KR100969243B1 - Method for testing a fault of a rotating component by modeling transfer function indicated by power signature line of a motor, electric generator - Google Patents

Abstract

PURPOSE: A rotating component fault determination method is provided to accurately find the efficiency of a unit facility and the cause of energy loss. CONSTITUTION: A decibel measurement data of a normal level rotating equipment component and a fault level rotating equipment component are obtained according to a fault frequency line(S10). A decibel increasing factor and a zoom factor are obtained(S20). A rating level based boundary value of the rotating equipment component is automatically set to a motor current signal analysis system(S30). The level of the rotating equipment component is determined through the comparison between the decibel measurement value of the rotating equipment component and the rating level based boundary value(S40).

Description

Method for Testing a Fault of a Rotating Component by Modeling Transfer Function Indicated by Power Signature Line of a Motor, Electric Generator}

The present invention relates to a method for determining a fault of a rotating equipment component using a transmission function of a facility state indicated by a motor and a generator power sign. By calculating the decibel rise factor and zoom factor according to the deterioration or deterioration and automatically setting thresholds for each grade, it is possible not only to accurately determine the cause of the efficiency and energy loss of the unit, In predicting the remaining life of rotating equipment components according to the abnormality and the progress of deterioration, the method of determining defects of rotating equipment components using the transfer function of the equipment status indicated by the improved motor and generator power signs to realize the proactive maintenance. It is about.

Rotating equipment components such as motors are the core parts for driving various process equipments, and defects such as abnormalities and deterioration of motors lead to the interruption of the entire process equipment. Proactive maintenance, which diagnoses component abnormalities and deterioration, predicts the remaining life, and takes countermeasures in advance, is a very important task.

Existing equipment diagnosis diagnoses defects such as bearing and eccentricity of motor through vibration technique. This vibration technique measures only the amplitude of the signal and the frequency in decibels by calculation, but it does not measure the rotational speed of the motor.In order to analyze the rotational speed of the motor, a tachometer is separately attached and an important motor or In the case of high voltage motors, an accelerometer is installed on the shaft of the motor. However, these tachometers and accelerometers are expensive equipment, and the measurement of the rotational speed by connecting a separate tachometer such as a tachometer to each rotating equipment component of the process equipment acts as a factor of the cost increase due to the increase of maintenance and maintenance costs. It is unfavorable in terms of economics and efficiency, and poses a safety risk.

The present invention has been made to solve all the problems of the prior art as described above, by calculating the decibel rising factor and the zoom factor by deriving the transfer function of the installation state indicated by the motor and the power sign of the generator to determine the threshold value for each grade of the motor By automatically setting the current signal analysis system (MCSA) and applying the contribution rate, the cause of efficiency and energy loss of the unit equipment due to the abnormality or deterioration of the rotating equipment component is accurately analyzed, and the rotating equipment component according to the progress of the abnormality or degradation. The purpose of the present invention is to provide a method for determining faults of rotating equipment components by using the transfer function of the equipment status indicated by the improved motor and generator power signs to accurately predict the remaining life of the engine.

Rotating equipment component defect determination method using the transmission function of the equipment state displayed by the motor and the generator power sign of the present invention for solving the above problems is the decibel measurement of the normal-grade rotating equipment components for each defective frequency line indicating the defect of the rotating equipment components Obtaining the rotational equipment component determination data for acquiring the decibel measurement data of the data and the defect rating rotating equipment component; A mapping factor acquiring step of acquiring a decibel rising factor and a zoom factor by mapping the decibel measurement data of the normal grade rotating equipment component and the decibel measurement data of the defective grade rotating equipment component to a transfer function; A determination criteria automatic setting step of calculating threshold values for each judgment class of the rotating equipment component by mapping the decibel rising factor and the zoom factor to the decibel data of the rotating equipment component and automatically setting them in the motor current signal analysis system; And a rotation equipment component class determining step of determining a grade of the rotating equipment component by comparing the decibel measurement value of the rotating equipment component with the threshold value for each of the determination classes.

