Online monitoring method for intelligent operation and maintenance of uncoiling machine of cold rolling unit

Abstract

The invention discloses a cold rolling unit uncoiler intelligent operation and maintenance on-line monitoring method, which comprises the following steps of: acquiring operation state data of an uncoiler, and respectively arranging a vibration sensor and a sound sensor at corresponding positions of a motor and a gear box of the uncoiler; original vibration signals are collected through a vibration sensor, and the total vibration value of the original vibration signals is timely obtained after spectral analysis; and respectively calculating an uncoiler shafting installation centering bad fault coefficient G, a gearbox fault coefficient H, an uncoiler drum rotation abnormal sound fault coefficient M, a motor operation abnormal fault coefficient J, a tension fluctuation abnormal coefficient W and a drum expansion stroke abnormal coefficient F, and judging corresponding faults of the uncoiler according to the coefficients. According to the method, the degradation trend of the running state of the uncoiler is monitored through the classification indexes, when the classification indexes are abnormal, the faults of the uncoiler are forecasted, operation and equipment management personnel are guided to take countermeasures, and normal production and running of the cold rolling uncoiler are supported.

Description

Technical Field

[0001] The present invention relates to the technical field of mechanical equipment monitoring and diagnosis, and in particular to an intelligent operation and maintenance online monitoring method for a cold rolling mill uncoiler. Background Art

[0002] The uncoiler is a key equipment at the head of the cold rolling unit. The state of the uncoiler equipment is directly related to whether the unit can produce normally and operate stably. The cold rolling uncoiler is widely used in cold rolling strip workshops. All units that use strip coils as blanks, such as continuous pickling, shearing, continuous heat treatment and boundary coating units, must be equipped with uncoilers to complete the processes of strip uncoiling, straightening and feeding. Therefore, it is very important to accurately grasp the equipment state of the uncoiler.

[0003] The cold rolling uncoiler is used to unwind the steel coil and keep it working continuously. It consists of a mandrel, a transmission system and an external support device. The mandrel is driven by a motor to rotate to complete the normal unwinding action, and at the same time, the expansion and contraction cylinder is used to cooperate with the coiling trolley to complete the coiling action. The transmission system transmits the torque of the motor to the mandrel through a two-stage gear box to rotate the mandrel. The external support device ensures that there is no deformation when the maximum unwinding amount is on the mandrel, and the movable external support ensures that the coiling trolley automatically coils.

[0004] Common failures of uncoilers include burning of motor sliding bearings, damage to transmission gearbox bearings, high temperature of reduction box bearings, explosion of copper slide bars, broken shoulders of fan-shaped blocks, broken transmission pins of general caps, broken liner screws, severe wear of drum keyways causing four-sided pyramid sleeves to slide and get stuck, insufficient expansion and contraction of the drum, displacement of the base frame, etc. These are manifested in abnormal motor operation (high sliding bearing temperature and large vibration), abnormal rotation of the uncoiler (abnormal tension, inverter current alarm, abnormal vibration and temperature data of the gearbox), abnormal sound of drum rotation (broken fan-shaped blocks and broken wings of four-sided pyramids), and inability to shrink or open the drum.

[0005] At present, the uncoiler failure is basically repaired after the fact. The uncoiler failure will cause unplanned shutdown of the unit, increase the maintenance time and production cost of the unit, affect the normal production operation of the cold rolling production line, and there are certain safety hazards. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide an online monitoring method for intelligent operation and maintenance of a cold rolling mill uncoiler. The method uses specialized detection means to monitor the characteristic information of common faults of the uncoiler in real time, realizes online monitoring of the uncoiler status, and gives early warnings for uncoiler motor failures, gearbox bearing failures, reel failures, reel fan block breakage, and four-sided pyramid wing breakage, thereby avoiding unplanned shutdown of the unit, saving maintenance time, and supporting the normal production operation of the cold rolling production line.

