Intelligent monitoring method for operating state of blast furnace TRT generator set

Abstract

The application discloses an intelligent monitoring method for the running state of a blast furnace TRT generator set, the method uses the vibration sensor already installed on the TRT generator set, collects the vibration data of the TRT generator set through the secondary instrument of the TRT generator set, obtains the fault features of the TRT generator set through signal reconstruction, and the fault features include the classification index of oil film vortex, the classification index of shafting misalignment, the dynamic and static part rubbing fault coefficient, the surge fault classification index and the rotor unbalance fault classification index; and the limit values of various indexes are set respectively, and the early warning prompt is given when the value is greater than the corresponding limit value. The method realizes the intelligent monitoring and diagnosis of the running state of the TRT generator set, grasps the deterioration trend of the running state of the TRT generator set, and guides the equipment maintenance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mechanical equipment monitoring and diagnosis, and particularly relates to an intelligent monitoring method for a running state of a blast furnace TRT generator set. BACKGROUND

[0002] The blast furnace TRT generator set is composed of a turbine set system, a power oil system, a lubricating oil system, a nitrogen system, a large valve system, an electrical system, an automatic control system and a circulating water system.

[0003] At present, the protection system of the blast furnace TRT generator set mostly adopts a current output type vibration sensor, and protection control is performed through the connection of a PLC, and only protection tripping and alarm functions are provided, and no original signal output capability is provided, so that spectrum analysis and remote diagnosis of the generator set cannot be realized.

[0004] With the development of detection technology and information technology, in order to comply with the development of equipment intelligentization and equipment management technology, the organic integration of intelligent sensing, the Internet of Things, big data analysis and artificial intelligence and other technologies is used to centrally monitor the TRT generator set, improve the equipment management efficiency, optimize the equipment maintenance strategy, and improve the intelligent level of the management of the TRT generator set, which is an urgent research topic for those skilled in the art. The failure modes of the TRT generator set mainly include oil film vortex, misalignment, dynamic and static part rubbing, surge and rotor imbalance. Due to the limitation of the protection system of the TRT generator set, the monitoring of the running state of the blast furnace TRT generator set is seriously affected.

[0005] At present, the TRT generator set basically adopts after-event maintenance, and the online monitoring of the TRT equipment is performed by a chain protection instrument. In the actual operation process of the TRT equipment, faults such as oil film vortex, misalignment, dynamic and static part rubbing, surge and rotor imbalance are prone to occur, so it is of great significance to perform intelligent monitoring and diagnosis on the online running state of the TRT equipment. SUMMARY

[0006] The technical problem to be solved by the present application is to provide an intelligent monitoring method for a running state of a blast furnace TRT generator set. The method is aimed at the main failure modes of the TRT equipment, uses the vibration sensor of the TRT equipment to collect vibration data of the TRT equipment, obtains fault features of the TRT equipment through signal reconstruction, collects running state data of the TRT equipment, realizes intelligent monitoring and diagnosis of the running state of the TRT equipment, grasps the deterioration trend of the running state of the TRT equipment, and guides the maintenance and repair of the equipment.

[0007] To solve the above technical problems, the intelligent monitoring method for a running state of a blast furnace TRT generator set comprises the following steps:

[0008] Step one, collect TRT generator set vibration sensor output original vibration signal Xi, the original vibration signal Xi is analyzed by spectrum analysis, and the characteristic signal of TRT generator set is extracted; the total vibration value A of the original vibration signal is obtained from the discrete value X(i) (i=1, 2, …, N) of the original vibration signal;

[0009]

[0010] In formula (1) is the average value of X(i);

[0011] Step two, establish the classification index of TRT generator set oil film vortex, the original vibration signal Xi obtained is transformed by FFT to extract the vibration amplitude component X(i) (i=1, 2, 3, 4, 5) at 1 to 5 times frequency, extract 0.4 times frequency to 0.5 times frequency vibration amplitude component Xw(t), TRT generator set oil film vortex failure coefficient W is calculated according to formula (2);

[0012]

[0013] When W>25%, the TRT generator set oil film vortex failure is warned;