Rotation equipment component defect determination method using the transmission function of the equipment state displayed by the motor and generator power signs of the present invention through the induction of the transfer function displayed by the current signal of the rotating equipment components without installing an expensive tachometer or accelerometer Detects defects such as abnormality and deterioration of rotating equipment by setting threshold values for each grade from the decibel rise factor and zoom factor that are calculated. Therefore, it is possible to accurately analyze the causes of efficiency and energy loss of rotating equipment by using economical and efficient methods. Can be.

In addition, by accurately predicting the remaining life of rotating equipment components according to the progress of defects such as abnormalities or deterioration, it is possible to realize the planned maintenance to replace defective parts in a timely manner, which is caused by the process interruption caused by the failure of rotating equipment components. Economic losses can also be prevented.

Diagnosis of these rotating installations includes deterioration of the stator windings, rotor bar cracking, rotor air gaps, shaft alignment, and bearing degradation. ), Dynamic / static eccentricity, gear and belt imperfections, and instantaneous torque can be suitably applied.

Hereinafter, with reference to the accompanying drawings will be described in detail the method of determining the defects of the rotational equipment components using the transfer function of the equipment state displayed by the motor and generator power signs of the present invention.

1 is a flow chart (Flow Chart) of a method for determining a defect of a rotating equipment component using a transfer function of the equipment state indicated by a motor and a generator power sign according to the present invention.

Referring to FIG. 1, a method for determining a defect of a rotational equipment component using a transmission function of a facility state indicated by a motor and a generator power sign of the present invention includes a motor rating determination data acquiring step S10 and a mapping factor acquiring step S20. , Determination criteria automatic setting step (S30) and motor rating determination step (S40).

The rotating equipment component means a component of a motor and a generator and a component of a rotating load machine associated with the motor. Hereinafter, a method of determining a defect of the present invention will be described with an embodiment of diagnosing a motor as a rotating equipment component.

To describe this step by step, first, the motor rating determination data acquiring step (S10) of acquiring the decibel measurement data of the normal grade motor and the decibel measurement data of the defect grade motor for each defect frequency line indicating a defect such as deterioration or abnormality of the motor. To perform.

The defect frequency line refers to a frequency line that appears in the frequency spectrum of the voltage or current signal of the motor according to the defect of each part of the motor, and is characterized by the signature formula and torque (ESA) of the electrical signature analysis (ESA) technology. Torque) corresponds to a frequency line having an actual contribution rate with respect to the corresponding frequency of the formula or a frequency line capable of determining the remaining lifetime.

Referring to the process of converting the frequency spectrum from the voltage or current signal of the motor, first, the components of the motor or the components of the rotating device or the generator in the load machine linked to the motor through the three-phase power lead from the power supply When powered by a (motor), a transformer or current transformer, commonly known as a three-phase power lead, can be installed and converted into an analog voltage or current signal from the motor. Next, a high pass signal of the analog voltage or current signal is removed through a low pass filter to prevent signal distortion due to aliasing (Anti-Aliasing), and then the analog voltage or current signal of the motor is A / A. After converting into digital signal which can be analyzed by computer through Analog to Digital Converter, DSA (Dynamic Signature Analysis) analyzes voltage or current signal of motor. In case of signal analysis through DSA, after windowing operation according to control command of controller, signal demodulation is performed so that accurate frequency conversion can be performed by harmonic component of power frequency. Eliminate modulated frequency components. In this case, the signal demodulation may be performed by removing a specific modulation frequency using a notch filter. After demodulating the signal, Fourier Frequency Transform (FFT) is performed to generate a frequency spectrum, and from the decibels of the line having a contribution rate from the frequency spectrum, it is possible to diagnose a defect of a rotating equipment such as a motor. Will be.

The defective frequency line can be detected from the rotational speed of the motor as an example. The rotational speed of the motor is measured using an acceleration sensor or a rotational speed sensor, or the frequency conversion of the motor current signal using a motor current signal analysis (MCSA) without using the acceleration sensor and the rotational speed sensor ( After the Fourier Frequency Transform (FFT) is performed, the rotational speed of the motor can be accurately measured by a technique of analyzing the frequency spectrum lines obtained by the frequency transformation.

