JP2007285875A - Anomaly diagnosis apparatus and anomaly diagnosis method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an anomaly diagnosis apparatus capable of specifying the presence or absence of anomalies and anomalous sections while securing diagnostic accuracy as much as possible by making the apparatus insusceptible to the effects of noise and to provide an anomaly diagnosis method. <P>SOLUTION: The apparatus for diagnosing anomalies of mechanical facilities 10 provided with a rotating part 12 compares and collates a frequency component of spectral data for diagnosis based on envelope and frequency analyses with a frequency component computed on the basis of rotational speed signals of the rotating part 12 and determines the presence or absence of anomalies and anomalous sections of the rotating part 12 on the basis of results of the collation. The spectral data for diagnosis is data acquired by raising envelope frequency spectral data acquired by the envelope and frequency analyses to the power of a value greater than 1. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to an abnormality diagnosis device and abnormality diagnosis method for rotating parts used in mechanical equipment such as a reduction gear, an electric motor, a windmill, a railway vehicle, etc. The present invention relates to an abnormality diagnosis device and an abnormality diagnosis method to be identified.

Various methods have already been proposed as an abnormality diagnosis method for a bearing portion of a rotating device such as a motor (see, for example, Patent Document 1). As the most general one, as described in Patent Document 1, an accelerometer is installed in the bearing portion, the vibration acceleration of the bearing portion is measured, and further, FFT (Fast Fourier Transform) processing is performed on this signal. There is known a method of performing diagnosis by extracting a signal of a vibration generating frequency component.
JP 2002-22617 A

  However, the above-described abnormality diagnosis method requires complicated processing such as wavelet analysis in order to analyze a vibration peak due to a minute abnormality.

  The present invention has been made in view of the above-described circumstances, and an object of the present invention is to provide an abnormality that can identify the presence or absence of a minute abnormality or an abnormal part while ensuring diagnosis accuracy as much as possible with relatively simple processing. An object of the present invention is to provide a diagnostic device and an abnormality diagnostic method.

The object of the present invention is achieved by the following constitution.
(1) Detecting spectral data based on envelope and frequency analysis by performing envelope analysis and frequency analysis on the waveform of the electrical signal, with a detector that outputs physical quantities generated as a result of vibration of rotating parts of machinery and equipment A signal processing unit that compares the frequency component and the frequency component caused by the rotating component, and determines the presence / absence and abnormal part of the component based on the comparison result,
The diagnostic spectrum data is an abnormality diagnosis device characterized in that envelope frequency spectrum data obtained by envelope and frequency analysis is data obtained by raising a power greater than 1 to an exponent.

(2) Detecting spectral data based on the envelope and frequency analysis by performing a envelope analysis and a frequency analysis on the waveform of the electrical signal, a detector that outputs the physical quantity generated due to the vibration of the rotating parts of the mechanical equipment as an electrical signal A signal processing unit that compares the frequency component and the frequency component caused by the rotating component, and determines the presence / absence and abnormal part of the component based on the comparison result,
The diagnostic spectrum data is data obtained by averaging a plurality of envelope frequency spectrum data obtained by performing envelope and frequency analysis a plurality of times.

(3) Detecting spectral data based on an envelope and frequency analysis by performing a envelope analysis and a frequency analysis on the waveform of the electrical signal, a detection unit that outputs a physical quantity generated as a result of vibration of rotating parts of mechanical equipment as an electrical signal A signal processing unit that compares the frequency component and the frequency component caused by the rotating component, and determines the presence / absence and abnormal part of the component based on the comparison result,
The diagnostic spectrum data is data calculated by multiplying a plurality of envelope frequency spectrum data obtained by performing envelope and frequency analysis a plurality of times.

(4) The abnormality diagnosis apparatus according to any one of (1) to (3), further including a microcomputer that performs processing by the signal processing unit and processing for outputting a determination result to the control unit.
(5) The abnormality diagnosis device according to any one of (1) to (4), wherein the mechanical equipment is a railway vehicle bearing device.
(6) The abnormality diagnosis device according to any one of (1) to (4), wherein the mechanical facility is a wind turbine bearing device.
(7) The abnormality diagnosis device according to any one of (1) to (4), wherein the machine facility is a machine tool spindle bearing device.

