JP2007285874A - Anomaly diagnosis apparatus and anomaly diagnosis method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an anomaly diagnosis apparatus and an anomaly diagnosis method capable of specifying the presence or absence of very small anomalies and anomalous sections while securing diagnostic accuracy by relatively simple processings as much as possible. <P>SOLUTION: The anomaly diagnosis apparatus is provided with both a detection part for detecting physical quantities which occur due to vibrations of rotating parts of mechanical facilities and a signal processing part for determining the presence or absence of anomalies of the rotating parts by processing signals based on the physical quantities. Random spike noise is added to the signals based on the physical quantities. The signal processing part synchronously averages signals to which spike noise is added and determines anomalies by comparison with a preset reference value. <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) An abnormality diagnosis comprising: a detection unit that detects a physical quantity generated due to vibration of a rotating component of a mechanical facility; and a signal processing unit that processes a signal based on the physical quantity and determines whether there is an abnormality in the rotating component. A device,
Signals based on physical quantities are given random spike noise,
An abnormality diagnosis apparatus, wherein the signal processing unit performs an abnormality averaging by performing a synchronous addition average on a signal to which spike noise is added and comparing it with a preset reference value.

(2) An abnormality diagnosis comprising: a detection unit that detects a physical quantity generated due to vibration of a rotating part of mechanical equipment; and a signal processing unit that processes a signal based on the physical quantity and determines whether there is an abnormality in the rotating part. A device,
Signals based on physical quantities are given random spike noise,
The signal processing unit performs synchronous addition averaging, filter processing, envelope analysis, and frequency analysis on the signal to which spike noise is added, and calculates the frequency component of the measured spectrum data after frequency analysis and the frequency component due to the rotating component. An abnormality diagnosis device characterized by comparing and collating and determining the presence or absence and abnormality part of a part based on the collation result.

(3) The abnormality diagnosis device according to (1) or (2), wherein the spike noise is mechanically applied to the physical quantity detected by the detection unit by impulse excitation.
(4) The abnormality diagnosis device according to (1) or (2), wherein the spike noise is electrically added to a signal based on a physical quantity detected by the detection unit.
(5) The abnormality diagnosis apparatus according to any one of (1) to (4), further including a microcomputer that performs processing by the signal processing unit and processing for outputting a determination result to the control unit.

(6) The abnormality diagnosis device according to any one of (1) to (5), wherein the mechanical facility is a railway vehicle bearing device.
(7) The abnormality diagnosis device according to any one of (1) to (5), wherein the mechanical equipment is a windmill bearing device.
(8) The abnormality diagnosis device according to any one of (1) to (5), wherein the machine facility is a bearing device for a machine tool main shaft.

(9) An abnormality diagnosis method for detecting a physical quantity generated due to vibration of a rotating part of mechanical equipment and processing the signal based on the physical quantity to determine whether there is an abnormality in the rotating part,
Signals based on physical quantities are given random spike noise,
An abnormality diagnosing method, wherein abnormality is determined by performing synchronous addition averaging on a signal to which spike noise is added and comparing with a preset reference value.

(10) An abnormality diagnosis method for detecting a physical quantity generated due to vibration of a rotating part of mechanical equipment and processing the signal based on the physical quantity to determine whether or not the rotating part is abnormal.
Signals based on physical quantities are given random spike noise,
Synchronous averaging, filtering, envelope analysis, and frequency analysis are performed on signals with spike noise added, and the frequency components of the measured spectrum data after frequency analysis are compared with the frequency components caused by rotating parts. An abnormality diagnosis method characterized by determining the presence / absence of an abnormality of a part and an abnormal part based on a collation result.

  According to the abnormality diagnosis device and abnormality diagnosis method of the present invention, spike noise that is temporally random is added to a signal based on a physical quantity, and thus the spike noise can be combined with a signal based on the physical quantity. For this reason, even a small signal generated by a minute abnormality can be easily amplified. Thereby, although the noise level is also amplified, amplification of the noise level can be reduced by performing synchronous addition averaging on the signal to which spike noise is added. Therefore, by comparing the signal with reduced noise level amplification with a reference value set in advance, it is possible to perform an abnormality determination with a simple configuration and ensuring diagnostic accuracy as much as possible.

  Further, according to the abnormality diagnosis apparatus and abnormality diagnosis method of the present invention, as described above, an appropriate filter process is performed on the signal in which the amplification of the noise level is reduced by performing synchronous addition averaging, envelope analysis, Performs frequency analysis, compares the frequency component of the measured spectrum data after frequency analysis with the frequency component caused by the rotating component, and determines the presence or absence of an abnormality of the part and the abnormal part based on the result of the comparison. Thus, it is possible to specify the presence / absence of abnormality and the site of abnormality while ensuring the diagnostic accuracy as much as possible.