When the rotational speed of the motor is detected, a fault frequency line for each part of the motor may be set from the equation defined by the detected rotational speed of the motor and the motor current signal analysis (MCSA) technique. FIG. 6 is a diagram illustrating equations defined by a motor current signal analysis (MCSA) technique. The stator, rotor, shaft or Defective frequency lines for motor components such as bearings can be distinguished. At this time, the formula defined by the motor current signal analysis (MCSA) technique is preferably used according to the criteria of the NASA (National Aeronautics and Space Administration).

In the state where the fault frequency line is set as described above, through a fault test using a normal motor in a normal state and a fault motor in which an artificial fault is generated (S11), the normal frequency is represented by each fault frequency line indicating a fault. Acquire data obtained by measuring the decibels of the motor and the fault grade motor (S12). At this time, it is preferable to classify the state of the motor into several classes for each part of the motor, and to obtain data obtained by measuring the decibels of the motors classified into each class. For example, motor grades can be classified into four grades: A, which is a normal grade, B, which requires action within 12 months, C, which requires action within 6 months, and D, which requires immediate action. Can be. At this time, it is possible to obtain motors of various defect grades by differentiating the respective parts by a method such as adjusting the tightening degree of the motor to give an arbitrary defect to the motor. The parts of the motor include each component constituting the motor and a rotating body that is rotated in association with the motor. The stator (line insulation, winding insulation, winding asymmetry check) and the rotor (rotator bar damage, static eccentricity) , Dynamic eccentricity, air gap defect check), shaft (axis alignment, balance check), bearing (outer ring, inner ring, ball), and the like. However, it is not necessarily limited thereto, and includes components for each part of the motor which are not listed.

At this time, for the normal grade motor and the defective class motor, it is repeated several times for the defective frequency line for each motor part to acquire data obtained by measuring the decibels of the motor.

2 is a graph showing the decibel measurement results for a normal grade motor and a defect grade motor for a defective frequency line. As shown in FIG. 2, the motor decibels of the normal grade (A) with respect to the defect frequency 177.5 Hz show a distribution of 0.17-0.24 (db), but the motor decibels of the defect grades (B, C, D) are each rated B. It shows 0.31 ~ 0.40 (db), C class 0.41 ~ 0.48 (db), D class 0.48 ~ 0.53 (db), and the higher the degree of defect, the higher the decibel of the motor.

After performing the motor grade determination data acquisition step (S10) for each fault frequency line, the decibel measurement data of the normal grade motor and the decibel measurement data of the defect grade motor are substituted into Equation 2 below (S21). In operation S22, a decibel increase factor and a zoom factor are obtained through Equation 2. This is to obtain the mapping factor which is a criterion for the motor rating by quantifying the decibel difference between the normal motor and the defective motor in the defective frequency line. In Equation 2, the decibel rise factor (B _DB = B _DZ / N _SP, C _DB = C _DZ / N _SP, D _DB = D _DZ / N _SP ) is used for motor dead zones of Class B, C, D and Class A motors. Set point as a ratio, zoom factor (B _ZM = (B _H -B _L ) / (N _H -N _L ), C _ZM = (C _H -C _L ) / (N _H -N _L ), D _ZM = (D _H -D _L ) / (N _H -N _L )) is the difference between the high and low decibel values of B, C and D motors, and the difference between the high and low decibel values of Class A motors. It is expressed as a ratio.

As described above, the decibels of the faulty motors are increased according to the degree of defects compared to the normal motors, and the decibels increase factor indicates the degree of change of the decibels of the normal motor and the elevated decibels of the defective motor. Can be. In addition, the decibel deviation of the motor in a steady state and the decibel deviation of a motor in an abnormal defect state are also different from each other. Such a change in the decibel deviation of the normal motor and the decibel deviation of the defective motor may be represented by a zoom factor.

For example, the transfer function formula expresses the decibel relationship between a class A (normal) motor and a class B, C, or D (defective) motor as a function such as Equation 2 to obtain a decibel rising factor and a zoom factor. The obtained decibel rise factor and zoom factor are used to obtain the boundary value between the grades of the motor for the diagnosis of the motor.