(8) The physical quantity generated due to the vibration of the rotating parts of the mechanical equipment is output as an electrical signal, the envelope analysis and frequency analysis are performed on the waveform of the electrical signal, and the frequency components of the diagnostic spectrum data based on the envelope and frequency analysis And an abnormal diagnosis method for comparing the frequency component caused by the rotating component and determining the presence / absence and abnormal part of the component based on the verification result,
The diagnostic spectrum data is an abnormality diagnosis method characterized in that envelope frequency spectrum data obtained by envelope and frequency analysis is data obtained by raising a power greater than 1 to an exponent.

(9) The physical quantity generated due to the vibration of the rotating parts of the mechanical equipment is output as an electrical signal, the envelope analysis and frequency analysis are performed on the waveform of the electrical signal, and the frequency components of the diagnostic spectrum data based on the envelope and frequency analysis And an abnormal diagnosis method for comparing the frequency component caused by the rotating component and determining the presence / absence and abnormal part of the component based on the verification result,
The diagnostic spectrum data is data obtained by averaging a plurality of envelope frequency spectrum data obtained by performing envelope and frequency analysis a plurality of times.

(10) The physical quantity generated due to the vibration of the rotating parts of the mechanical equipment is output as an electrical signal, the envelope analysis and frequency analysis are performed on the waveform of the electrical signal, and the frequency component of the diagnostic spectrum data based on the envelope and frequency analysis And an abnormal diagnosis method for comparing the frequency component caused by the rotating component and determining the presence / absence and abnormal part of the component based on the verification result,
The diagnostic spectrum data is data calculated by multiplying a plurality of envelope frequency spectrum data obtained by performing envelope and frequency analysis a plurality of times.

(11) The abnormality diagnosis method according to any one of (8) to (10), further including a microcomputer that performs processing by the signal processing unit and processing for outputting a determination result to the control unit.

  According to the abnormality diagnosis apparatus and the abnormality diagnosis method of the present invention, as the spectrum data for diagnosis, data obtained by raising the power of the envelope frequency spectrum data as an exponent to a value larger than 1; data obtained by averaging a plurality of envelope frequency spectrum data; By making any of the data calculated by multiplying the envelope frequency spectrum data, the peak level of the signal generated by a minute abnormality can be amplified more greatly than the noise level, and the diagnosis accuracy can be improved with a simple configuration. While ensuring as much as possible, it is possible to specify the presence or absence of abnormality and the site of abnormality.

  Hereinafter, an abnormality diagnosis device and an abnormality diagnosis method according to each embodiment of the present invention will be described in detail with reference to the drawings.

  First, as shown in FIG. 1, the abnormality diagnosis apparatus according to the first embodiment includes a detection unit 20 that detects a signal generated from the machine facility 10, an abnormality of the machine facility 10 from an electrical signal output from the detection unit 20, and the like. A controller 30 having a signal processing unit 32 for determining the state of the machine and a control unit 34 for driving and controlling the mechanical equipment 10, and an output device 40 such as a monitor and an alarm device.

  The mechanical equipment 10 is provided with a rolling bearing 12 that is a rotating part. The rolling bearing 12 includes an inner ring 14 that is a rotating ring fitted around a rotating shaft (not shown), and a housing (not shown). An outer ring 16 that is a fixed ring fitted into the inner ring 14, a ball 18 that is a plurality of rolling elements disposed between the inner ring 14 and the outer ring 16, and a cage (not shown) that holds the ball 18 in a freely rolling manner. ).

  The detection unit 20 includes an acceleration sensor 22 for detecting vibration generated from the rolling bearing 12 during operation. The acceleration sensor 22 is fixed in the vicinity of the outer ring of the housing by bolt fixing, bonding, bolt fixing and bonding, or embedding with a molding material. In addition, in the case of bolt fixation, you may make it provide a rotation stop function. Further, when the sensor 22 is molded, the waterproof property is achieved and the vibration proofing property against external vibration is improved, so that the reliability of the sensor 22 itself can be drastically improved.

  In addition, although the acceleration sensor 22 is shown as a sensor for detecting vibrations, other than that, an AE (acoustic emission) sensor, an ultrasonic sensor, a shock pulse sensor, a microphone, or the like, or speed, acceleration, distortion, stress Any material that can convert a physical quantity generated due to vibration of the rolling bearing 12 into an electrical signal, such as a displacement type, may be used. Further, when attaching to a mechanical device having a lot of noise, it is preferable to use an insulating type because it is less affected by noise. Further, when a vibration detecting element such as a piezoelectric element is used, this element may be molded into plastic or the like.