[0014] Step three, establish the misalignment classification index of shaft system, the original vibration signal Xi obtained is transformed by FFT to extract the vibration amplitude component X(i) (i=1, 2) at 1 times frequency and 2 times frequency, the fault signal caused by misalignment is reconstructed by inverse calculation, and the misalignment failure coefficient L is calculated according to formula (3);

[0015] L=X(2) / [X(1)+X(2)] (3)

[0016] In formula (3), X(1) and X(2) represent the vibration amplitude at 1 times frequency and 2 times frequency of the vibration signal respectively;

[0017] The misalignment failure coefficient L of TRT generator set shaft system is monitored, when L>20%, the TRT generator set shaft system misalignment is predicted, and the TRT generator set shaft center line correction is suggested;

[0018] Step four, establish the dynamic and static part rubbing failure coefficient, the original vibration signal Xi obtained is transformed by FFT to extract the vibration amplitude component X(i) (i=1, 2, 3, 4, …, 9) at 1 to 9 times frequency, the dynamic and static part rubbing failure signal is reconstructed by inverse calculation, and the dynamic and static part rubbing failure coefficient G is calculated according to formula (4);

[0019]

[0020] X(5), X(6), X(7), X(8), X(9) in formula (4) respectively represent vibration amplitude values at 5 to 9 times frequency of vibration signal 5 to 9;

[0021] The rubbing fault coefficient G of the moving and static parts is monitored, and when G>40%, the rubbing of the moving and static parts is predicted;

[0022] Step five, a surge fault classification index of the TRT generator set is established; the vibration amplitude value components X(i) (i=1, 2, 3, 4, 5) at 1 to 5 times frequency of the original vibration signal X acquired are extracted through the frequency spectrum analysis FFT transformation, the vibration amplitude value component Xs(t) at 0.2 times frequency to 0.4 times frequency is extracted, and the surge fault coefficient S of the TRT generator set is calculated according to formula (5);

[0023]

[0024] When S>20%, the surge fault of the TRT generator set is predicted;

[0025] Step six, a classification index of the rotor imbalance fault of the TRT generator set is established; the vibration amplitude value components X(i) (i=1, 2, 3, 4, 5, 9) at 1 to 9 times frequency of the original vibration signal X acquired are extracted through the frequency spectrum analysis FFT transformation, the rotor imbalance fault signal of the TRT generator set after superposition is reconstructed through inverse calculation, and the rotor imbalance fault coefficient B is calculated according to formula (6);

[0026]

[0027] The rotor imbalance fault coefficient of the TRT generator set is monitored, and when B>60%, the poor installation centering is predicted.

[0028] Since the intelligent monitoring method for the running state of the blast furnace TRT generator set adopts the technical scheme, that is, the method uses the vibration sensors already installed on the TRT generator set, collects the vibration data of the TRT generator set through the secondary instrument of the TRT generator set, obtains the fault characteristics of the TRT generator set through signal reconstruction, including the classification index of the oil film vortex, the classification index of the shaft misalignment, the rubbing fault coefficient of the moving and static parts, the surge fault classification index and the rotor imbalance fault classification index, and the limit values of various indexes are set respectively, and greater than the corresponding limit value gives a warning prompt. The method realizes the intelligent monitoring and diagnosis of the running state of the TRT generator set, grasps the deterioration trend of the running state of the TRT generator set, and guides the equipment maintenance and maintenance. BRIEF DESCRIPTION OF DRAWINGS

[0029] The application will be further described in detail below in combination with the drawings and embodiments:

[0030] Figure 1The flow chart of the intelligent monitoring method for the running state of the blast furnace TRT generator set. DETAILED DESCRIPTION

[0031] Embodiments such as Figure 1 The intelligent monitoring method for the running state of the blast furnace TRT generator set includes the following steps:

[0032] Step one, collect the original vibration signal Xi output by the TRT generator set vibration sensor, perform frequency spectrum analysis on the original vibration signal Xi, and extract the characteristic signal of the TRT generator set; the total vibration value A of the original vibration signal is obtained from the discrete value X(i) (i=1, 2, …, N) of the original vibration signal;

