On-line monitoring and intelligent monitoring technology for operation state of air feeder

Abstract

The invention discloses an on-line monitoring and intelligent monitoring technology for the operation state of an air feeder, which comprises an air feeder, a motor, two bearing seats, a decentralized system and a plurality of vibration acceleration sensors, and is characterized in that the decentralized control system is used for controlling units such as the air feeder and the motor and collecting various data of the units; the vibration acceleration sensors are distributed in the vertical directions of the motor and the bearing seats at the two ends of the air feeder; vibration state signals of a motor and an air feeder are collected through a vibration acceleration sensor, so that the vibration state signals are reconstructed to obtain fault characteristic parameters of states of the motor and the air feeder; technological parameters such as the rotating speed of the air feeder, the current of a motor and the opening degree of an air door and a movable blade in an air and smoke system of the air feeder are linked with equipment vibration state signals through a decentralized control system, and therefore intelligent monitoring and diagnosis of the operation state of the air feeder are achieved through classification indexes. Various defects of the motor and the fan can be found in time, and normal operation of equipment is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring and diagnosis of metallurgical mechanical equipment, and in particular to an online monitoring and intelligent monitoring technology for the operating status of a blower. Background Art

[0002] The blower is one of the three major blowers of the boiler. It delivers the hot air necessary for combustion to the furnace after passing through the air preheater. Whether it can work normally is related to the safe operation of the boiler and even the entire unit. Since the blower unit of a certain factory was put into operation, there have been a series of failures such as the blower suddenly stopping during operation, which led to the chain stop of the boiler blower and the extinguishing of the boiler. Especially when the moving blade adjustment is out of control, it will automatically open fully, and the wind pressure and air volume will drop, resulting in non-accidental shutdown events. Therefore, in order to ensure the safe and stable operation of the unit, whether the cause of the abnormal vibration of the blower can be quickly diagnosed and the corresponding solution can be given, which is of great significance to the safe operation of the unit. There are various manifestations in the phenomenon of blower failure, and the main faults include: unbalanced mass of the blower blades, misaligned installation, surge, abnormal oil temperature in the lubricating oil tank, low oil level in the lubricating oil tank, sliding bearing failure, and blockage of the cooling water pipe of the oil station.

[0003] Therefore, based on the above technical problems, this application specifically proposes an online monitoring and intelligent monitoring technology for the operation status of the blower, which can ensure the normal operation of the equipment and present fault problems intuitively. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies of the prior art and to provide an online monitoring and intelligent monitoring technology for the operation status of a blower which can ensure the normal operation of the equipment and present fault problems intuitively.

[0005] In order to achieve the above-mentioned purpose, the present invention provides an online monitoring and intelligent monitoring technology for the operation status of a blower, including a blower, a motor, two bearing seats, a distributed system and a plurality of vibration acceleration sensors, wherein the motor is used to drive the blower to operate, and the distributed control system is used to control the blower, the motor and other units and collect various data of the units, and the vibration acceleration sensors are distributed in the vertical direction of the motor and the bearing seats at both ends of the blower; the vibration acceleration sensors are used to collect vibration status signals of the motor and the blower, thereby reconstructing the vibration status signals to obtain fault characteristic parameters of the motor and the blower status; the fan speed, motor current, damper in the air and smoke system of the blower, blade opening and other process parameters are linked with the equipment vibration status signal through the distributed control system, thereby using classification indicators to realize intelligent monitoring and diagnosis of the operation status of the blower.

[0006] Furthermore, a vibration classification index table caused by the mass imbalance of the fan blades is established. The specific steps are as follows: Step 1: Obtain the vibration amplitude at the 1st frequency of the rotor when the fan blade has a mass imbalance fault; Step 2: Obtain the original vibration signal of the bearing seat; Step 3 The original vibration signal is subjected to fast Fourier transform to extract the vibration amplitude components at the rotation speed frequency (1 times frequency), 2 times the rotation speed frequency (2 times frequency) and 3 times the rotation speed frequency (3 times frequency); Step 4: monitor the ratio of the 1st frequency to the sum of the vibration amplitudes at the 1st frequency, 2nd frequency and 3rd frequency of the reconstructed signal, that is, monitor the mass imbalance fault coefficient of the fan blade; Step 5 When it is monitored that the mass imbalance fault coefficient of the fan blade is greater than 80%, a mass imbalance fault of the fan blade is predicted.