  The controller 30 including the signal processing unit 32 and the control unit 34 is configured by a microcomputer (IC chip, dedicated microchip, CPU, MPU, DSP, etc.), and the electric power from the sensor 22 is transmitted via the data transmission means 24. Receive a signal.

  As shown in FIG. 2, the signal processing unit 32 includes a data storage / distribution unit 50, a rotation analysis unit 52, a filter processing unit 54, a vibration analysis unit 56, a diagnostic spectrum calculation unit 58, a comparison determination unit 60, and an internal memory. 62. The data storage / distribution unit 50 receives and temporarily stores the electrical signal from the sensor 22 and the electrical signal related to the rotational speed, and collects and distributes the signal to one of the analysis units 52 and 56 according to the type of the signal. It has a function. Various signals are A / D converted into digital signals by an A / D converter (not shown) before being sent to the data storage / distribution unit 50, amplified by an amplifier (not shown), and then sent to the data storage / distribution unit 50. Note that the order of A / D conversion and amplification may be reversed, or one of them may be performed.

  The rotation analysis unit 52 calculates the rotation speed of the inner ring 14, that is, the rotation shaft, based on an output signal from a sensor (not shown) that detects the rotation speed, and transmits the calculated rotation speed to the comparison determination unit 60. . When the detection element is composed of an encoder attached to the inner ring 14 and a magnet and a magnetic detection element attached to the outer ring 16, the signal output from the detection element depends on the shape and rotational speed of the encoder. A corresponding pulse signal is obtained. The rotation analysis unit 52 has a predetermined conversion function or conversion table corresponding to the shape of the encoder, and calculates the rotation speed of the inner ring 14 and the rotation shaft from the pulse signal according to the function or table.

  Based on the natural frequency of the rolling bearing 12, which is a rotating component, the filter processing unit 54 extracts only a predetermined frequency band corresponding to the natural frequency from the vibration signal and removes unnecessary frequency bands. The natural frequency can be easily obtained by vibrating the rotating part as an object to be measured by the striking method and analyzing the frequency of the vibration detector attached to the object to be measured or the sound generated by the striking. When the object to be measured is a bearing, a natural frequency due to any of the inner ring, outer ring, rolling element, cage, etc. is given. In general, there are a plurality of natural frequencies of mechanical parts, and the amplitude level at the natural frequencies is high, so the sensitivity of measurement is good.

  The vibration analyzing unit 56 performs frequency analysis of vibration generated in the bearing 12 after performing absolute value processing and envelope processing based on the output signal from the sensor 22. Specifically, the vibration analysis unit 56 includes an FFT calculation unit that calculates a frequency spectrum of a vibration signal, and calculates a frequency spectrum of vibration based on an FFT algorithm. The calculated frequency spectrum is transmitted to the diagnostic spectrum calculation unit 58.

  The diagnostic spectrum calculation unit 58 calculates diagnostic spectrum data obtained by raising the envelope frequency spectrum data obtained by the envelope and frequency analysis by the vibration analysis unit 56 to a power that is larger than 1. A real number larger than 1 can be adopted as the value of the index, but it is preferably 2 or more from the viewpoint of improving diagnostic accuracy and 10 or less from the viewpoint of calculation time, and an integer of 2 to 5 is used. More desirable.

  The comparison determination unit 60 compares and collates the frequency component caused by the rolling bearing 12 with the frequency component of the vibration diagnostic spectrum data by the diagnostic spectrum calculation unit 58. In this embodiment, the comparison / determination unit 60 calculates a reference value (for example, sound pressure level or voltage level) from the diagnostic spectrum data, while using the relational expression shown in FIG. (Vibration generation frequency) is calculated, the sound pressure level (or voltage level) at the vibration generation frequency is extracted from the spectrum data for diagnosis, and compared with a reference value. Furthermore, the comparison / determination unit 60 identifies the presence / absence of an abnormality and an abnormal part based on the determination result.

  The calculation of the vibration generation frequency may be performed before this, and when the same diagnosis has been performed before, the data may be stored in the internal memory 62 and used. Further, design specification data of each rotating part used for calculation is input and stored in advance.

