JP4003088B2 - Rotating body abnormality diagnosis method and apparatus - Google Patents

Description

  The present invention relates to an abnormality diagnosis method and apparatus for a plurality of rotating parts used in a reduction gear, an electric motor, an axle for a railway vehicle, and the like. In particular, the present invention relates to a method and apparatus capable of detecting a defect of a rotating part without disassembling a mechanical device.

  The rotating body abnormality diagnosis device is a device that analyzes sound, temperature, vibration, and the like generated from a rotating body such as a reduction gear, an electric motor, and an axle for a railway vehicle, and diagnoses the abnormality of the rotating body. In abnormality diagnosis, wave information such as sound, temperature, and vibration is detected by a detector such as a microphone, a temperature sensor, and a vibration sensor, and the detection signal is amplified using an amplifier. The amplified detection signal is converted into a digital signal by an A / D converter and output to a diagnostic PC equipped with diagnostic software. The diagnostic software performs various types of analysis such as frequency analysis and comparative analysis on the diagnostic PC. In the comparative analysis, the obtained frequency spectrum is compared with the frequency component caused by the rotating body. The user determines the presence / absence of an abnormality based on the diagnosis result displayed on the monitor, and when the abnormality occurs, takes measures such as stopping the rotating body.

  However, due to the rotation state and the influence of the structure, the actual rotational speed of the rotating body may differ from the rotational speed for calculating the peak generation frequency at which a peak occurs when an abnormality occurs, resulting in misdiagnosis. there is a possibility. In addition, there is a problem that, in order to confirm sequential matching of frequency components, the calculation time and the calculation load are enormous.

  The present invention has been made to solve the above-described problems, and an object of the present invention is to provide a method and apparatus for preventing an erroneous diagnosis and performing an abnormality diagnosis with a small calculation load in a short time.

In order to solve the above-mentioned problem, the abnormality diagnosis method according to claim 1 of the present invention is an abnormality diagnosis method for diagnosing an abnormality of a mechanical facility including one or a plurality of sliding members,
Detecting a wave generated from the mechanical equipment;
Calculating a frequency spectrum of the wave;
A first calculation step of calculating a root mean square or partial overall of one order component band of the frequency spectrum, and a normalization value that is a root mean square or overall of the entire band of the frequency spectrum;
A second calculation step of calculating a value obtained by dividing a root mean square or partial overall of the one order component band by the normalized value, or a difference value;
Performing a comparison of the divided value or difference value with a predetermined constant;
Based on the result of the comparison and verification, have a, a step of diagnosing an abnormality of the mechanical equipment,
Calculating the frequency spectrum of the wave,
Generating a frequency spectrum data by frequency-converting the detected wave using an FFT algorithm;
Based on the obtained frequency spectrum, performing a filtering process to cut out a frequency including an abnormal peak frequency;
Performing envelope processing on the signal of the cut-out frequency including the abnormal frequency by performing the filtering process;
Generating frequency spectrum data by frequency-converting the envelope-processed signal using an FFT algorithm;
It is characterized by providing .

The abnormality diagnosis apparatus according to claim 3 of the present invention is an abnormality diagnosis apparatus for diagnosing an abnormality of a mechanical facility including one or a plurality of sliding members,
A wave detection means for detecting a wave generated from the mechanical equipment;
A frequency analysis means for calculating a frequency spectrum of the wave;
Calculate a root mean square or partial overall of one order component band of the frequency spectrum and a normalization value that is a root mean square or overall of the entire band of the frequency spectrum, and calculate a root mean square or partial of the one order component band Parameter calculation means for calculating a value obtained by dividing overall by the normalized value or a difference value;
A comparison collation means for performing a comparison collation between the divided value or the difference value and a predetermined constant;
Diagnostic means for diagnosing abnormalities in the mechanical equipment based on the result of the comparison,
The frequency analysis means includes
A first frequency processing unit for generating a frequency spectrum data by frequency-converting the detected wave using an FFT algorithm;
Based on the obtained frequency spectrum, a filter unit that performs a filter process for cutting out a frequency including an abnormal peak frequency,
An envelope unit that performs envelope processing on a signal of a cut-out frequency that includes the abnormal frequency by performing the filtering process;
A second frequency processing unit for generating frequency spectrum data by performing frequency conversion on the signal subjected to the envelope processing by an FFT algorithm;
It is characterized by providing .