[0007] Furthermore, the vibration fault characteristics caused by rotor misalignment are extracted. The specific steps are as follows: Step 1: Obtain the vibration amplitude at the 2nd frequency of the rotor when the rotor is installed misaligned; Step 2: Obtain the original vibration signal of the bearing seat; Step 3 The vibration amplitude components at the rotation speed frequency (1 times frequency), 2 times the rotation speed frequency (2 times frequency), 3 times the rotation speed frequency (3 times frequency) and 4 times the rotation speed frequency (4 times frequency) are respectively extracted from the original vibration signal through spectrum analysis and fast Fourier transform; Step 4: monitor the ratio of the sum of the vibration amplitudes at the 2nd frequency and the 1st frequency, 2nd frequency, 3rd frequency and 4th frequency of the reconstructed signal, that is, the installation misalignment fault coefficient; Step 5 When it is monitored that the rotor installation misalignment failure coefficient is greater than 30%, a poor rotor installation alignment is predicted.

[0008] Furthermore, the vibration classification index caused by surge is established, and the specific steps are as follows: Step 1: Obtain the original vibration signal of the bearing seat; Step 2: Extract the vibration amplitude components at 0.2 times to 0.4 times the rotation speed through spectrum analysis and fast Fourier transform of the original vibration signal; Step 3: monitor the ratio of the vibration amplitude component at 0.2 times the frequency to 0.4 times the frequency of the reconstructed signal and the constant, that is, the surge fault coefficient; Step 4 When the surge fault coefficient is monitored to be greater than 15%, a surge fault of the pneumatic feedwater pump is predicted.

[0009] Further, the abnormal oil temperature in the lubricating oil tank is monitored, and the specific steps are as follows: Step 1: Set the first-level alarm value and the second-level alarm value according to the oil temperature in the lubricating oil tank; Step 2: monitor the actual value of the oil temperature in the lubricating oil tank; Step 3: monitor the difference between the actual temperature value and the first level alarm value and the difference between the actual temperature value and the second level alarm value, that is, the first level lubricating oil temperature check coefficient and the second level lubricating oil temperature check coefficient; Step 4 When it is monitored that the primary lubricating oil temperature check coefficient is greater than 0 or the secondary lubricating oil temperature check coefficient is greater than 0, an abnormal fault of the oil temperature in the lubricating oil tank is predicted.

[0010] Further, monitor the lubricating oil tank level, the specific steps are as follows: Step 1: Set the liquid level alarm value according to the lubricating oil tank liquid level; Step 2: monitoring the actual value of the lubricating oil tank liquid level; Step 3: monitor the difference between the actual value of the liquid level and the liquid level alarm value, that is, the lubricating oil tank liquid level failure coefficient; Step 4 When the lubricating oil tank level fault coefficient is monitored to be less than 0, it is predicted that the lubricating oil tank level is too low.

[0011] Furthermore, the mechanical vibration fault characteristics of the sliding bearing fault are extracted, and the specific steps are as follows: Step 1: Obtain the original vibration signal of the bearing seat; Step 2: Extract the vibration amplitude components at 1 times to 10 times the speed through spectrum analysis and fast Fourier transform of the original vibration signal; Step 3: monitor the ratio of the sum of the vibration amplitudes at 1 times the frequency to 10 times the frequency of the reconstructed signal to a constant, that is, the fault coefficient of the sliding bearing of the blower; Step 4 When it is monitored that the failure coefficient of the sliding bearing of the blower is greater than 60%, the sliding bearing state is predicted to be abnormal.