[0007] In order to solve the above technical problems, the cold rolling mill uncoiler intelligent operation and maintenance online monitoring method of the present invention comprises the following steps:

[0008] Step 1: Obtain the data of the uncoiler motor current, sliding bearing temperature, uncoiler tension, hydraulic expansion and contraction cylinder tensioning solenoid valve pressure, and reel expansion and contraction stroke from the equipment intelligent operation and maintenance platform, and respectively set vibration sensors and sound sensors on the load side of the uncoiler motor and the bearing seats on both sides of the gearbox input and output shafts;

[0009] Step 2: Collect the original vibration signal Xi output by the vibration sensor, perform spectrum analysis on the original vibration signal Xi, and extract the characteristic signal of the uncoiler; the total vibration value A of the original vibration signal is obtained by the discrete value Xi (i=1, 2, ..., N) of the original vibration signal,

[0010]

[0011] in, is the average value of the vibration signal Xi,

[0012] Step 3: Establish the mechanical vibration fault characteristics caused by poor alignment of the uncoiler shaft system. For the acquired original vibration signal, extract the vibration amplitude component X at the 2 times speed frequency through spectrum analysis FFT transformation. 2 (t), reconstruct the fault signal caused by poor installation alignment by back calculation and superposition, and calculate the poor installation alignment fault coefficient G,

[0013] G=X 2 (t) / A (2)

[0014] Monitor the installation misalignment fault coefficient G, and predict the shaft system installation misalignment fault when G>30%;

[0015] Step 4: Extract the mechanical vibration fault characteristics of the uncoiler gearbox operating state fault. The original vibration signal obtained from the gearbox bearing seat is transformed by FFT to extract the vibration amplitude component Xi(t) (i=1, 2, 3) at the gearbox meshing 1-fold to 3-fold speed frequency. The gearbox fault signal is reconstructed by back calculation and superposition, and the gearbox fault coefficient H is calculated.

[0016]

[0017] Monitor the gearbox fault coefficient H. When H>40%, warn of abnormal gearbox tooth surface meshing operation status fault;

[0018] Step 5: Establish the classification index of abnormal sound of the uncoiler drum rotation, set the standard value Ls according to the sound history data of the normal operation of the uncoiler, detect the real-time sound data Lr of the uncoiler drum by the sound sensor, and calculate the abnormal sound fault coefficient M of the uncoiler drum rotation.

[0019] M=(L r -L s ) / L s ×100% (4)

[0020] Monitor the abnormal noise fault coefficient M of the uncoiler drum rotation. When M>50%, it will predict the abnormal noise fault of the uncoiler drum rotation.

[0021] Step 6: Establish the classification index of abnormal operation of the uncoiler motor, monitor the temperature rise of the sliding bearings on both sides of the motor, set the temperature rise on the drive side as Td and the temperature rise on the free side as Tf, and calculate the motor abnormal operation fault coefficient J.

[0022] J=(T f -40)*(T d -40)×[(T d -T f ) / 5]×100% (5)

[0023] Monitor the motor abnormal operation fault coefficient J, and when J>0, predict the motor abnormal operation fault caused by the sliding bearing temperature rise;

[0024] Step 7: Determine if the tension data fluctuates abnormally. Monitor the tension data S of the unwinder. For the acquired historical tension data, obtain the upper limit Smax and lower limit Smin of the tension threshold after data cleaning. Calculate the tension fluctuation abnormality coefficient W.

[0025] W=(SS max )×(S min -S) (6)

[0026] Monitor the tension fluctuation abnormal coefficient W, and predict the tension fluctuation abnormal fault when W<0;

[0027] Step 8: Determine the abnormal expansion and contraction stroke of the uncoiler drum. Monitor the expansion and contraction stroke data Q of the drum. According to the technical specifications of the uncoiler, obtain the maximum value Qmax and minimum value Qmin of the expansion and contraction stroke of the drum. Calculate the abnormal coefficient F of the expansion and contraction stroke of the drum.

[0028] F=(QQ max )×(Q min -Q) (7)

[0029] Monitor the abnormal coefficient F of the reel expansion and contraction stroke, and when F<0, predict the abnormal failure of the reel expansion and contraction stroke.

[0030] Furthermore, when a uncoiler failure is predicted, the uncoiler operation status is displayed in red.