According to the above, since the frequency band caused by the mechanical equipment including one or a plurality of sliding members is used, it is possible to eliminate the influence of noise and the peak of the frequency component not caused by the rotating body. Further, since it is not necessary to collate the frequency of the data actually measured with the fundamental frequency obtained by calculation and its harmonics, the calculation load can be reduced and the loss of time required for analysis can be reduced. Furthermore, since the abnormality can be reliably captured even when the level of the frequency component due to the rotating body is small, more accurate diagnosis is possible. In addition, it is possible to specify the presence / absence of an abnormality of each part of the rotating body constituting the mechanical equipment and the part thereof by only one measurement. In addition, since the detected wave is frequency-converted by the FFT algorithm to generate frequency spectrum data, it is possible to check in which frequency band the abnormal peak frequency is included.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

  FIG. 1 is a diagram showing an abnormality diagnosis apparatus 10 of the present embodiment. The abnormality diagnosis apparatus 10 includes a detection unit 11 disposed on or near the machine facility 1 to be diagnosed, an amplification unit 12 that amplifies the output of the detection unit 11, and a PC unit 30 that performs abnormality diagnosis. ing.

The mechanical equipment 1 is a mechanical equipment having roller bearings (inner ring rotation speed: 169 min ⁻¹ ) and normal gears (number of teeth: 31). The roller bearing has an inner ring that fits outside the shaft, an outer ring that fits inside the housing or the like and faces the inner ring in the radial direction, and a roller as a rolling element that is arranged to roll between the inner ring and the outer ring. ing. Here, the case where the mechanical equipment 1 is damaged, in particular, the case where the outer ring 3 is damaged will be described as an example.

  The detection unit 11 includes a vibration sensor that detects vibrations of the mechanical equipment, a rotation sensor that detects the rotation speed of the roller bearing, a temperature sensor that detects the temperature of the mechanical equipment, a sound sensor that detects sound generated from the mechanical equipment, AE, and the like. , And a plurality of sensors such as an AE sensor, etc., which are respectively or integrally disposed in the vicinity of the mechanical equipment 1 which is a diagnosis target.

  A rotation sensor detects the rotation speed of the inner ring | wheel of the roller bearing attached to the rotary body. As the rotation sensor, it is possible to use a magnetic sensor that detects a magnet arrangement on an encoder (not shown) attached to an inner ring or an outer ring of a roller bearing, a displacement sensor that detects the shape of the encoder, and the like. .

  The temperature sensor is a sensor that is disposed in the vicinity of the mechanical equipment 1 and detects the temperature of the mechanical equipment 1 or the vicinity thereof. As the temperature sensor, a contact temperature sensor such as a thermocouple or a non-contact temperature sensor such as an infrared radiation thermometer can be used. In the case of a non-contact type temperature sensor, the temperature of a rotating member such as an inner ring can be detected.

  The detection unit 11 may have all of these sensors or only a part of them according to the application or the configuration or situation of the mechanical equipment. The various sensors may be housed and integrated in one sensor unit, or may be configured separately. In some cases, the preferred location for mounting each sensor is different, and it may be possible to perform detection with higher accuracy if each sensor is a separate unit.

  The amplifying unit 12 is composed of an OP amplifier or the like, and amplifies the intensity of various signals from the detecting unit 11 to a level suitable for the processing of the PC unit 30. Various signals are output from the amplifying unit 12 to the PC unit 30. In addition, when the detection part 11 has an amplification function, or the signal which does not need amplification does not need to go through the amplification part 12 dare. In this case, the output of the detection unit 11 is sent directly to the PC unit 30.

  The PC unit 30 is a computer in which a predetermined OS and analysis / diagnosis software 40 are installed. The PC unit 30 performs various signal processing using the analysis / diagnosis software 40 functioning on the OS, and performs an abnormality diagnosis of the mechanical equipment. Of course, as the diagnostic unit 14, a dedicated diagnostic device may be used instead of software processing by the PC.

  The PC unit 30 performs A / D conversion on the received signals, and performs diagnosis using the diagnostic software 40 in the PC unit. Note that a dedicated A / D converter may be provided in the previous stage of the PC unit 30 and A / D conversion may be performed before inputting to the PC unit 30.

  The diagnostic software 40 has various analysis processing units 41, various parameter calculation units 42, a collation unit 43, and a diagnostic unit 44 as functions.

  FIG. 2 is a diagram illustrating various analysis processing units 41. The various analysis processing units 41 include a first frequency processing unit 41a, a filter unit 41b, an envelope processing unit 41c, and a second frequency processing unit 41d, and perform processing for calculating a frequency spectrum of a signal used for frequency analysis. Do.

  The first frequency processing unit 41a performs frequency conversion on the detected vibration signal, sound signal, AE signal, and the like using an FFT algorithm or the like to generate frequency spectrum data. Here, it is performed in order to investigate which frequency band the abnormal peak frequency included in the detection signal is included.