As described above, the transfer function equation shows that the two mapping factors (decibel rise factor and zoom factor) of the defect grade motor change according to the degree of defect, and the ratio between the motor dead zone and the set point and the high / low decibel value Expressed as the ratio of the difference, the mapping factor (decibel rising factor and zoom factor) obtained through the transfer function induction is applied to Equation 4 to set the boundary value for each judgment class of the actual driving motor, and from this, the efficiency and energy of the driving motor. The cause of the loss can be diagnosed and the remaining life of the operating motor can be predicted.

FIG. 3 is a view for explaining a method for determining a defect of a rotating equipment component using a transfer function of the equipment state indicated by a motor and a generator power signal according to the present invention.

Referring to FIG. 3, the mapping factor acquisition step S20 will be described in more detail. First, the average and standard deviation of the decibel measurement data acquired for each motor grade in the motor rating determination data acquisition step S10 are obtained to obtain a parent mean. Estimate. To illustrate the process of calculating the mapping factor from the decibel measurement data, the motor ratings are four: A (normal), B (12 months), C (6 months), and D (immediate action). In the case of classifying as an example, first, the high decibel value (HI VALUE) and the low decibel value (LOW VALUE) for the normal (A) motor and the fault rating (B, C, D) motor according to the following formula 1 To calculate.

[Equation 1]

Class A motors: N _H = X _N + 2.58 * σ _N / √n _N , N _L = X _N -2.58 * σ _N / √n _N (N _H : high decibel value, N _L : low decibel value, X _N : Class A motor decibel, σ _N : Standard deviation of class A motor, n _N : Sample number of class A motor) (99% reliability estimate)

Class B motors: B _H = X _B + 2.58 * σ _B / √n _B , B _L = X _B -2.58 * σ _B / √n _B (B _H : high decibel value, B _L : low decibel value, X _B : Class B motor average decibel, σ _B : Class B motor standard deviation, n _B : Class B motor sample) (99% reliability estimate)

Class C motor: C _H = X _C + 2.58 * σ _C / √n _C , C _L = X _C -2.58 * σ _C / √n _C (C _H : high decibel value, C _L : low decibel value, X _C : Class C motor average decibel, σ _C : Standard deviation of class C motors, n _C : Sample number of class C motors) (99% reliability estimate)

Class D Motor: D _H = X _D + 2.58 * σ _D / √n _D , D _L = X _D -2.58 * σ _D / √n _D (D _H : high decibel value, D _L : low decibel value, X _D : Class D motor average decibel, σ _D : Standard deviation of class D motor, n _D : Sample number of class D motor) (99% reliability estimate)

Also, set point of Class A motor is calculated from decibel data (P) of Class A motor, and each defect class (B, C, D) from decibel data (Q) of Class B motor. Calculate the dead zone of the motor. At this time, the dead zone (DEADZONE) means the decibel of the distance from the 0 decibel to the set point of each defect class (B, C, D) motor.

In addition, a zoom factor and a DB rise factor are calculated by applying a transfer function between decibel data P of a normal grade motor and decibel data Q of a defective grade motor.

[Equation 2]

Class A Motors: N _SP = Med (x) (N _SP : Setpoint)

Class B motors: B _DZ = B _L + (B _H -B _L ) / 2, B _ZM = (B _H -B _L ) / (N _H -N _L ), B _DB = B _DZ / N _SP

(B _DZ : Dead Zone, B _ZM : Zoom Factor, B _DB : Decibel Rise Factor)

Class C motors: C _DZ = C _L + (C _H -C _L ) / 2, C _ZM = (C _H -C _L ) / (N _H -N _L ), C _DB = C _DZ / N _SP

(C _DZ : Dead Zone, C _ZM : Zoom Factor, C _DB : Decibel Rise Factor)

Class D motors: D _DZ = D _L + (D _H -D _L ) / 2, D _ZM = (D _H -D _L ) / (N _H -N _L ), D _DB = D _DZ / N _SP

(D _DZ : Dead Zone, D _ZM : Zoom Factor, D _DB : Decibel Rise Factor)

Here, calculating the setpoint of a class A motor as a median value accurately sets the setpoint of the normal motor by excluding data outside the error range that degrades the accuracy of the frequency line decibel value of the normal motor. For sake.