[0012] Furthermore, a classification index for blockage of cooling water pipes in the fan oil station is established. The specific steps are as follows: Step 1: Set the flow alarm value according to the outlet flow of the oil station cooling water pipe; Step 2: monitor the actual flow rate of the outlet flow rate of the oil station cooling water pipe; Step 3: monitor the ratio of the difference between the flow alarm value and the actual flow value to the flow alarm value, that is, the blockage failure coefficient of the cooling water pipe of the blower oil station; Step 4 When it is detected that the cooling water pipe blockage failure coefficient of the fan oil station is greater than 20%, a cooling water pipe blockage failure of the fan oil station is predicted.

[0013] Furthermore, it also includes a number of indicator lights, wherein one end of the indicator light is connected to various parts of the blower, and the other end is connected to the distributed control system; the indicator light is used to display the operating status of various parts of the blower, when no failure of the blower is detected, the indicator light is green; when a failure of the equipment is detected, the indicator light connected to the corresponding part of the blower is red.

[0014] The present invention adopts the above-mentioned solution, and its beneficial effects are: By obtaining vibration signals from the motor and the fan bearing seat, classification indicators are used to monitor the deterioration trend of the motor and fan operating status. When the classification indicators are abnormal, motor and fan failures are predicted, and operation and equipment management personnel are guided to take countermeasures, thereby reducing maintenance costs and ensuring the orderly progress of normal production. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Schematic diagram of the process of this embodiment. DETAILED DESCRIPTION

[0016] In order to facilitate the understanding of the present invention, the present invention is described more fully below with reference to the accompanying drawings. The accompanying drawings provide preferred embodiments of the present invention. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. The purpose of providing these embodiments is to make the disclosure of the present invention more thoroughly and comprehensively understood.

[0017] See attached Figure 1 As shown, in this embodiment, an online monitoring and intelligent monitoring technology for the operation status of a blower includes a blower, a motor, two bearing seats, a distributed system and a plurality of vibration acceleration sensors, wherein the motor is used to drive the blower to operate, the distributed control system is used to control the blower, the motor and other units and collect various data of the units, and the vibration acceleration sensors are distributed in the vertical direction of the bearing seats at both ends of the motor and the blower; the vibration acceleration sensors are used to collect vibration status signals of the motor and the blower, thereby reconstructing the vibration status signals to obtain fault characteristic parameters of the motor and the blower status; the fan speed, motor current, damper in the air and smoke system of the blower, blade opening and other process parameters are linked with the equipment vibration status signal through the distributed control system, thereby using classification indicators to realize intelligent monitoring and diagnosis of the operation status of the blower.

[0018] Furthermore, a vibration classification index table caused by the mass imbalance of the fan blades is established. The specific steps are as follows: Step 1: Obtain the vibration amplitude at the 1st frequency of the rotor when the fan blade has a mass imbalance fault; Step 2: Obtain the original vibration signal of the bearing seat; Step 3: The vibration amplitude components Xi(t) at the speed frequency (1 times), 2 times the speed frequency (2 times) and 3 times the speed frequency (3 times) are extracted from the original vibration signal through fast Fourier transform, where (i=1, 2, 3). The specific algorithm is as follows: Step 4: Monitor 1x frequency The sum of the vibration amplitudes at 1 times, 2 times, and 3 times of the reconstructed signal The ratio H, that is, the mass imbalance failure coefficient of the fan blades is monitored. The specific algorithm is as follows: H={X}_{1}(t) / [\sum ^{3}_{i=1} {{X}_{i}}(t)]

[0019] Step 5 When it is monitored that the mass imbalance fault coefficient H of the fan impeller is greater than 80%, a mass imbalance fault is predicted to occur in the fan impeller.

[0020] Furthermore, the vibration fault characteristics caused by rotor misalignment are extracted. The specific steps are as follows: Step 1: Obtain the vibration amplitude at the 2nd frequency of the rotor when the rotor is installed misaligned; Step 2: Obtain the original vibration signal of the bearing seat; Step 3: Use spectrum analysis and fast Fourier transform to extract the vibration amplitude components at the speed frequency (1 times), 2 times the speed frequency (2 times), 3 times the speed frequency (3 times) and 4 times the speed frequency (4 times) of the original vibration signal. , where (i=1, 2, 3, 4); Step 4: Monitor the 2x frequency The sum of the vibration amplitudes at 1, 2, 3, and 4 times the reconstructed signal The ratio P of , that is, the installation misalignment failure coefficient, the specific algorithm is as follows: P={X}_{2}(t) / \left [ {\sum ^{4}_{i=1} {{X}_{i}(t)}} \right ]

[0021] Step 5 When the rotor misalignment fault coefficient P>30% is monitored, a poor rotor alignment is predicted.

