left and right sides of the turbine front casing at time i. This represents the value of the displacement signal L1 at time i. ζ1 and ζ2 represent the value of displacement signal L2 at time i; ζ1 and ζ2 represent the standard deviations of the measured displacement change sequences at the two measuring points, respectively.

[0060] Otherwise, the turbine front box expansion is considered normal.

[0061] Furthermore, in this invention, in step six-one, at time i, the axial displacement L of the bearing housing... i for:

[0062]

[0063]

[0064]

[0065] in, This represents the value of the displacement signal L1 at time i-1. The value of displacement signal L2 at time i-1.

[0066] Furthermore, in this invention, in step six-one, the deviation sequence ε of displacement signals L1 and L2 is:

[0067]

[0068] In this context, the subscripts 1 and 2 of L represent the displacement sensor numbers on the left and right sides of the turbine front casing, respectively; the superscript N represents time; and ε 0 ,ε 1 ,…,ε j ,…ε N These represent the deviations of the left and right displacement sensors in the turbine front box from time 0 to time N.

[0069] In this invention, ζ1 and ζ2 are obtained by calculating the time series of the axial displacement of the turbine front bearing housing obtained during the normal start-up and shutdown process of the unit, as obtained in step one. This is based on the measurement deviation sequence of the two measuring points.

[0070] Calculate the sequence of changes in the measured displacement at two measuring points (the displacement measurement deviation at the current moment minus the displacement measurement deviation at the previous moment) to obtain... Calculate the standard deviations ζ1 and ζ2 of the two sequences.

[0071] Furthermore, in this invention, the method for determining the axial displacement L of the front bearing housing using L1 and L2 in step one is as follows:

[0072] L = (L1 + L2) / 2.

[0073] Furthermore, in this invention, in step two, the method for performing correlation analysis on the 10 temperature signals acquired in step one to obtain the correlation combination is as follows:

[0074] Using the Spearman coefficient analysis method, the correlation coefficients between the 10 temperature signals in step one are calculated. Nine correlation coefficients are obtained for each temperature signal. The correlation coefficient with the largest absolute value is selected from the nine correlation coefficients corresponding to each temperature signal, and ten correlation coefficients with the largest absolute values ​​are obtained. Values ​​greater than 0.95 are extracted from the ten correlation coefficients with the largest absolute values, and the two temperature signals corresponding to the values ​​greater than 0.95 are considered as a correlation combination.

[0075] The 10 temperature signals mentioned in this real-time method are the metal temperature signals T2, T3, T4, T5, T6, and T7 of bearings 2, 3, 4, 5, 6, and 7, and the shaft seal steam supply temperature signal T. ss Regulating metal temperature signal T gs Steam chamber valve shell inner wall temperature signal T vs And the positive thrust bearing metal temperature signal T0.

[0076] Furthermore, in this invention, the specific method for obtaining one or more temperature signals related to the axial displacement L of the front bearing housing in step three is as follows:

[0077] Step 3: 1. Use Spearman coefficient analysis to calculate the correlation coefficient between the axial displacement L of the front bearing housing and the 10 temperature signals from Step 1; filter out temperature signals with an absolute correlation coefficient greater than 0.8.

[0078] Step 32: From the temperature signals with an absolute correlation coefficient greater than 0.8 mentioned in Step 31, search for temperature signals that are mutually correlated. If they are, delete the temperature signals with a smaller correlation coefficient with the axial displacement L of the front bearing housing from the correlation combination, and obtain one or more temperature signals related to the axial displacement L of the front bearing housing.

[0079] Furthermore, in this invention, in step three-one, the Spearman coefficient analysis method is used to calculate the correlation coefficient between the axial displacement L of the front bearing housing and the 10 temperature signals in step two, which is the same method.

[0080] The method for calculating the correlation coefficient between the axial displacement L of the front bearing housing and the temperature T2 of bearing No. 2 is as follows:

[0081] Step 3: Sample the temperature signal T2 of bearing No. 2 at n time points to obtain n temperature sample values; arrange the n sample values ​​in descending order to obtain a sequence A of n temperature values.

[0082] Samples are taken from two displacement sensors on the left and right sides of the turbine front box at n time points. Each displacement sensor acquires n displacement sample values ​​and calculates n axial displacement L values ​​of the front bearing housing. The n axial displacement L values ​​of the front bearing housing are arranged in descending order to obtain the sequence B of n displacement values.

[0083] Step 3.12: Using the sequence of n temperature values ​​A and the sequence of n displacement values ​​B, calculate the correlation coefficient between the axial displacement L of the front bearing housing and the temperature T2 of bearing No. 2.