  Furthermore, since the spike noise generated by impulse excitation has a wide frequency range, the overall frequency level increases, but the tendency increases especially in the resonance range near the natural frequency. By extracting a frequency band in the vicinity of the natural frequency of the rotating part by filtering, a more accurate diagnosis is possible.

  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 mechanical facility 10 and an impulse that imparts random spike noise to the mechanical facility 10 in time. A controller 30 including a vibration unit 26, a signal processing unit 32 for determining a state of abnormality or the like of the mechanical equipment 10 from the electrical signal output from the detection unit 20, and a control unit 34 for driving and controlling the mechanical equipment 10. And an output device 40 such as a monitor or an alarm.

  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.

  Further, the impulse excitation unit 26 mechanically applies spike noise to the physical quantity by impulse excitation, and the signal detected by the detection unit 20 is a simple vibration generated from the rolling bearing 12 during operation. It will be amplified. In addition, as long as the mechanical equipment 10 can be directly vibrated as in the present embodiment, in consideration of signal amplification, in the process of transmission from the detection unit 20 to the signal processing unit 32, the gain is instantaneously increased. Electrical spike noise may be given.

  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 synchronous addition averaging processing unit 54, a filter processing unit 56, a vibration analysis 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 58 according to the type of the signal. It has a function. The 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 and sent to the data storage / distribution unit 50.

  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.

  The synchronous addition average processing unit 54 performs a synchronous addition average for calculating an addition average of a plurality of vibration time data, and reduces the amplified noise level together with a signal indicating a defect or abnormality.

  Based on the natural frequency of the rolling bearing 12, which is a rotating component, the filter processing unit 56 extracts only a predetermined frequency band in the vicinity of the natural frequency from the signal obtained by synchronous addition averaging, and removes unnecessary frequency bands. To do. 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.

  Since spike noise generated by impulse excitation has a wide frequency range, the overall frequency level increases, but the tendency increases especially in the resonance range near the natural frequency. By extracting the frequency band in the vicinity of the natural frequency of the rotating component by 56, more accurate diagnosis is possible.

  The vibration analysis unit 58 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 58 includes an FFT calculation unit that calculates a frequency spectrum of the vibration signal, and calculates a frequency spectrum of vibration based on an FFT algorithm. The calculated frequency spectrum is transmitted to the comparison determination unit 60.

  The comparison / determination unit 60 compares the frequency component caused by the rolling bearing 12 with the frequency component of the measured spectrum data by the vibration analysis unit 58. In the present embodiment, the comparison / determination unit 60 calculates a reference value (for example, sound pressure level or voltage level) from the actually measured spectrum data, while using a 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.

  Note that the order of the synchronous addition average processing unit 54 and the filter processing unit 56 may be reversed, and the synchronous addition average processing may be performed on frequency data instead of time data.

  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 in a state where random spike noise is applied by the impulse excitation unit 26 (step S101). The detected vibration signal is converted into a digital signal by the A / D converter (step S102), and after synchronous addition averaging processing is performed on the accumulated time data of the plurality of vibration signals (step S103), the filter processing unit 54 is performed. Thus, 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).

  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 S107), 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 S108).

  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 S109). 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 S108 and the reference value calculated in step S109 is divided in order for each rotating component having a different design specification. Perform (step S110). When all the components do not match, it is determined that there is no abnormality in the rotating component (step S111). 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 S112), and the result of the comparison is sent to the control unit 34 or the output device 40 such as a monitor or alarm device. Output (step S113).

  As described above, in the abnormality diagnosis device and abnormality diagnosis method for a mechanical device according to the present embodiment, a spike noise that is temporally random is added to a signal based on a physical quantity, and thus the spike noise is combined with a signal based on the physical quantity. Can be made. For this reason, even a small signal generated by a minute abnormality can be easily amplified. Thereby, although the noise level is also amplified, amplification of the noise level can be reduced by performing synchronous addition averaging on the signal to which spike noise is added.

  Therefore, the signal with reduced noise level amplification is subjected to appropriate filter processing, envelope analysis and frequency analysis are performed, and the frequency component of the measured spectrum data after frequency analysis and the frequency component due to the rotating component Are compared, and the presence / absence and abnormal part of the part are determined based on the result of the collation. Therefore, the presence / absence of the abnormality and the abnormal part can be specified with a simple configuration while ensuring the diagnostic accuracy as much as possible.

  Furthermore, since the spike noise generated by impulse excitation has a wide frequency range, the overall frequency level increases, but the tendency increases especially in the resonance range near the natural frequency. By extracting a frequency band in the vicinity of the natural frequency of the rotating part by filtering, a more accurate diagnosis is 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, with reference to FIG. 5, the abnormality diagnosis apparatus and abnormality diagnosis method according to the second 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.