  Then, the determination result in the comparison / determination unit 60 may be stored in an internal memory 62 such as a memory or an HDD, or may be transmitted to the output device 40 via the data transmission unit 42. Further, the determination result may be output to the control unit 34 that controls the operation of the drive mechanism of the mechanical equipment 10 and a control signal corresponding to the determination result may be fed back.

  Further, the output device 40 may display the determination result on a monitor or the like in real time, or may notify the abnormality using an alarm device such as a light or a buzzer when an abnormality is detected. The data transmission means 24 and 42 only need to be able to transmit and receive signals accurately, may be wired, or may be wireless using a network.

  Next, a specific example of the abnormality diagnosis processing flow based on the vibration signal will be described with reference to FIG.

  First, the sensor 22 detects the vibration of each rotating component (step S101). The detected vibration signal is converted into a digital signal by an A / D converter (step S102) and amplified with a predetermined amplification factor (step S103), and then the noise component of the vibration signal is removed or filtered by the filter processing unit 54. Filter processing for extracting only a predetermined frequency band is performed (step S104). Thereafter, the vibration analysis unit 56 performs envelope processing on the digital signal after the filter processing (step S105), and obtains the frequency spectrum of the digital signal after the envelope processing (step S106). Further, the diagnostic spectrum calculation unit 58 calculates diagnostic spectrum data obtained by raising the envelope frequency spectrum data to a power that is a value larger than 1 (step S107). Note that amplification processing and filter processing are appropriately performed as necessary, and the digital signal obtained in step S102 may be directly envelope processed.

  Next, from the relational expression shown in FIG. 4, the vibration generation frequency generated due to the abnormality of each rotating component is obtained based on the rotation speed signal (step S108), and the abnormal frequency band of each rotating component is obtained with respect to the obtained frequency. Sound pressure levels (in the case of the rolling bearing 12, the bearing flaw component Sx, that is, the inner ring flaw component Si, the outer ring flaw component So, the rolling element flaw component Sb, and the cage component Sc) are obtained (step S109).

  On the other hand, a reference value (for example, sound pressure level or voltage level) used for abnormality diagnosis is calculated from the diagnostic spectrum data obtained by the diagnostic spectrum calculation unit (step S110). Here, this reference value may be calculated based on the effective value, crest factor (peak value / effective value), kurtosis, peak value, or these values of the digital signal of the spectrum curve at an arbitrary time. Good.

  Next, the comparison between the sound pressure level (or voltage level) of the abnormal frequency band of each rotating component calculated in step S109 and the reference value calculated in step S110 is divided in order for each rotating component having different design specifications. This is performed (step S111). If all the components do not match, it is determined that there is no abnormality in the rotating component (step S112). On the other hand, if any of the components match, it is determined that there is an abnormality and the abnormal part is specified (step S113), and the comparison result is sent to the control unit 34 or the output device 40 such as a monitor or alarm device. Output (step S114).

  As described above, in this embodiment, the diagnostic spectrum calculation unit 58 calculates the diagnostic spectrum data obtained by raising the envelope frequency spectrum data to a power that is a value larger than 1, so that the defect or abnormality of the rotating component is very small. Alternatively, even when the signal indicating abnormality is small, the peak level can be amplified more greatly than the noise level. As a result, the frequency component of the diagnostic spectrum data and the frequency component caused by the rotating component are compared and collated, and based on the collation result, the presence / absence and abnormality of the rotating component are determined. It is difficult to be affected by noise, and it is possible to specify the presence / absence of an abnormality and the site of the abnormality while ensuring the diagnostic accuracy as much as possible.

  Further, according to the abnormality diagnosis device and abnormality diagnosis method for the mechanical device of the present embodiment, since the signal processing unit is configured by a microcomputer, the signal processing unit is unitized, and the abnormality diagnosis device can be reduced in size and modules. Can be achieved.

  Next, the abnormality diagnosis apparatus and abnormality diagnosis method according to the second embodiment of the present invention will be described in detail with reference to the processing flow of FIG. Note that this embodiment is different from the first embodiment only in the processing of the diagnostic spectrum calculation unit, and therefore, the same parts as those in the first embodiment are denoted by the same reference numerals and the description thereof is omitted or simplified.