[0084] The correlation coefficient between the axial displacement L of the front bearing housing and the temperature T2 of bearing No. 2 is:

[0085]

[0086] d i =T i '-L' i

[0087] Where, ρ s This represents the correlation coefficient between the axial displacement L of the front bearing housing and the temperature T2 of bearing No. 2. i ' represents the position of the temperature value at time i in sequence A, L' i This indicates the position of the displacement value at time i in sequence B.

[0088] In this invention, when calculating the correlation coefficient between the axial displacement L of the front bearing housing and various temperatures, there is one axial displacement L and 10 temperature measurement points. The data from each temperature measurement point and L form a pair of data used to calculate the Spearman coefficient between that temperature measurement point and L. The number of Spearman coefficients corresponds to the number of data pairs. In this paper, there are 10 pairs, corresponding to 10 Spearman coefficients. The next step is to select the Spearman coefficients with an absolute value greater than or equal to 0.8 from these 10 as the dependent variable. Specific implementation examples:

[0090] Install the measuring points according to step one, and collect data according to step two. Calculate the Spearman correlation coefficient according to step three. The results are shown in Table 1.

[0091] Table 1. Spearman Correlation Coefficient Analysis of Axial Displacement under Start-up Conditions (Optimized)

[0092] Steam chamber valve body inner wall temperature -0.98673 Regulated metal temperature -0.98666 Forward thrust bearing metal temperature -0.94484 #5 Bearing Metal Temperature -0.80017

[0093] Based on the table above, the steam chamber valve shell inner wall temperature, regulating stage metal temperature, positive thrust bearing metal temperature, and #5 bearing metal temperature were selected as dependent variables for curve fitting. The fitting results are as follows:

[0094] L pr =f(T)=0.0108T5-0.0084T0-0.0004T gs -9.129×10 -05 T vs -0.2422

[0095] The standard deviation of the above sequence is calculated as σ = 0.047563.

[0096] Calculate the predicted axial displacement value, compare it with the measured value, and statistically analyze 60 data points within 1 minute. m / M=1≥0.75, indicating that the axial expansion of the unit is normal.

[0097] Table 2 Predicted values ​​of axial displacement

[0098]

[0099]

[0100] Calculate the standard deviations ζ1 = 0.001203, ζ2 = 0.00088.

[0101] Table 3 Test Values

[0102]

[0103]

[0104] Follow step six to count the data points within 1 minute (total data points are T). The number of data points t, the measured value 1 t / T=1≥0.75 indicates that the expansion of the left side of the turbine front box (measuring point 1) is normal. The measured value 2 t / T=0.98≥0.75 indicates that the expansion of the right side of the turbine front box (measuring point 1) is normal.

[0105] While the invention has been described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the invention. Therefore, it should be understood that many modifications can be made to the exemplary embodiments, and other arrangements can be designed without departing from the spirit and scope of the invention as defined by the appended claims. It should be understood that different dependent claims and features described herein can be combined in ways different from those described in the original claims. It is also understood that features described in conjunction with individual embodiments can be used in other described embodiments.

Claims

1. A method for detecting the axial displacement of a steam turbine's intermediate and high-pressure cylinders, characterized in that, Specifically, it includes: Step 1: Two displacement sensors are installed opposite each other on the left and right sides of the turbine front casing to measure the axial displacement of the cylinder front bearing housing and obtain two displacement signals. and ; using the displacement signal and Determine the axial displacement of the front bearing housing ; Temperature sensors were used to collect the metal temperature signals of bearings No. 2, No. 3, No. 4, No. 5, No. 6, and No.