In this way, the mapping factor (decibel rising factor, zoom factor) can be calculated from the decibel distribution for each defective frequency line of the motor of the normal class and the motor of the defective class which has artificially generated a fault.

After decibel measurement data of the normal grade motor and the defect grade motor are mapped to a transfer function, a mapping factor acquisition step (S20) of obtaining a zoom factor and a decibel rise factor (DB FACTOR) is performed. And performing mapping to apply the zoom factor to the decibel data R of the driving motor (S31), calculating a threshold value between the grades of the driving motor, and automatically setting the driving factor to the motor current signal analysis system (S32). To perform.

To this end, a high decibel value (HIGH VALUE), a low decibel value (LOW VALUE), and a SET POINT of the driving motor are calculated from the decibel data R of the driving motor according to Equation 3 below.

[Equation 3]

R _H = Max (xi) = X _R + 2.58 * σ _R / √n _R ,

R _L = Min (xi) = X _R -2.58 * σ _R / √n _R ,

R _SP = R _L + (R _H -R _L ) / 2

(R _H : high decibel value, R _L : low decibel value, R _SP : set point, X _R : driving motor average decibel, σ _R : driving motor standard deviation, n _R : driving motor sample number) (99% reliability estimate)

Referring to FIG. 3, each defect class (B, C, D) is performed by performing a mapping that applies a decibel rising factor and a zoom factor to the decibel data R of the driving motor in order to set a threshold value between the driving motors. Is converted into predicted decibel data (S), and from decibel data (S) of each defect class (B, C, D), between AB grades (rB) and BC, which are decibels between each defect class of the driving motor. The boundary value rC and the boundary value rD between CD grades are calculated according to the following expression (4).

[Equation 4]

Boundary value between AB grades: rB = rB _DZ- (R _SP -R _L ) * B _ZM , rB _DZ = R _SP * B _DB

Between BC grade _{boundaries: rC = rC DZ - (R} SP - R L) * C ZM, rC DZ = R SP * C DB,

CD grade boundaries _{between: rD = rD DZ - (R} SP - R L) * D ZM, rD DZ = R SP * D DB,

rB _DZ , rC _DZ , and rD _DZ are the driving motors calculated by mapping applying the B, C, D decibel increase factor (B _DB , C _DB , D _DB ) to the set point (R _SP ) of the driving motor, respectively. The predicted decibel set point for B, C, and D classes.

rB, rC, and rD are boundary values between AB grades, BC grades, and CD grades, respectively, and are based on the B, C, and D predicted decibel set points. It is calculated by applying.

That is, the prediction decibel deviation for each grade is calculated by performing mapping that applies the B, C, D grade zoom factors (B _ZM , C _ZM , D _ZM ) to the deviation (R _SP -R _L ) of the driving motor. The inter-boundary value is calculated by subtracting the prediction decibel deviation for each grade from the B, C, and D grade prediction decibel set points (rB _DZ , rC _DZ , rD _DZ ).

4 is a diagram showing the automatic setting of the determination model in the MCSA by the method of determining the failure of the rotary equipment using the transfer function of the equipment state indicated by the motor and generator power signs according to the present invention, as shown in FIG. It shows that the mapping factor is obtained by applying the transfer function from the decibel data of the motors of each class, and the threshold value between grades for determining the class of the driving motor is automatically set as the determination model (H). As shown in FIG. 4, the boundary value between grades calculated by the transfer function application and the mapping factor acquisition may be input to the motor current signal analysis (MCSA) system to automatically set the determination model H.