  The filter unit 41 b is a frequency filter that extracts a desired frequency band from the vibration signal output from the amplification unit 13. For this desired frequency band, a process of cutting out a frequency band including an abnormal peak frequency is performed based on the frequency spectrum obtained by the first frequency processing unit 41a. The filter unit 41b sends the filtered signal to the envelope unit 41c. The filter unit 41b may be omitted as appropriate depending on signal characteristics.

  In addition, when the expected abnormal peak frequency detected due to damage to the mechanical equipment 1 is known, this desired frequency band is set according to the abnormal frequency peak where the abnormality is expected to occur. Is also possible. For example, if the bearing is damaged, it depends on the inner ring rotation speed, the number of rolling elements, the rotation speed of the cage, the rotation speed of the rolling elements, the size of the rolling elements, the pitch circle diameter, the contact angle, etc. Thus, a unique frequency component is observed depending on the damaged part. Therefore, based on the rotation speed obtained from the rotation sensor, the occurrence region of these abnormal frequency components may be predicted, and the frequency may be cut out for the expected generation range.

The envelope unit 41c performs envelope processing on the signal transmitted from the filter unit 41b. The envelope process is to obtain an output proportional to the envelope of the input vibration waveform. The envelope unit 41c outputs the signal after the envelope processing to the second frequency processing unit 41d. The envelope portion 41c can be omitted as appropriate depending on signal characteristics.

The second frequency processing unit 41d performs frequency conversion on the signal output from the envelope unit 41c using an FFT algorithm or the like to generate frequency spectrum data. 3 and 4 are graphs showing frequency spectrum data generated by the second frequency processing unit 41d. When an abnormality occurs in the outer ring, as shown in FIG. 3, it can be seen that a periodic spectral peak due to damage to the outer ring is included in the signal component. On the other hand, when no abnormality has occurred, no special peak is found in any frequency band, as shown in FIG. The generated frequency spectrum data is output to various parameter calculation units 42.

The various parameter calculation unit 42 calculates the root mean square (Vi) or partial overall (Si) of one order component band of the frequency spectrum obtained by the second frequency processing unit 41d , and the root mean square of the entire frequency spectrum band ( V _RMS ) or overall value (S _OA ) is calculated, and the root mean square (Vi) or partial overall (Si) of the aforementioned one-order component band is calculated as the normalized value (V _RMS or S _OA ). A process of calculating a value divided by or a difference value is performed. The calculated divided value or difference value is sent to the collation unit 43.

Here, the root mean square (Vi), partial overall (Si), root mean square (V _RMS ) and overall (S _OA ) are given by the following equations.

  The collation unit 43 compares and collates the divided value or difference value sent from the various parameter calculation units 42 with the stored reference data, and whether the value sent from the parameter calculation unit 42 is within a normal range. Judge whether. Then, the collation unit 43 outputs the determination result to the diagnosis unit 44. The reference data is stored in a reference data holding unit 51 made of a recording medium such as a hard disk provided inside the PC unit 30 or a reference data holding unit 50 provided outside the PC unit 30. The reference data holding unit 50 provided outside may be connected to the PC unit 30 via a network such as the Internet or a LAN. The reference data stored in the collating unit 43 may be a predetermined constant, or may be a value calculated based on a normal value measured when no abnormality has occurred.

  The diagnosis unit 44 receives the determination result of the collation unit 44 and determines whether an abnormality has occurred according to the determination result. When the diagnosis unit 44 determines that an abnormality has occurred, the diagnosis unit 44 notifies the monitor 60 connected to the PC unit 30 that the abnormality has occurred, or issues a warning sound to notify the user of the abnormality. To do.

  Hereinafter, the above method will be applied to the data shown in FIGS. An abnormal peak frequency band exists in the vicinity of the left end (around 10 to 20 Hz) in FIG. The root mean square value Va of this whole spectrum is 0.016. On the other hand, the root mean square value Vn of the entire corresponding spectrum in FIG. 4 is 0.008. Here, assuming that the frequency bandwidth to be extracted for the abnormal frequency band (fundamental frequency) caused by the outer ring scar is 2 Hz, the value obtained by normalizing the mean square value in that band by V is 90.78 in the case of FIG. In the case of FIG. 4, it becomes 38.47. When there is an abnormality, it can be seen that the normalized value is about 2.4 times larger than the normal value. Therefore, a predetermined threshold value is provided between 90.78 and 38.47, or the ratio between normal and abnormal conditions, and when it is larger than the threshold value, it can be determined that an abnormality has occurred in the outer ring.