  In the abnormality diagnosis of the present embodiment, the vibration detection of each rotating component (step S101) is performed a plurality of times, the same processing as in steps S102 to S106 of the first embodiment is performed on each detection signal, and the obtained envelope frequency Each spectrum is stored in the internal memory 62. Then, the diagnostic spectrum calculation unit 58 averages the plurality of envelope frequency spectrum data stored in the internal memory 62 to calculate diagnostic spectrum data (step S107A). Thereafter, similarly to steps S108 to S114 of the first embodiment, the frequency component of the obtained diagnostic spectrum data is compared with the frequency component caused by the rotating component, and the presence or absence of abnormality of the rotating component is determined based on the comparison result. And determine the abnormal site.

Therefore, according to the abnormality diagnosis apparatus and abnormality diagnosis method of the present embodiment, the diagnostic spectrum data is obtained by averaging a plurality of envelope frequency spectrum data obtained by performing the envelope and frequency analysis a plurality of times. Similarly to the first embodiment, even when the defect or abnormality of the rotating component is minute and the signal indicating the defect or abnormality is small, the peak level can be amplified more greatly than the noise level. As a result, the frequency component of the diagnostic spectrum data and the frequency component caused by the rotating component are compared and collated, and based on the collation result, the presence / absence and abnormality of the rotating component are determined. It is difficult to be affected by noise, and it is possible to specify the presence / absence of an abnormality and the site of the abnormality while ensuring as much diagnostic accuracy as possible.
Other configurations and operations are the same as those in the first embodiment.

  Next, the abnormality diagnosis apparatus and abnormality diagnosis method according to the third embodiment of the present invention will be described in detail with reference to the processing flow of FIG. Note that this embodiment is different from the first embodiment only in the processing of the diagnostic spectrum calculation unit, and therefore, the same parts as those in the first embodiment are denoted by the same reference numerals and the description thereof is omitted or simplified.

  In the abnormality diagnosis of the present embodiment, the vibration detection of each rotating component (step S101) is performed a plurality of times, the same processing as in steps S102 to S106 of the first embodiment is performed on each detection signal, and the obtained envelope frequency Each spectrum is stored in the internal memory 62. Then, the diagnostic spectrum calculation unit 58 calculates diagnostic spectrum data by multiplying the plurality of envelope frequency spectrum data stored in the internal memory 62 (step S107B). Thereafter, similarly to steps S108 to S114 of the first embodiment, the frequency component of the obtained diagnostic spectrum data is compared with the frequency component caused by the rotating component, and the presence or absence of abnormality of the rotating component is determined based on the comparison result. And determine the abnormal site.

Therefore, according to the abnormality diagnosis apparatus and abnormality diagnosis method of the present embodiment, the diagnostic spectrum data is data calculated by multiplying a plurality of envelope frequency spectrum data obtained by performing envelope and frequency analysis a plurality of times. Thus, as in the first embodiment, even when the defect or abnormality of the rotating component is minute and the signal indicating the defect or abnormality is small, the peak level can be amplified more greatly than the noise level. As a result, the frequency component of the diagnostic spectrum data and the frequency component caused by the rotating component are compared and collated, and based on the collation result, the presence / absence and abnormality of the rotating component are determined. It is difficult to be affected by noise, and it is possible to specify the presence / absence of an abnormality and the site of the abnormality while ensuring as much diagnostic accuracy as possible.
Other configurations and operations are the same as those in the first embodiment.

  Next, with reference to FIG. 7, the abnormality diagnosis apparatus and abnormality diagnosis method according to the fourth embodiment of the present invention will be described in detail. In addition, about the part equivalent to 1st Embodiment, the same code | symbol is attached | subjected and description is abbreviate | omitted or simplified.

This embodiment is a single processing unit that combines a detection unit including a sensor 22 and a signal processing unit including a microcomputer 50 in an abnormality diagnosis device for a mechanical facility 70 including a plurality of rolling bearings 12 and 12. 80 is incorporated in the bearing device of the rolling bearing 12. As a result, the abnormality diagnosis apparatus can perform management in a centralized manner, so that efficient monitoring is possible. Further, it is preferable to incorporate a single processing unit in the bearing device because there is a merit that the entire device becomes compact. In addition, this single processing unit may be incorporated in mechanical equipment for compactness, or a single processing unit may be configured for a plurality of rolling bearings.
Other configurations and operations are the same as those in the first embodiment. The processing flow for abnormality diagnosis may be any of the first to third embodiments.