[0022] Furthermore, the vibration classification index caused by surge is established, and the specific steps are as follows: Step 1: Obtain the original vibration signal of the bearing seat; Step 2: The original vibration signal is subjected to spectrum analysis and fast Fourier transform to extract the vibration amplitude components Xw(t) at 0.2 times to 0.4 times the rotation speed; Step 3: Monitor the ratio W of the vibration amplitude component Xw(t) at 0.2 times the frequency to 0.4 times the frequency of the reconstructed signal and the constant A, that is, the surge fault coefficient. The specific algorithm is as follows:

[0023] Step 4 When the surge fault coefficient W is monitored to be greater than 15%, a surge fault of the pneumatic water supply pump is predicted, thereby reminding the operator to adjust the pressure or flow of the oil supply system to ensure the normal operation of the equipment.

[0024] Further, the abnormal oil temperature in the lubricating oil tank is monitored, and the specific steps are as follows: Step 1: Set the first level alarm value Ts1 and the second level alarm value Ts2 according to the oil temperature in the lubricating oil tank; Step 2: monitor the actual temperature value T of the oil temperature in the lubricating oil tank; Step 3: Monitor the difference M1 between the actual temperature value T and the first level alarm value Ts1 and the difference M2 between the actual temperature value T and the second level alarm value Ts2, that is, the first level lubricating oil temperature check coefficient and the second level lubricating oil temperature check coefficient. The specific algorithm is as follows:

[0025] Step 4 When it is monitored that the first-level lubricating oil temperature check coefficient M1>0 or the second-level lubricating oil temperature check coefficient M2>0, it is predicted that the oil temperature in the lubricating oil tank is abnormal.

[0026] Further, monitor the lubricating oil tank level, the specific steps are as follows: Step 1: Set the liquid level alarm value Es according to the lubricating oil tank liquid level; Step 2: monitor the actual value E of the liquid level of the lubricating oil tank; Step 3: Monitor the difference R between the actual liquid level value E and the liquid level alarm value Es, that is, the lubricating oil tank liquid level failure coefficient. The specific algorithm is as follows:

[0027] Step 4: When the lubricating oil tank level fault coefficient R is monitored to be less than 0, it is predicted that the lubricating oil tank level is too low.

[0028] Furthermore, the mechanical vibration fault characteristics of the sliding bearing fault are extracted, and the specific steps are as follows: Step 1: Obtain the original vibration signal of the bearing seat; Step 2: Extract the vibration amplitude components Xi(t) at 1 times to 10 times the speed through spectrum analysis and fast Fourier transform of the original vibration signal, where (i=1, 2, 3, 4, 5, 6, 7, 8, 9, 10); Step 3: Monitor the sum of the vibration amplitudes at 1x to 10x of the reconstructed signal. The ratio C to the constant A, that is, the failure coefficient of the fan sliding bearing, is calculated as follows: C=[\sum ^{10}_{i=1} {{X}_{i}(t)}] / A

[0029] Step 4 When it is monitored that the fault coefficient C of the fan sliding bearing is greater than 60%, an abnormal state of the sliding bearing is predicted.

[0030] Furthermore, a classification index for blockage of cooling water pipes in the fan oil station is established. The specific steps are as follows: Step 1 Set the flow alarm value Q according to the outlet flow of the oil station cooling water pipe s ; Step 2: Monitor the actual flow value Q of the outlet flow of the oil station cooling water pipe f ; Step 3: Monitor the flow alarm value Q s and the actual flow value Q f The difference between the flow alarm value Q s The ratio J, that is, the cooling water pipe blockage failure coefficient of the fan oil station, the specific algorithm is as follows:

[0031] Step 4 When it is detected that the cooling water pipe blockage fault coefficient J of the fan oil station is greater than 20%, a cooling water pipe blockage fault of the fan oil station is predicted.