[0031] Furthermore, when an abnormal noise fault is predicted in the reel rotation of the uncoiler, the state of the reel sector blocks and the wings of the four-sided pyramid is checked to determine whether they are broken.

[0032] Furthermore, when an abnormal failure of the reel expansion and contraction stroke is predicted, check the state of the reel general cap transmission pin, liner screw, reel fan block and four-sided pyramid wings to determine whether there is any breakage.

[0033] Since the cold rolling mill unit uncoiler intelligent operation and maintenance online monitoring method of the present invention adopts the above technical scheme, that is, the method collects the uncoiler operation status data, and respectively sets vibration sensors and sound sensors at the corresponding positions of the uncoiler motor and gearbox; collects the original vibration signal through the vibration sensor, and timely obtains the total vibration value of the original vibration signal after spectrum analysis; respectively calculates the uncoiler shaft system installation misalignment fault coefficient G, gearbox fault coefficient H, uncoiler reel rotation abnormal noise fault coefficient M, motor operation abnormal fault coefficient J, tension fluctuation abnormal coefficient W and reel expansion and contraction stroke abnormal coefficient F, and judges the corresponding fault of the uncoiler accordingly. The method uses classification indicators to monitor the deterioration trend of the uncoiler operation status. When the classification indicators are abnormal, the uncoiler fault is predicted, and the operation and equipment management personnel are guided to take countermeasures to support the normal production and operation of the cold rolling uncoiler. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments:

[0035] Figure 1 It is a flowchart of the online monitoring method for intelligent operation and maintenance of the uncoiler of a cold rolling mill according to the present invention. DETAILED DESCRIPTION

[0036] Implementation example Figure 1 As shown, the intelligent operation and maintenance online monitoring method of the cold rolling mill uncoiler of the present invention comprises the following steps:

[0037] Step 1: Obtain the data of the uncoiler motor current, sliding bearing temperature, uncoiler tension, hydraulic expansion and contraction cylinder tensioning solenoid valve pressure, and reel expansion and contraction stroke from the equipment intelligent operation and maintenance platform, and respectively set vibration sensors and sound sensors on the load side of the uncoiler motor and the bearing seats on both sides of the gearbox input and output shafts;

[0038] Step 2: Collect the original vibration signal Xi output by the vibration sensor, perform spectrum analysis on the original vibration signal Xi, and extract the characteristic signal of the uncoiler; the total vibration value A of the original vibration signal is obtained by the discrete value Xi (i=1, 2, ..., N) of the original vibration signal,

[0039]

[0040] in, is the average value of the vibration signal Xi,

[0041] Step 3: Establish the mechanical vibration fault characteristics caused by poor installation alignment of the unwinder shaft system. For the acquired original vibration signal, extract the vibration amplitude component X at the 1-fold speed frequency and the 2-fold speed frequency through spectrum analysis FFT transformation. 2 (t), reconstruct the fault signal caused by poor installation alignment by back calculation and superposition, and calculate the poor installation alignment fault coefficient G,

[0042] G=X 2 (t) / A (2)

[0043] Monitor the installation misalignment fault coefficient G, and predict the shaft system installation misalignment fault when G>30%;

[0044] Step 4: Extract the mechanical vibration fault characteristics of the uncoiler gearbox operating state fault. The original vibration signal obtained from the gearbox bearing seat is transformed by FFT to extract the vibration amplitude component Xi(t) (i=1, 2, 3) at the gearbox meshing 1-fold to 3-fold speed frequency. The gearbox fault signal is reconstructed by back calculation and superposition, and the gearbox fault coefficient H is calculated.

[0045]

[0046] Monitor the gearbox fault coefficient H. When H>40%, warn of abnormal gearbox tooth surface meshing operation status fault;

[0047] Step 5: Establish the classification index of abnormal sound of the uncoiler drum rotation, set the standard value Ls according to the sound history data of the normal operation of the uncoiler, detect the real-time sound data Lr of the uncoiler drum by the sound sensor, and calculate the abnormal sound fault coefficient M of the uncoiler drum rotation.