  In addition, this invention is not limited to the said embodiment, In the range which does not deviate from the summary of this invention, it can change suitably.

  The mechanical equipment of the present invention may be anything provided with a rotating part that is an abnormality diagnosis target, and includes a railway vehicle bearing device, a windmill bearing device, a machine tool main shaft bearing device, and the like. In particular, by applying the abnormality diagnosis device of the present invention to a railway vehicle bearing device, accurate abnormality diagnosis can be performed even when the speed changes or the vibration acceleration changes even when operating normally, such as on a curve. It can be performed. In addition, by applying the abnormality diagnosis device of the present invention to a wind turbine bearing device, an abnormality that ensures reliability even when used for a long period of time under conditions in which the wind direction, the strength of the wind, and the external temperature change. Diagnosis can be provided, and in addition, by applying the abnormality diagnosis device of the present invention to a machine tool spindle bearing device, accurate abnormality diagnosis can be provided under high-speed and high-precision rotation conditions.

  The rotating component may be a rolling bearing, a gear, a wheel, a ball screw, or the like, as long as it is a component that generates periodic vibrations due to damage.

  Hereinafter, specific examples using the abnormality diagnosis apparatus and abnormality diagnosis method of the present invention will be described.

Fig. 8 shows the vibration of the housing when the inner ring of a double row tapered roller bearing (inner diameter 120mm, outer diameter 220mm, width 155mm) with a defect in the outer ring raceway surface is rotated at 684min- ¹ and enveloped. The frequency analysis is performed later, and the diagnostic spectrum data calculated using the method of the first embodiment (that is, the envelope frequency spectrum multiplied by the fourth power) is displayed.
FIG. 9 shows diagnostic spectrum data calculated using the method of the second embodiment (that is, averaging four envelope frequency spectra), and FIG. 10 shows the method of the third embodiment ( That is, the spectrum data for diagnosis calculated by multiplying four envelope frequency spectra) is displayed, and FIG. 11 shows the envelope frequency spectrum after frequency analysis as it is without using this method. It is. Here, the solid line indicates the envelope frequency spectrum based on the actually measured vibration data, the dotted line indicates the reference value (three times the effective value), and the alternate long and short dash line indicates the frequency component due to the outer ring damage based on the rotational speed 684 min ⁻¹ .

  In FIG. 11, since the noise component is large, the SN ratio with the peak of the generated frequency of the bearing is poor, and there is almost no peak exceeding the reference value, and it is difficult to collate with the frequency component caused by the bearing. ing. In other words, in this case, the primary components coincide with each other, but it is difficult to distinguish whether the component is a noise component or a component caused by the bearing, which affects the diagnostic accuracy. On the other hand, in the diagnostic spectrum data shown in FIGS. 8 to 10, it is clearly understood that the peak exceeding the reference value matches the frequency component caused by the outer ring damage, and the bearing outer ring is damaged with high accuracy. can do.

It is the schematic of the abnormality diagnosis apparatus which is 1st Embodiment of this invention. It is a block diagram of the signal processing part of FIG. It is a flowchart which shows the abnormality diagnosis method which is 1st Embodiment of this invention. It is a figure which shows the relationship between the site | part of the damage | wound of a bearing, and the vibration generation frequency which originates in a damage | wound. It is a flowchart which shows the abnormality diagnosis method which is 2nd Embodiment of this invention. It is a flowchart which shows the abnormality diagnosis method which is 3rd Embodiment of this invention. It is the schematic of the abnormality diagnosis apparatus which is 4th Embodiment of this invention. It is a graph of the spectrum curve for diagnosis for demonstrating the abnormality diagnosis of 1st Embodiment of this invention. It is a graph of the spectrum curve for diagnosis for demonstrating the abnormality diagnosis of 2nd Embodiment of this invention. It is a graph of the spectrum curve for diagnosis for demonstrating the abnormality diagnosis of 3rd Embodiment of this invention. It is a graph of the actual measurement spectrum curve for demonstrating the conventional abnormality diagnosis.