[0033]

[0034] In formula (1) is the average value of X(i);

[0035] Step two, establish the classification index of the oil film whirl of the TRT generator set, extract the vibration amplitude component X(i) (i=1, 2, 3, 4, 5) at 1 to 5 times the frequency of the rotating speed from the original vibration signal Xi obtained through the frequency spectrum analysis FFT transformation, extract the vibration amplitude component Xw(t) at 0.4 times the frequency to 0.5 times the frequency, and obtain the TRT generator set oil film whirl failure coefficient W according to formula (2);

[0036]

[0037] When W>25%, the TRT generator set oil film whirl failure is warned;

[0038] Step three, establish the misalignment classification index of the shaft system, extract the vibration amplitude component X(i) (i=1, 2) at 1 times the frequency of the rotating speed and 2 times the frequency of the rotating speed from the original vibration signal Xi obtained through the frequency spectrum analysis FFT transformation, reconstruct the failure signal caused by the misalignment through inverse calculation after superposition, and obtain the misalignment failure coefficient L of the shaft system according to formula (3);

[0039] L=X(2) / [X(1)+X(2)] (3)

[0040] In formula (3), X(1) and X(2) respectively represent the vibration amplitude at 1 times the frequency of the rotating speed and 2 times the frequency of the rotating speed of the vibration signal;

[0041] Monitor the misalignment failure coefficient L of the shaft system of the TRT generator set, when L>20%, the shaft system of the TRT generator set is misaligned, and it is suggested to perform the center line correction of the shaft system of the TRT generator set;

[0042] Step four, the establishment of the dynamic and static parts of the rubbing fault coefficient, the original vibration signal Xi has been obtained, the frequency spectrum analysis FFT transform extracts the vibration amplitude component X(i) (i = 1, 2, 3, 4, ┈┈, 9) at 1 to 9 times the frequency of the rotating speed frequency, through the inverse calculation of the superposition of the reconstructed dynamic and static parts of the rubbing fault signal, the dynamic and static parts of the rubbing fault coefficient G is calculated according to formula (4);

[0043]

[0044] In formula (4), X(5), X(6), X(7), X(8) and X(9) represent the vibration amplitudes at 5 to 9 times the frequency of the rotating speed frequency, respectively;

[0045] The dynamic and static parts of the rubbing fault coefficient G is monitored, and when G>40%, the dynamic and static parts of the rubbing are predicted;

[0046] Step five, the establishment of the TRT generator set surge fault classification index; the original vibration signal Xi has been obtained, the frequency spectrum analysis FFT transform extracts the vibration amplitude component X(i) (i = 1, 2, 3, 4, 5) at 1 to 5 times the frequency of the rotating speed frequency, and the vibration amplitude component Xs(t) at 0.2 times the frequency to 0.4 times the frequency is extracted, and the TRT generator set surge fault coefficient S is calculated according to formula (5);

[0047]

[0048] When S>20%, the TRT generator set surge fault is predicted;

[0049] Step six, the establishment of the TRT generator set rotor imbalance fault classification index, for the original vibration signal Xi has been obtained, the frequency spectrum analysis FFT transform extracts the vibration amplitude component X(i) (i = 1, 2, 3, 4, ┈┈, 9) at 1 to 9 times the frequency of the rotating speed frequency, through the inverse calculation of the superposition of the reconstructed TRT generator set rotor imbalance fault signal, the rotor imbalance fault coefficient B is calculated according to formula (6);

[0050]

[0051] The TRT generator set rotor imbalance fault coefficient is monitored, and when B>60%, the installation centering is predicted to be poor.

