JP2006234785A - Abnormality diagnosis device and abnormality diagnosis method for mechanical equipment - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an abnormality diagnosis device and an abnormality diagnosis method for mechanical equipment, specifying the presence/absence of abnormality and an abnormal region while securing the diagnosis accuracy even when the actual rotating speed cannot be directly taken. <P>SOLUTION: This abnormality diagnosis device for the mechanical equipment 10 including at least one rotating or sliding part 12 includes: at least one detecting part 20 for outputting a signal generated from the mechanical equipment 10 as an electric signal; and a signal processing part 32 for analyzing the frequency of a waveform of the electric signal, extracting the peak of the spectrum larger than a reference value calculated based upon the spectrum obtained by the frequency analysis, comparing and collating the frequency between the peaks with the frequency component derived from damage of the part calculated based upon the rotating speed signal to determined the presence/absence of abnormality of the part and the abnormal region based upon the collating result. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to an abnormality diagnosis apparatus and abnormality diagnosis method for rotating or sliding 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 apparatus and an abnormality diagnosis method for a mechanical facility that identifies the abnormal part.

  Conventionally, after rotating parts such as railway vehicles and wind turbines for power generation are used for a certain period, bearings and other rotating parts are regularly inspected for abnormalities such as damage and wear. This periodic inspection is carried out by disassembling the mechanical device in which the rotating parts are incorporated, and the person in charge finds damage and wear generated in the rotating parts by visual inspection. And the main defects found in the inspection are in the case of bearings, indentations caused by the biting of foreign matter, peeling due to rolling fatigue, other wear, etc., in the case of gears, missing teeth and wear, etc. In the case of a wheel, there is wear such as a flat, and in any case, if irregularities or wear that is not found in a new article is found, it is replaced with a new one.

  Various methods for diagnosing abnormalities of rotating parts in an actual operating state without disassembling a mechanical device in which the rotating parts are incorporated have been proposed (see, for example, Patent Documents 1 to 3). Most commonly, as described in Patent Document 1, an accelerometer is installed in the bearing section, the vibration acceleration of the bearing section is measured, and 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.

In addition, various methods have been proposed for detecting a flat portion called a flat caused by friction or wear of a wheel due to a brake malfunction or sliding on a rolling surface of a rolling wheel of a railway vehicle (for example, a patent) References 4-6.) Patent Document 4 proposes a device that detects a defect state of a railroad vehicle wheel and a track through which the train passes by a vibration sensor, a rotation measuring device, or the like.
JP 2002-22617 A JP 2003-202276 A Japanese Patent Laid-Open No. 2004-257836 Japanese National Patent Publication No. 9-500452 JP-A-4-148839 Special table 2003-535755 gazette

  However, in the method of disassembling the entire mechanical equipment and visually inspecting by the person in charge, the disassembling work for removing the rotating body and the sliding member from the apparatus, and the inspected rotating body and the sliding member being incorporated into the apparatus again. There has been a problem that a large amount of labor is required for the assembly work and the maintenance cost of the apparatus is greatly increased.

  Further, when reassembling, the inspection itself may cause a defect in the rotating body or the sliding member, such as attaching a dent to the rotating body or the sliding member that was not present before the inspection. Further, since a large number of bearings are visually inspected within a limited time, there is a problem that a possibility of overlooking a defect remains. Furthermore, the determination of the degree of the defect also varies depending on the individual, and parts are exchanged even if there is substantially no defect, resulting in a wasteful cost.

  On the other hand, in the abnormality diagnosis method described in Patent Document 1, the vibration generation frequency component is calculated based on the rotational speed. However, when the actual rotational speed cannot be directly captured, the rotational speed data used for the calculation is actually If there is a deviation from the rotational speed, there is a problem that the diagnostic accuracy deteriorates.

  Further, the defect state detection apparatus described in Patent Document 4 cannot identify whether the defect state that shows abnormal vibration in a railway vehicle is due to a flat wheel, an axle bearing, or due to a track or other abnormality. There is a problem.