[0032] Furthermore, it also includes a number of indicator lights, wherein one end of the indicator light is connected to various parts of the blower, and the other end is connected to the distributed control system; the indicator light is used to display the operating status of various parts of the blower, and when no failure of the blower is detected, the indicator light is green; when a failure of the equipment is detected, the indicator light connected to the corresponding part of the blower is red, so that the operator can more intuitively understand the specific location and cause of the failure of the blower, and maintain the blower in time to improve production efficiency.

[0033] The embodiments described above are only preferred embodiments of the present invention and are not intended to limit the present invention in any form. Any technician familiar with the art who, without departing from the scope of the technical solution of the present invention, makes more possible changes and modifications to the technical solution of the present invention using the technical content disclosed above, or modifications are all equivalent embodiments of the present invention. Therefore, any equivalent and equivalent changes made according to the ideas of the present invention without departing from the content of the technical solution of the present invention should be included in the protection scope of the present invention.

Claims

1. A technology for online monitoring and intelligent monitoring of the operation status of a blower, characterized in that: It includes a blower, a motor, two bearing seats, a distributed system and several vibration acceleration sensors. The motor is used to drive the blower to operate. The distributed control system is used to control the blower and the motor and other units and collect various data of the units. The vibration acceleration sensors are distributed in the vertical direction of the bearing seats at both ends of the motor and the blower. The vibration state signals of the motor and the blower are collected by the vibration acceleration sensors, so as to reconstruct the vibration state signals to obtain the fault characteristic parameters of the motor and the blower state. The distributed control system links process parameters such as fan speed, motor current, damper in the air and smoke system of the fan, blade opening and the like with equipment vibration status signals, thereby using classification indicators to achieve intelligent monitoring and diagnosis of the operating status of the fan.

2. The online monitoring and intelligent monitoring technology for the operation status of a blower according to claim 1 is characterized in that: Establish a vibration classification index table caused by the mass imbalance of the fan blades. The specific steps are as follows: Step 1: Obtain the vibration amplitude at the 1st frequency of the rotor when the fan blade has a mass imbalance fault; Step 2: Obtain the original vibration signal of the bearing seat; Step 3 The original vibration signal is subjected to fast Fourier transform to extract the vibration amplitude components at the rotation speed frequency (1 times frequency), 2 times the rotation speed frequency (2 times frequency) and 3 times the rotation speed frequency (3 times frequency); Step 4: monitor the ratio of the 1st frequency to the sum of the vibration amplitudes at the 1st frequency, 2nd frequency and 3rd frequency of the reconstructed signal, that is, monitor the mass imbalance fault coefficient of the fan blade; Step 5 When it is monitored that the mass imbalance fault coefficient of the fan blade is greater than 80%, a mass imbalance fault of the fan blade is predicted.

3. The online monitoring and intelligent monitoring technology for the operation status of a blower according to claim 1 is characterized in that: Extract the vibration fault characteristics caused by rotor misalignment. The specific steps are as follows: Step 1: Obtain the vibration amplitude at the 2nd frequency of the rotor when the rotor is installed misaligned; Step 2: Obtain the original vibration signal of the bearing seat; Step 3 The vibration amplitude components at the rotation speed frequency (1 times frequency), 2 times the rotation speed frequency (2 times frequency), 3 times the rotation speed frequency (3 times frequency) and 4 times the rotation speed frequency (4 times frequency) are respectively extracted from the original vibration signal through spectrum analysis and fast Fourier transform; Step 4: monitor the ratio of the sum of the vibration amplitudes at the 2nd frequency and the 1st frequency, 2nd frequency, 3rd frequency and 4th frequency of the reconstructed signal, that is, the installation misalignment fault coefficient; Step 5 When it is monitored that the rotor misalignment failure coefficient is greater than 30%, a poor rotor alignment is predicted.