[0048] M=(L r -L s ) / L s ×100% (4)

[0049] Monitor the abnormal noise fault coefficient M of the uncoiler drum rotation. When M>50%, it will predict the abnormal noise fault of the uncoiler drum rotation.

[0050] Step 6: Establish the classification index of abnormal operation of the uncoiler motor, monitor the temperature rise of the sliding bearings on both sides of the motor, set the temperature rise on the drive side as Td and the temperature rise on the free side as Tf, and calculate the motor abnormal operation fault coefficient J.

[0051] J=(T f -40)*(T d -40)×[(T d -T f ) / 5]×100% (5)

[0052] Monitor the motor abnormal operation fault coefficient J, and when J>0, predict the motor abnormal operation fault caused by the sliding bearing temperature rise;

[0053] Step 7: Determine if the tension data fluctuates abnormally. Monitor the tension data S of the unwinder. For the acquired historical tension data, obtain the upper limit Smax and lower limit Smin of the tension threshold after data cleaning. Calculate the tension fluctuation abnormality coefficient W.

[0054] W=(SS max )×(S min -S) (6)

[0055] Monitor the tension fluctuation abnormal coefficient W, and predict the tension fluctuation abnormal fault when W<0;

[0056] Step 8: Determine the abnormal expansion and contraction stroke of the uncoiler drum. Monitor the expansion and contraction stroke data Q of the drum. According to the technical specifications of the uncoiler, obtain the maximum value Qmax and minimum value Qmin of the expansion and contraction stroke of the drum. Calculate the abnormal coefficient F of the expansion and contraction stroke of the drum.

[0057] F=(QQ max )×(Q min -Q) (7)

[0058] Monitor the abnormal coefficient F of the reel expansion and contraction stroke, and when F<0, predict the abnormal failure of the reel expansion and contraction stroke.

[0059] Preferably, when a uncoiler failure is predicted, the uncoiler operating status is displayed in red.

[0060] Preferably, when an abnormal noise fault is predicted in the reel rotation of the uncoiler, the state of the reel fan-shaped block and the wings of the four-sided pyramid is checked to determine whether there is any breakage.

[0061] Preferably, when an abnormal failure of the reel expansion and contraction stroke is predicted, the state of the reel cap transmission pin, the lining screw, the reel fan block and the four-sided pyramid wings is checked to determine whether there is any breakage.

[0062] Aiming at the main failure modes of the uncoiler, this method collects data such as the uncoiler motor current, sliding bearing temperature, uncoiler tension, hydraulic expansion and contraction cylinder tensioning solenoid valve pressure, and reel expansion and contraction stroke from the remote equipment operation and maintenance platform, and uses classification indicators to realize intelligent monitoring of the uncoiler operation status, timely discover various defects of the uncoiler, realize real-time early warning of the uncoiler operation status, guide the remote intelligent operation and maintenance of the uncoiler, avoid long-term unplanned shutdown of the uncoiler, greatly save maintenance time, and ensure the normal production and operation of the cold rolling uncoiler.

[0063] This method makes up for the deficiency that the online monitoring information of the process data of the current intelligent operation and maintenance equipment of the uncoiler cannot be effectively utilized, and solves the problems of the uncoiler motor and gearbox bearing failures, the inability to retract or open the reel, the breakage of the reel fan-shaped blocks, and the unpredictable equipment status before the breakage of the four-sided pyramid wings. It provides a scientific basis for the stable operation of the cold rolling uncoiler, the timely replacement of the uncoiler parts and the intelligent operation and maintenance.