[0052] The method is aimed at the main failure mode of the TRT equipment, uses the vibration sensor installed in the TRT generator set, collects the vibration data of the TRT generator set from the buffer of the secondary instrument of the TRT generator set, and obtains the fault features of the TRT equipment through signal reconstruction; the intelligent monitoring and diagnosis of the running state of the TRT equipment are realized, the deterioration trend of the running state of the TRT is grasped, and the equipment repair and maintenance are guided. The method makes up for the defect that the online monitoring information of the running state of the blast furnace TRT equipment cannot be effectively utilized, solves the problem that the oil film vortex, misalignment, dynamic and static part rubbing, surge and rotor imbalance and other faults cannot be monitored in real time, and provides an effective basis for grasping the running state of the blast furnace TRT generator set and timely replacement and condition-based maintenance of the parts.

Claims

1. A method for intelligent monitoring of the operating status of a blast furnace TRT generator unit, characterized in that... This method includes the following steps: Step 1: Collect the original vibration signal Xi output by the vibration sensor of the TRT generator set, perform spectrum analysis on the original vibration signal Xi, and extract the characteristic signal of the TRT generator set; the total vibration value A of the original vibration signal is obtained from the discrete values ​​X(i) (i=1,2,……,N) of the original vibration signal. In formula (1) It is the average value of X(i); Step 2: Establish classification index for oil film whirl in TRT generator set. For the acquired original vibration signal Xi, extract the vibration amplitude component X(i) (i=1,2,3,4,5) at the 1st to 5th harmonic speed frequency through spectrum analysis and FFT transformation. Extract the vibration amplitude component Xw(t) from the 0.4th to 0.5th harmonic frequency. The oil film whirl fault coefficient W of TRT generator set is calculated according to formula (2). When W>25%, an early warning is issued for oil film eddy fault in the TRT generator set. Step 3: Establish a classification index for shaft misalignment. For the acquired original vibration signal Xi, the vibration amplitude components X(i) (i=1,2) at the first harmonic speed frequency and the second harmonic speed frequency are extracted by spectrum analysis and FFT transformation. The fault signal caused by misalignment is reconstructed by inverse calculation and superposition. The shaft misalignment fault coefficient L is calculated according to formula (3). L=X(2) / [X(1)+X(2)] (3) In equation (3), X(1) and X(2) represent the vibration amplitude at the first harmonic speed frequency and the second harmonic speed frequency of the vibration signal, respectively; Monitor the shaft misalignment fault factor L of the TRT generator set. When L>20%, it indicates that the TRT generator set shaft misalignment is predicted and it is recommended to perform TRT generator set shaft centerline correction. Step 4: Establish the collision and rubbing fault coefficient of moving and stationary parts. For the original vibration signal Xi that has been acquired, the vibration amplitude components X(i) (i=1,2,3,4,…,9) at the 1st to 9th harmonic speed frequencies are extracted by spectrum analysis and FFT transformation. The collision and rubbing fault signal of moving and stationary parts is reconstructed by inverse calculation and superposition. The collision and rubbing fault coefficient G of moving and stationary parts is calculated according to formula (4). In equation (4), X(5), X(6), X(7), X(8), and X(9) represent the vibration amplitudes at the 5th to 9th harmonic rotational speeds of the vibration signal, respectively. Monitor the failure coefficient G of moving and stationary parts rubbing together. When G>40%, predict the rubbing between moving and stationary parts. Step 5: Establish TRT generator set surge fault classification index; For the acquired original vibration signal Xi, extract the vibration amplitude components X(i) (i=1,2,3,4,5) at the 1st to 5th harmonic speed frequencies through spectrum analysis and FFT transformation, extract the vibration amplitude components Xs(t) from the 0.2th to 0.4th harmonic frequencies, and calculate the surge fault coefficient S of the TRT generator set according to formula (5); When S>20%, a surge fault is detected in the TRT generator set. Step 6: Establish classification index for rotor imbalance fault of TRT generator set. For the original vibration signal Xi that has been acquired, the vibration amplitude components X(i) (i=1,2,3,4,…,9) at the 1st to 9th harmonic speed frequencies are extracted by spectrum analysis and FFT transformation. The rotor imbalance fault signal of TRT generator set is reconstructed by inverse calculation and superposition. The rotor imbalance fault coefficient B is calculated according to formula (6). Monitor the rotor imbalance fault coefficient of the TRT generator set. When B>60%, it is predicted that the installation is not aligned properly.

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