Motor faults are classified into faults formulated by MCSA such as stator, rotor, alignment, bearing, eccentricity, and load stages by torque analysis. It reports the detailed judgment, excess rate, measured value, boundary value, and the cause of the defect for one part and the defect setting, and includes the frequency (Frequency), distortion rate (THD%), current (RMS), and phase angle (Phase) which are the power environment at the time of measurement. ), Signal (Distortion + Noise), signal of RMS value and signal to noise and distortion rate (SINAD) which is noise ratio of RMS value are provided together.

Finally, the motor rating determination step (S40) is performed to determine the rating of the driving motor by comparing the decibel measurement value of the driving motor with the threshold value for each determination rating. FIG. 5 compares the decibel value and the threshold value of the motor fault frequency line with the result of classifying the judgment grade by performing the motor diagnosis by the MCSA as described above. The results are shown in the following table. When the decibel value of the driving motor corresponding to the defective frequency line is input in the MCSA automatically set the threshold value (M) between the grades, the decibel value of each grade is compared with the decibel value of the driving motor. From the output value, the severity (N) of the actual driving motor is diagnosed, and the remaining life is predicted and output. In addition, the rating is judged based on the relative decibel value for each defective frequency line and classified into A (normal), B (within 12 months), C (within 6 months), and D (immediate action).

At this time, the motor model, operation load (high / medium / low) mode, load type, power transmission (direct / belt / gear ratio) method, and criteria for increasing decibels are used. Classes can be classified accordingly.

As described above, the present invention obtains a mapping factor indicating the decibel relationship between the normal grade and the defective grade motor from the decibel data acquired based on the defect test of the motor for each actual grade of the defect, and applies the mapping factor to the decibel data of the driving motor. By automatically setting the boundary values between grades, it is possible to accurately analyze the causes of the efficiency and energy loss of the unit equipment and to accurately predict the remaining life from the progress of abnormality or deterioration of the rotating equipment components to realize planned maintenance. Will be.

 In the above description, the fault diagnosis of the motor and the prediction of the remaining life of the motor are performed through the method of determining the fault of the rotating equipment using the transmission function of the equipment state indicated by the motor and the generator power sign of the present invention. It is not limited to performing the diagnosis and the prediction of the remaining life, and it is not applicable, and diagnosis of abnormality and deterioration of components of motors and generators other than motors and components of rotating equipment inside the load machine interlocked with the motors without departing from the spirit of the present invention. And also applied to the control should be seen as belonging to the protection scope of the present invention.

Figure 1 is a flow chart (flow chart) of a method for determining defects of rotating equipment components using the transfer function of the equipment state displayed by the motor and generator power signs according to the present invention.

Figure 2 is a graph showing the decibel measurement results for a normal grade motor and a defect grade motor for a defective frequency line.

3 is a view for explaining a method for determining defects of rotating equipment components using the transfer function of the equipment state displayed by the motor and the generator power signs according to the present invention.

4 is a diagram showing the automatic setting of the judgment model in the MCSA by the method of determining the defects of the rotational equipment components using the transmission function of the equipment state indicated by the motor and the generator power signs according to the present invention.

FIG. 5 is a diagram illustrating a result of classifying a determination class by performing MCSA motor diagnosis of a motor defect frequency line. FIG.

Figure 6 shows the equations defined by the Motor Current Signal Analysis (MCSA) technique.

Claims (1)

Acquiring the decibel measurement data of the normal-grade rotating equipment component and the decibel measurement data of the defect-grade rotating equipment component for each defect frequency line indicating a defect of the rotating equipment component; A mapping factor acquiring step of acquiring a decibel rising factor and a zoom factor by mapping the decibel measurement data of the normal grade rotating equipment component and the decibel measurement data of the defective grade rotating equipment component to a transfer function; A determination criteria automatic setting step of calculating boundary values for each judgment grade of the rotating equipment component by mapping the decibel rising factor and the zoom factor to the decibel data of the rotating equipment component and automatically setting them in the motor current signal analysis system; And A rotating equipment component class determining step of determining a class of the rotating equipment component by comparing the decibel measurement value of the rotating equipment component with the boundary value for each of the determination classes; Rotation equipment component defect determination method using the transfer function of the equipment state indicated by the motor and the generator power signs consisting of.

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