7. Shaft seal steam supply temperature signal , regulating level metal temperature signal Steam chamber valve body inner wall temperature signal and positive thrust bearing metal temperature signal ; Step 2: Using the Spearman coefficient analysis method, perform correlation analysis on the 10 temperature signals collected in Step 1 to obtain correlation combinations; Step 3: Calculate the 10 temperature signals collected in Step 1 and the axial displacement of the front bearing housing. The correlation coefficient between them is used to obtain the correlation coefficient with the axial displacement of the front bearing housing by combining the correlation coefficients described in step two. One or more related temperature signals; Step 4: Compare one or more temperature signals mentioned in Step 3 with the axial displacement of the front bearing housing. Perform curve fitting to obtain the polynomial relating the one or more temperature signals to the axial displacement L of the front bearing housing; Step 5: Calculate the axial displacement of the front bearing housing using the relational polynomial described in Step 4. Theoretical value ; Step 6: Utilize the axial displacement of the front bearing housing Theoretical value Displacement signal at current time and Real-time axial displacement and displacement signal and The real-time changes in the steam turbine's axial and front box expansion are used to determine whether there are any abnormalities, thus enabling the monitoring of the axial displacement of the high-pressure and intermediate-pressure cylinders of the steam turbine. The method for obtaining correlation combinations by performing correlation analysis on the 10 temperature signals collected in step one is as follows: Using the Spearman coefficient analysis method, the correlation coefficients between the 10 temperature signals in step one are calculated. Nine correlation coefficients are obtained for each temperature signal. The correlation coefficient with the largest absolute value is selected from the nine correlation coefficients corresponding to each temperature signal to obtain 10 correlation coefficients with the largest absolute values. Values ​​greater than 0.95 are extracted from the 10 correlation coefficients with the largest absolute values, and the two temperature signals corresponding to the values ​​greater than 0.95 are considered as a correlation combination. In step three, the axial displacement relative to the front bearing housing is obtained. The specific method for handling one or more related temperature signals is as follows: Step 3: Calculate the axial displacement of the front bearing housing using the Spearman coefficient analysis method. The correlation coefficient with the 10 temperature signals from step one; temperature signals with an absolute correlation coefficient greater than 0.8 are selected. Step 32: From the temperature signals with an absolute correlation coefficient greater than 0.8 mentioned in Step 31, search for temperature signals that are mutually correlated. If they exist, delete the temperature signals in the correlation combination that are related to the axial displacement of the front bearing housing. Temperature signals with low correlation coefficients are used to obtain information related to the axial displacement of the front bearing housing. One or more related temperature signals; In step six, the specific method for judging whether the axial expansion of the steam turbine and the front box are abnormal is as follows: Step 61: Record the results within 1 minute. Number of displacement data ,when If the axial expansion of the unit is abnormal, it is determined that the axial expansion of the unit is abnormal; otherwise, the axial expansion of the unit is normal, and step six-two is executed. in, This indicates the axial displacement of the bearing housing at time i; Displacement signal and deviation sequence standard deviation The total number of all displacement data obtained within 1 minute; Step 62: Record the results within 1 minute. Data points ;when The expansion at the measuring point on the left side of the turbine front box was determined to be abnormal. At the same time, record The number of data points s; when If the expansion at the measuring point on the right side of the turbine front casing is abnormal, then it is determined that the expansion is abnormal; among them, and They are respectively The displacement changes are measured by two displacement sensors on the left and right sides of the turbine front casing at all times. Displacement signal The value at time i, Displacement signal The value at time i; These represent the standard deviations of the measured displacement change sequences at the two measuring points, respectively. Otherwise, the turbine front box expansion is considered normal.

2. The method for detecting axial displacement of the intermediate and high-pressure cylinder of a steam turbine according to claim 1, characterized in that, In step six, at time i, the axial displacement L of the bearing housing is... i for: in, Displacement signal The value at time i-1 Displacement signal The value at time i-1.

3. The method for detecting axial displacement of the intermediate and high-pressure cylinder of a steam turbine according to claim 2, characterized in that, In step six-one, the displacement signal and deviation sequence for: Where, the subscript of L These represent the numbers of the displacement sensors on the left and right sides of the turbine front casing, respectively; the superscript N represents time. These represent the deviations of the left and right displacement sensors in the turbine front box from time 0 to time N.

4. The method for detecting axial displacement of the intermediate and high-pressure cylinder of a steam turbine according to claim 1, characterized in that, In step one, using the above and Determine the axial displacement of the front bearing housing The method is as follows: 。 5. The method for detecting axial displacement of the intermediate and high-pressure cylinder of a steam turbine according to claim 1, characterized in that, In step 3, the Spearman coefficient analysis method is used to calculate the axial displacement of the front bearing housing. The method for obtaining the correlation coefficients of the 10 temperature signals in step two is the same; Among them, the axial displacement of the front bearing housing is calculated. Temperature of bearing No. 2 The method for obtaining the correlation coefficient is as follows: Step 3.11: Check the temperature signal of bearing No.

2. conduct Sample at each time point to obtain Each temperature sample value; The sampled values ​​are arranged in descending order to obtain... A sequence of temperature values, A; Corresponding displacement sensors on the left and right sides of the turbine front casing Sampling at each time point, each displacement sensor acquires... Each displacement sample value is obtained, and the result is calculated. Axial displacement of the front bearing housing The value will Axial displacement of the front bearing housing Arrange the values ​​in descending order to obtain A sequence of displacement values ; Step 3.12, Utilize A temperature value sequence A and A sequence of displacement values Calculate the axial displacement of the front bearing housing. Temperature of bearing No. 2 The correlation coefficient.

6. The method for detecting axial displacement of the intermediate and high-pressure cylinder of a steam turbine according to claim 1, characterized in that, Axial displacement of front bearing housing Value and temperature of bearing No. 2 The correlation coefficient is: in, Indicates the axial displacement of the front bearing housing Value and temperature of bearing No. 2 The correlation coefficient, This represents the position of the temperature value at time i in sequence A. This indicates the displacement value at time i in the sequence. The order of the numbers.

Images