  When the frequency component band caused by the inner ring flaw of the roller bearing is calculated, the normalized value at the time of abnormality is about 0.3 times that at normal time. Further, when the frequency component band caused by the rolling elements and the cage of the roller bearing and the gears is calculated, it is equal to or smaller than the normal value. From this, it can be confirmed that the presence / absence and part of each component (the outer ring in this example) can be specified. In addition, here, the comparison is performed using the root mean square, but the presence or absence and the part of each component can be specified by performing the same comparison using the overall.

  In the above description, abnormality diagnosis is performed using one frequency component band. However, the present invention is not limited to this, and abnormality diagnosis can be performed using a plurality of frequency component bands. Specifically, the root mean square or partial overall of the multiple order component bands of the frequency spectrum and the normalized value that is the mean square or overall of the entire frequency spectrum band are calculated, and the mean square of the multiple order component bands Alternatively, it is achieved by calculating a value or difference value obtained by dividing partial overall by a normalized value, and comparing and comparing the divided value or difference value with a predetermined constant.

  5 and 6 show an example in which a plurality of bands are used. FIG. 5 is a graph showing an envelope frequency spectrum of a mechanical facility having a roller bearing having a damaged outer ring and a normal gear (number of teeth: 31). In this figure, five frequency peaks are observed, and from the fundamental frequency, the first harmonic to the fourth harmonic are observed every integer multiple thereof. On the other hand, FIG. 6 shows observation data corresponding to FIG. 5, and no singular frequency is found.

  Hereinafter, the above method will be applied to the data shown in FIGS. The value obtained by normalizing the sum of the root mean square values in each band of the fundamental frequency caused by the outer ring flaw and the harmonics up to the fifth order by the mean square value of the whole spectrum is 11.64 in the case of FIG. The case is 5.19. Here, the fifth high-order wave means the fifth peak counted from the fundamental frequency. When there is an abnormality, it can be seen that the normalized value is about 2.2 times larger than the normal value. Therefore, a predetermined threshold value is provided between 11.64 and 5.19, or the ratio between normal and abnormal, and when it is larger than the threshold, it can be determined that an abnormality has occurred in the outer ring.

  Similarly, when calculating the frequency component band caused by the inner ring, rolling element, cage, etc. of the roller bearing and the gear, it is equal to or smaller than the normal value, so each component (bearing in this example) is abnormal. The presence or absence and the part (in this example, the outer ring) can be specified. According to the present embodiment, it can be seen that the present invention is effective even when a plurality of order component bands are targeted. Here, the plurality of order component bands are preferably a combination of a fifth frequency band from a fundamental frequency band of a predetermined vibration.

  The plurality of order component bands are a first combination of a predetermined vibration fundamental frequency band, a second frequency band, and a third frequency band, a predetermined vibration fundamental frequency band, a second frequency band, And a second combination consisting of the fourth order frequency band, or a third combination consisting of the second order frequency band, the fourth order frequency band and the sixth order frequency band. Further, the normalized value may be a root mean square or overall of the entire frequency band from the fundamental frequency to the nth harmonic.

  Whether you use a single frequency band or multiple frequency bands, you can determine whether there is an abnormality by setting a reference value and comparing it with a reference value instead of comparing it with a normal value. It becomes possible to do. For example, in this embodiment, if the root mean square value 10 is set as a reference value, it can be determined that there is an abnormality when this value is exceeded. When setting the reference value, it may be set arbitrarily or may be fixed in advance.

  FIG. 7 shows the relationship between the frequency bandwidth and the value obtained by normalizing the root mean square of only the component band caused by the outer ring flaw in the frequency spectrum by using the spectrum data of FIG. 5 and FIG. . A solid line is a case where comparison calculation is performed using only the fundamental frequency, and a wavy line is a case where comparison calculation is performed using each band from the fundamental frequency to the fifth harmonic. From the figure, it can be seen that even if the frequency bandwidth is increased, the ratio of the abnormal value values is clearly distinguished as compared with the normal case. Therefore, even if the rotational speed used when calculating the fundamental frequency is slightly different from the actual rotational speed, it is possible to accurately perform abnormality diagnosis by taking a wide frequency bandwidth.

  As described above, according to this embodiment, the root mean square value or the overall is calculated, various calculations are performed, and an abnormality diagnosis is performed by comparing with a predetermined threshold value (reference value), and a good abnormality diagnosis is performed. It is possible. Further, in this embodiment, since the number of data is limited, the influence of the peak of the frequency component not caused by noise or a rotating body can be eliminated, the calculation load is reduced, and the time required for analysis is reduced. Is possible.