Explanation of symbols

10 Mechanical equipment 12 Rolling bearings (Rotating parts)
20 detection unit 22 sensor 30 controller 32 signal processing unit 34 control unit 40 output device 50 data accumulation / distribution unit 52 rotation analysis unit 54 filter processing unit 56 vibration analysis unit 58 diagnostic spectrum calculation unit 60 comparison determination unit 62 internal memory

Claims (11)

A detection unit that outputs a physical quantity generated due to vibration of a rotating part of a mechanical facility as an electric signal, and performs envelope analysis and frequency analysis on the waveform of the electric signal, and performs diagnostic spectrum data based on the envelope and frequency analysis. Comparing the frequency component with the frequency component caused by the rotating component, and a signal processing unit that determines the presence / absence and abnormal part of the component based on the comparison result, an abnormality diagnosis device comprising:
The abnormality diagnosis apparatus, wherein the diagnostic spectrum data is data obtained by raising the value of the envelope frequency spectrum data obtained by the envelope and frequency analysis to a power that is a value greater than 1. A detection unit that outputs a physical quantity generated due to vibration of a rotating part of a mechanical facility as an electric signal, and performs envelope analysis and frequency analysis on the waveform of the electric signal, and performs diagnostic spectrum data based on the envelope and frequency analysis. Comparing the frequency component with the frequency component caused by the rotating component, and a signal processing unit that determines the presence / absence and abnormal part of the component based on the comparison result, an abnormality diagnosis device comprising:
The diagnostic spectrum data is data obtained by averaging a plurality of envelope frequency spectrum data obtained by performing the envelope and frequency analysis a plurality of times. A detection unit that outputs a physical quantity generated due to vibration of a rotating part of a mechanical facility as an electric signal, and performs envelope analysis and frequency analysis on the waveform of the electric signal, and performs diagnostic spectrum data based on the envelope and frequency analysis. Comparing the frequency component with the frequency component caused by the rotating component, and a signal processing unit that determines the presence / absence and abnormal part of the component based on the comparison result, an abnormality diagnosis device comprising:
The abnormality diagnosis apparatus, wherein the diagnostic spectrum data is data calculated by multiplying a plurality of envelope frequency spectrum data obtained by performing the envelope and frequency analysis a plurality of times.   The abnormality diagnosis apparatus according to claim 1, further comprising a microcomputer that performs processing by the signal processing unit and processing for outputting the determination result to a control unit.   The abnormality diagnosis device according to claim 1, wherein the mechanical equipment is a railcar bearing device.   The abnormality diagnosis device according to claim 1, wherein the mechanical facility is a wind turbine bearing device.   The abnormality diagnosis device according to claim 1, wherein the machine facility is a machine tool spindle bearing device. A physical quantity generated due to vibration of rotating parts of mechanical equipment is output as an electric signal, envelope analysis and frequency analysis are performed on the waveform of the electric signal, and frequency components of diagnostic spectrum data based on the envelope and frequency analysis , An abnormality diagnosis method for comparing and collating the frequency component caused by the rotating component, and determining the presence / absence and abnormal part of the component based on the collation result,
The abnormality diagnosis method, wherein the spectrum data for diagnosis is data obtained by raising the value of the envelope frequency spectrum data obtained by the envelope and frequency analysis to a power of a value larger than 1. A physical quantity generated due to vibration of rotating parts of mechanical equipment is output as an electric signal, envelope analysis and frequency analysis are performed on the waveform of the electric signal, and frequency components of diagnostic spectrum data based on the envelope and frequency analysis , An abnormality diagnosis method for comparing and collating the frequency component caused by the rotating component, and determining the presence / absence and abnormal part of the component based on the collation result,
The diagnostic spectrum data is data obtained by averaging a plurality of envelope frequency spectrum data obtained by performing the envelope and frequency analysis a plurality of times. A physical quantity generated due to vibration of rotating parts of mechanical equipment is output as an electric signal, envelope analysis and frequency analysis are performed on the waveform of the electric signal, and frequency components of diagnostic spectrum data based on the envelope and frequency analysis , An abnormality diagnosis method for comparing and collating the frequency component caused by the rotating component, and determining the presence / absence and abnormal part of the component based on the collation result,
The abnormality diagnosis method, wherein the diagnostic spectrum data is data calculated by multiplying a plurality of envelope frequency spectrum data obtained by performing the envelope and frequency analysis a plurality of times.   The abnormality diagnosis method according to claim 8, further comprising a microcomputer that performs processing by the signal processing unit and processing for outputting the determination result to a control unit.

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