Claims

1. A method for online monitoring of intelligent operation and maintenance of a cold rolling mill unwinder, characterized in that The steps include: Step 1: Obtain the data of the uncoiler motor current, sliding bearing temperature, uncoiler tension, hydraulic expansion and contraction cylinder tensioning solenoid valve pressure, and reel expansion and contraction stroke from the equipment intelligent operation and maintenance platform, and respectively set vibration sensors and sound sensors on the load side of the uncoiler motor and the bearing seats on both sides of the gearbox input and output shafts; Step 2: Collect the original vibration signal Xi output by the vibration sensor, perform spectrum analysis on the original vibration signal Xi, and extract the characteristic signal of the uncoiler; the total vibration value A of the original vibration signal is obtained by the discrete value Xi (i=1, 2, ..., N) of the original vibration signal, in, is the average value of the vibration signal Xi, Step 3: Establish the mechanical vibration fault characteristics caused by poor installation alignment of the uncoiler shaft system. For the original vibration signal obtained, extract the vibration amplitude component X2(t) at the 2 times speed frequency through spectrum analysis FFT transformation. Reconstruct the fault signal caused by poor installation alignment through back calculation and superposition, and calculate the poor installation alignment fault coefficient G. G=X2(t) / A (2) Monitor the installation misalignment fault coefficient G, and predict the shaft system installation misalignment fault when G>30%; Step 4: Extract the mechanical vibration fault characteristics of the uncoiler gearbox operating state fault. The original vibration signal obtained from the gearbox bearing seat is transformed by FFT to extract the vibration amplitude component Xi(t) (i=1, 2, 3) at the gearbox meshing 1-fold to 3-fold speed frequency. The gearbox fault signal is reconstructed by back calculation and superposition, and the gearbox fault coefficient H is calculated. Monitor the gearbox fault coefficient H. When H>40%, warn of abnormal gearbox tooth surface meshing operation status fault; Step 5: Establish the classification index of abnormal sound of the uncoiler drum rotation, set the standard value Ls according to the sound history data of the normal operation of the uncoiler, detect the real-time sound data Lr of the uncoiler drum by the sound sensor, and calculate the abnormal sound fault coefficient M of the uncoiler drum rotation. M=|(L r -L s )| / L s ×100% (4) Monitor the abnormal noise fault coefficient M of the uncoiler drum rotation. When M>50%, it will predict the abnormal noise fault of the uncoiler drum rotation. Step 6: Establish the classification index of abnormal operation of the uncoiler motor, monitor the temperature rise of the sliding bearings on both sides of the motor, set the temperature rise on the drive side as Td and the temperature rise on the free side as Tf, and calculate the motor abnormal operation fault coefficient J, J = (T f -40)*(T d -40)×[(T d -T f ) / 5]×100% (5) Monitor the motor abnormal operation fault coefficient J, and when J>0, predict the motor abnormal operation fault caused by the sliding bearing temperature rise; Step 7: Determine if the tension data fluctuates abnormally. Monitor the tension data S of the unwinder. For the acquired historical tension data, obtain the upper limit Smax and lower limit Smin of the tension threshold after data cleaning. Calculate the tension fluctuation abnormality coefficient W. W=(S-S max )×(S min -S) (6) Monitor the tension fluctuation abnormal coefficient W, and predict the tension fluctuation abnormal fault when W<0; Step 8: Determine the abnormal expansion and contraction stroke of the uncoiler drum. Monitor the expansion and contraction stroke data Q of the drum. According to the technical specifications of the uncoiler, obtain the maximum value Qmax and minimum value Qmin of the expansion and contraction stroke of the drum. Calculate the abnormal coefficient F of the expansion and contraction stroke of the drum. F=(Q-Q max )×(Q min -Q) (7) Monitor the abnormal coefficient F of the reel expansion and contraction stroke, and when F<0, predict the abnormal failure of the reel expansion and contraction stroke.

2. The method for online monitoring of intelligent operation and maintenance of a cold rolling mill unwinder according to claim 1, characterized in that: When a uncoiler failure is predicted, the uncoiler operating status is displayed in red.

3. The method for online monitoring of intelligent operation and maintenance of a cold rolling mill unwinder according to claim 1, characterized in that: When abnormal noise is predicted during the rotation of the uncoiler drum, check the status of the drum fan blocks and the wings of the four-sided pyramid to determine whether they are broken.

4. The method for online monitoring of intelligent operation and maintenance of a cold rolling mill uncoiler according to claim 1, characterized in that: When an abnormal failure of the reel expansion and contraction stroke is predicted, check the status of the general cap transmission pin, liner screws, reel fan blocks and four-sided pyramid wings to determine whether there is any breakage.

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