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.
In the present embodiment, filtering processing, envelope analysis, frequency analysis, and comparison and collation are performed on a signal whose noise level amplification has been reduced by performing synchronous addition averaging to determine the presence / absence and abnormal part of rotating parts. However, as in the embodiment shown in FIG. 6 to be described later, the rotation of the signal with reduced noise level amplification (for example, time data) is compared with a preset reference value. An abnormality determination for a part may be performed.

  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. 6 shows random spike noise added to the vibration data when the inner ring of a double-row tapered roller bearing (inner diameter 120mm, outer diameter 220mm, width 155mm) with a defect on the outer ring raceway surface is rotating at 180min- ^1. FIG. 7 shows a time waveform with no spike noise added. In both cases, since the synchronous addition is averaged, it can be seen that the vibration peak amplification effect is greater than the noise effect due to random spike noise, and the peak exceeds the preset reference value (± 60% of full scale). I can confirm. It can also be confirmed that this time interval (cycle) matches the outer ring wound component.

8 and 9 show spectrum waveforms obtained by frequency analysis of these vibration time data after envelope processing. FIG. 8 uses the time data of FIG. 6 to which the present invention is applied, and FIG. The time data of FIG. 7 to which the method is not applied is used. Here, the solid line indicates the envelope frequency spectrum based on the actually measured vibration data, the dotted line indicates the reference value (twice the effective value), and the alternate long and short dash line indicates the frequency component resulting from the outer ring damage based on the rotational speed of 180 min ⁻¹ .

  As a result, when this method is applied, it can be clearly seen that the peak exceeding the reference value matches the frequency component and the harmonic component caused by damage to the outer ring. Can be diagnosed.

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 the schematic of the abnormality diagnosis method apparatus which is 2nd Embodiment of this invention. It is a time waveform to which random spike noise is added to which the present invention is applied. It is a time waveform without adding random spike noise. It is the spectrum waveform which frequency-analyzed the time waveform of FIG. 6 after envelope processing. It is the spectrum waveform which frequency-analyzed the time waveform of FIG. 7 after envelope processing.

Explanation of symbols

10 Mechanical equipment 12 Rolling bearings (Rotating parts)
DESCRIPTION OF SYMBOLS 20 Detection part 22 Sensor 26 Impulse excitation part 30 Controller 32 Signal processing part 34 Control part 40 Output device 50 Data storage distribution part 52 Rotation analysis part 54 Synchronous addition average process part 56 Filter process part 58 Vibration analysis part 60 Comparison determination part 62 Internal memory

Claims (10)

An abnormality diagnosing apparatus comprising: a detection unit that detects a physical quantity generated due to vibration of a rotating part of a mechanical facility; and a signal processing unit that processes a signal based on the physical quantity and determines whether or not the rotating part is abnormal. Because
Random spike noise is given to the signal based on the physical quantity,
The abnormality diagnosing apparatus according to claim 1, wherein the signal processing unit performs a synchronous addition average on the signal to which the spike noise is added and compares the signal with a reference value set in advance. An abnormality diagnosing apparatus comprising: a detection unit that detects a physical quantity generated due to vibration of a rotating part of a mechanical facility; and a signal processing unit that processes a signal based on the physical quantity and determines whether or not the rotating part is abnormal. Because
Random spike noise is given to the signal based on the physical quantity,
The signal processing unit performs synchronous addition averaging, filter processing, envelope analysis, and frequency analysis on the signal to which the spike noise is added, and is caused by the frequency component of the measured spectrum data after the frequency analysis and the rotating component. An abnormality diagnosis apparatus characterized by comparing and collating with frequency components, and determining the presence / absence and abnormality of the component based on the comparison result.   The abnormality diagnosis apparatus according to claim 1, wherein the spike noise is mechanically given to the physical quantity detected by the detection unit by impulse excitation.   The abnormality diagnosis apparatus according to claim 1, wherein the spike noise is electrically added to a signal based on the physical quantity detected by the detection unit.   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 facility 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 mechanical equipment is a machine tool spindle bearing device. An abnormality diagnosis method for detecting a physical quantity generated due to vibration of a rotating part of a mechanical facility and determining a presence or absence of an abnormality of the rotating part by processing a signal based on the physical quantity,
Random spike noise is given to the signal based on the physical quantity,
An abnormality diagnosing method, wherein abnormality is determined by performing synchronous addition averaging on a signal to which the spike noise is added and comparing with a preset reference value. An abnormality diagnosis method for detecting a physical quantity generated due to vibration of a rotating part of a mechanical facility and determining a presence or absence of an abnormality of the rotating part by processing a signal based on the physical quantity,
Random spike noise is given to the signal based on the physical quantity,
Synchronous averaging, filtering, envelope analysis, and frequency analysis are performed on the spiked noise signal, and the frequency components of the measured spectrum data after the frequency analysis are compared with the frequency components caused by the rotating parts. An abnormality diagnosis method characterized by determining the presence / absence of an abnormality of the component and an abnormal part based on the collation result.

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