4. The online monitoring and intelligent monitoring technology for the operation status of a blower according to claim 1 is characterized in that: Establish the vibration classification index caused by surge. The specific steps are as follows: Step 1: Obtain the original vibration signal of the bearing seat; Step 2: Extract the vibration amplitude components at 0.2 times to 0.4 times the rotation speed through spectrum analysis and fast Fourier transform of the original vibration signal; Step 3: monitor the ratio of the vibration amplitude component at 0.2 times the frequency to 0.4 times the frequency of the reconstructed signal and the constant, that is, the surge fault coefficient; Step 4 When the surge fault coefficient is monitored to be greater than 15%, a surge fault of the pneumatic feedwater pump is predicted.

5. The online monitoring and intelligent monitoring technology for the operation status of a blower according to claim 1 is characterized in that: Monitor the abnormal oil temperature in the lubricating oil tank. The specific steps are as follows: Step 1: Set the first-level alarm value and the second-level alarm value according to the oil temperature in the lubricating oil tank; Step 2: monitor the actual value of the oil temperature in the lubricating oil tank; Step 3: monitor the difference between the actual temperature value and the first level alarm value and the difference between the actual temperature value and the second level alarm value, that is, the first level lubricating oil temperature check coefficient and the second level lubricating oil temperature check coefficient; Step 4 When it is monitored that the primary lubricating oil temperature check coefficient is greater than 0 or the secondary lubricating oil temperature check coefficient is greater than 0, an abnormal fault of the oil temperature in the lubricating oil tank is predicted.

6. The online monitoring and intelligent monitoring technology for the operation status of a blower according to claim 1 is characterized in that: Monitor the lubricating oil tank level. The specific steps are as follows: Step 1: Set the liquid level alarm value according to the lubricating oil tank liquid level; Step 2: monitoring the actual value of the lubricating oil tank liquid level; Step 3: monitor the difference between the actual value of the liquid level and the liquid level alarm value, that is, the lubricating oil tank liquid level failure coefficient; Step 4 When the lubricating oil tank level fault coefficient is monitored to be less than 0, it is predicted that the lubricating oil tank level is too low.

7. The online monitoring and intelligent monitoring technology for the operation status of a blower according to claim 1 is characterized in that: Extract the mechanical vibration fault characteristics of sliding bearing faults. The specific steps are as follows: Step 1: Obtain the original vibration signal of the bearing seat; Step 2: Extract the vibration amplitude components at 1 times to 10 times the speed through spectrum analysis and fast Fourier transform of the original vibration signal; Step 3: monitor the ratio of the sum of the vibration amplitudes at 1 times the frequency to 10 times the frequency of the reconstructed signal to a constant, that is, the fault coefficient of the sliding bearing of the blower; Step 4 When it is monitored that the failure coefficient of the sliding bearing of the blower is greater than 60%, the sliding bearing state is predicted to be abnormal.

8. The online monitoring and intelligent monitoring technology for the operation status of a blower according to claim 1 is characterized in that: Establish the classification index of cooling water pipe blockage of fan oil station. The specific steps are as follows: Step 1: Set the flow alarm value according to the outlet flow of the oil station cooling water pipe; Step 2: monitor the actual flow rate of the outlet flow rate of the oil station cooling water pipe; Step 3: monitor the ratio of the difference between the flow alarm value and the actual flow value to the flow alarm value, that is, the blockage failure coefficient of the cooling water pipe of the blower oil station; Step 4 When it is detected that the cooling water pipe blockage failure coefficient of the fan oil station is greater than 20%, a cooling water pipe blockage failure of the fan oil station is predicted.

9. The online monitoring and intelligent monitoring technology for the operation status of a blower according to claim 1 is characterized in that: It also includes several indicator lights, wherein one end of the indicator light is connected to various parts of the blower, and the other end is connected to the distributed control system; the indicator light is used to display the operating status of various parts of the blower, when no failure of the blower is detected, the indicator light is green; when a failure of the equipment is detected, the indicator light connected to the corresponding part of the blower is red.

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