  In the present embodiment, a graph representing the current rotation speed or a temporal change in the rotation speed may be displayed on the monitor 60. Thereby, the user can check the abnormality of the rotational speed and determine that an abnormality has occurred in the mechanical equipment 1.

  The PC unit 30 may be configured to display an abnormality on the monitor 60 and to urgently stop the mechanical device having the mechanical equipment 1. In this case, even when the user is not in the vicinity of the mechanical device, it is possible to prevent further damage to the mechanical device.

  In the above embodiment, the rolling bearing is described as an example of the rotating body. However, the present invention is not limited to this, and can be applied to various mechanical devices having the rotating body. For example, the present invention can be applied to ball screws, linear guides, various motors, and the like.

It is a figure which shows the abnormality diagnosis apparatus of embodiment of this invention. It is a figure which shows the process in various analysis process parts. It is a graph which shows the envelope frequency spectrum of the roller bearing which has abnormality. It is a graph which shows the envelope frequency spectrum of a normal roller bearing. It is a graph which shows the envelope frequency spectrum of the roller bearing which has abnormality. It is a graph which shows the envelope frequency spectrum of a normal roller bearing. It is a graph which shows the relationship between the value which normalized the root mean square value of only the component zone | band resulting from the outer ring | flaw in a frequency spectrum by the root mean square of the whole frequency, and a frequency bandwidth.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Mechanical equipment 10 Abnormality diagnosis apparatus 11 Detection part 12 Amplification part 30 PC part 40 Diagnosis program 50,51 Reference data holding part 60 Monitor

Claims (4)

An abnormality diagnosis method for diagnosing an abnormality of a mechanical facility including one or a plurality of sliding members,
Detecting a wave generated from the mechanical equipment;
Calculating a frequency spectrum of the wave;
A first calculation step of calculating a root mean square or partial overall of one order component band of the frequency spectrum, and a normalization value that is a root mean square or overall of the entire band of the frequency spectrum;
A second calculation step of calculating a value obtained by dividing a root mean square or partial overall of the one order component band by the normalized value, or a difference value;
Performing a comparison of the divided value or difference value with a predetermined constant;
Based on the result of the comparison and verification, have a, a step of diagnosing an abnormality of the mechanical equipment,
Calculating the frequency spectrum of the wave,
Generating a frequency spectrum data by frequency-converting the detected wave using an FFT algorithm;
Based on the obtained frequency spectrum, performing a filtering process to cut out a frequency including an abnormal peak frequency;
Performing envelope processing on the signal of the cut-out frequency including the abnormal frequency by performing the filtering process;
Generating frequency spectrum data by frequency-converting the envelope-processed signal using an FFT algorithm;
An abnormality diagnosis method comprising: The first calculating step calculates a normalized value that is a mean square of one order component band of the frequency spectrum and a mean square of the entire band of the frequency spectrum;
2. The abnormality diagnosis method according to claim 1, wherein the second calculation step calculates a value obtained by dividing a root mean square of the one order component band by the normalized value or a difference value. An abnormality diagnosing device for diagnosing an abnormality of mechanical equipment including one or a plurality of sliding members,
A wave detection means for detecting a wave generated from the mechanical equipment;
A frequency analysis means for calculating a frequency spectrum of the wave;
Calculate a root mean square or partial overall of one order component band of the frequency spectrum and a normalization value that is a root mean square or overall of the entire band of the frequency spectrum, and calculate a root mean square or partial of the one order component band Parameter calculation means for calculating a value obtained by dividing overall by the normalized value or a difference value;
A comparison collation means for performing a comparison collation between the divided value or the difference value and a predetermined constant;
Based on the result of the comparison and verification, have a, a diagnostic means for diagnosing an abnormality of the mechanical equipment,
The frequency analysis means includes
A first frequency processing unit for generating a frequency spectrum data by frequency-converting the detected wave using an FFT algorithm;
Based on the obtained frequency spectrum, a filter unit that performs a filter process for cutting out a frequency including an abnormal peak frequency,
An envelope unit that performs envelope processing on a signal of a cut-out frequency that includes the abnormal frequency by performing the filtering process;
A second frequency processing unit for generating frequency spectrum data by performing frequency conversion on the signal subjected to the envelope processing by an FFT algorithm;
An abnormality diagnosis apparatus comprising: The parameter calculating means calculates a normalized value that is a mean square of one order component band of the frequency spectrum and a mean square of the entire band of the frequency spectrum, and normalizes the mean square of the one order component band 4. The abnormality diagnosis device according to claim 3 , wherein a value divided by the value or a difference value is calculated.

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