The present invention has been made in view of the above-described circumstances, and its purpose is to identify the presence or absence of an abnormality and an abnormal portion while ensuring diagnostic accuracy even when the actual rotational speed cannot be directly captured. An object of the present invention is to provide an abnormality diagnosis apparatus and abnormality diagnosis method for mechanical equipment.
Another object of the present invention is to provide a machine equipment abnormality diagnosis device and abnormality that can accurately detect a state in which an abnormality of a part such as a wheel flat in a railway vehicle has occurred and identify the wheel. It is to provide a diagnostic method.

The object of the present invention is achieved by the following constitution.
(1) A machine facility abnormality diagnosis device comprising at least one component that rotates or slides,
At least one detector that outputs a signal generated from the mechanical equipment as an electrical signal;
Perform frequency analysis of the waveform of the electrical signal, extract a peak of the spectrum that is larger than a reference value calculated based on the spectrum obtained by the frequency analysis, and based on the frequency between the peaks and the rotational speed signal or the moving speed signal A signal processing unit that compares and compares the calculated frequency component resulting from the damage of the component, and determines the presence / absence and abnormal part of the component based on the comparison result;
An apparatus for diagnosing abnormalities in mechanical equipment, comprising:
(2) The signal processing unit performs at least one of amplification processing and filtering processing on the detected signal, and performs envelope processing on the processed waveform. Abnormality diagnosis device.
(3) An abnormality diagnosis apparatus for mechanical equipment, comprising at least one component that rotates or slides,
At least one detector that outputs a signal generated from the mechanical equipment as an electrical signal;
A signal processing unit for determining the presence / absence and abnormality of the component based on the frequency of the shock wave in which the waveform per unit time of the electrical signal exceeds a threshold, and the rotation speed signal or the movement speed signal;
An apparatus for diagnosing abnormalities in mechanical equipment, comprising:
(4) The signal processing unit filters the waveform of the electrical signal and converts the waveform into a full-wave rectified waveform every time the threshold is exceeded, a predetermined time corresponding to the rotation speed signal, 4. A waveform converted to be held at a value exceeding a threshold value is formed, and the possibility that an abnormality has occurred in the component is notified by the number of times that the waveform exceeds the threshold value per predetermined number of revolutions. An abnormality diagnosis device for mechanical equipment as described in 1.
(5) The signal processing unit may determine whether the waveform has been converted to hold the threshold value by exceeding the threshold value per predetermined number of rotations, and whether or not the component has malfunctioned by performing a plurality of statistics. 5. The abnormality diagnosis apparatus for machine equipment according to claim 4, wherein the judgment is performed by manual judgment.
(6) The machine equipment abnormality diagnosis apparatus according to any one of (1) to (5), wherein the signal processing unit is executed when a rotational speed of the component is substantially constant.
(7) The detection unit includes an integrated sensor in which a sensor for detecting vibration generated from the mechanical equipment and a sensor for detecting the temperature of the mechanical equipment are accommodated in a single casing. The machine equipment abnormality diagnosis device according to any one of (1) to (6),
(8) The mechanical equipment includes a bearing and a bearing box that fixes the bearing.
The apparatus for diagnosing abnormality of mechanical equipment according to (7), wherein the integrated sensor is fixed to a flat portion of the bearing box.
(9) The abnormality diagnosis apparatus according to any one of (1) to (8), further including data transmission means for transmitting a determination result by the signal processing unit.
(10) The abnormality diagnosis apparatus according to any one of (1) to (9), further including a microcomputer that performs processing by the signal processing unit and processing for outputting the determination result to a control system.
(11) The machine equipment abnormality diagnosis device according to any one of (1) to (10), wherein the mechanical equipment is a railway vehicle bearing device.
(12) The machine equipment abnormality diagnosis device according to any one of (1) to (10), wherein the machine equipment is a wind turbine bearing device.
(13) The machine equipment abnormality diagnosis device according to any one of (1) to (10), wherein the machine equipment is a machine tool spindle bearing device.
(14) A machine facility abnormality diagnosis method comprising at least one component that rotates or slides,
Detecting a signal generated from the mechanical equipment and outputting it as an electrical signal;
Analyzing the frequency of the waveform of the detected signal;
The peak of the spectrum that is larger than the reference value calculated based on the spectrum obtained in the analysis step is extracted, and the frequency component resulting from the damage of the component calculated based on the frequency between the peaks and the rotation speed signal or the movement speed signal A process of comparing and comparing
Determining the presence / absence and abnormality of the part based on the comparison result in the comparison step;
An abnormality diagnosis method for mechanical equipment, comprising:
(15) A machine equipment abnormality diagnosis method comprising at least one component that rotates or slides.
Detecting a signal generated from the mechanical equipment and outputting it as an electrical signal;
Detecting the presence / absence of an abnormality of the component based on the frequency of the shock wave in which the waveform per unit time of the electrical signal exceeds a threshold, and the rotation speed signal or the movement speed signal;
An abnormality diagnosis method for mechanical equipment, comprising:

  According to the abnormality diagnosis apparatus and abnormality diagnosis method for a mechanical device of the present invention, a peak of a spectrum that is larger than a reference value calculated based on a spectrum obtained by frequency analysis is extracted and calculated based on a frequency between the peaks and a rotation speed signal. When the actual rotation speed cannot be directly captured because the frequency components resulting from damage of the rotating or sliding parts are compared and checked, and the presence / absence and abnormal part of the parts are determined based on the comparison result. In addition, even if the rotational speed data used for the calculation is different from the actual rotational speed, the presence / absence of an abnormality and the specification of the abnormal part can be accurately performed.

  In addition, according to the abnormality diagnosis device and abnormality diagnosis method for a mechanical device of the present invention, the presence or absence of abnormality and the part of the abnormality can be determined without disassembling a mechanical device incorporating a rotating or sliding part with a simple configuration. Thus, it is possible to reduce the time and effort required for disassembling and assembling the apparatus, and it is possible to prevent damage to the parts due to disassembling and assembling.

  Further, according to the abnormality diagnosis device and abnormality diagnosis method of the mechanical device of the present invention, the frequency of the shock wave in which the waveform per unit time of the electrical signal output from the signal generated from the mechanical equipment exceeds the threshold, and the rotation speed signal Therefore, the presence / absence of the abnormality of the part and the abnormal part are determined. Therefore, it is possible to accurately detect the state in which the abnormality of the part such as the flat of the wheel in the railway vehicle is generated and to identify the wheel.

  Hereinafter, an abnormality diagnosis apparatus for machine equipment according to a first embodiment of the present invention will be described in detail with reference to the drawings.

(First embodiment)
First, with reference to FIGS. 1-4, the abnormality diagnosis apparatus of the mechanical installation of 1st Embodiment is demonstrated. As shown in FIG. 1, the abnormality diagnosis apparatus detects a signal generated from the machine facility 10 and determines a state such as an abnormality of the machine facility 10 from an electrical signal output from the detection unit 20. The controller 30 includes a signal processing unit 32 and a control unit 34 that drives and controls the machine facility 10, and an output device 40 such as a monitor and an alarm device.

  As an example, the mechanical equipment 10 is provided with a rolling bearing 12 which is a rotating part. The rolling bearing 12 includes an inner ring 14 which is a rotating ring fitted on a rotating shaft (not shown), and a housing (see FIG. (Not shown) an outer ring 16 which is a fixed ring fitted inside, a ball 18 which is a plurality of rolling elements disposed between the inner ring 14 and the outer ring 16, and a cage (a ball 18 which can freely roll). (Not shown).

  The detection unit 20 includes a sensor 22 that detects vibration generated from the mechanical equipment 10 during operation. The sensor 22 is fixed near 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.

  The sensor 22 may be any sensor that can detect vibration, such as a vibration sensor, an AE (acoustic emission) sensor, an ultrasonic sensor, a shock pulse sensor, and the like, acceleration, speed, strain, stress, displacement type, etc. Any device that can convert the vibration into an electrical signal 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 the sensor 22 uses a vibration detection element such as a piezoelectric element, the element may be molded into plastic or the like. In addition, the mechanical equipment 10 of the present embodiment can detect vibrations of gears and wheels (both not shown) in addition to the rolling bearing 12 by the sensor 22.

  Further, the detection unit 20 may be an integrated sensor in which a sensor 22 that detects vibration generated from mechanical equipment, and a temperature sensor and a rotational speed sensor that detect the temperature of the mechanical equipment are housed in a single casing. Good. In this case, the integrated sensor is preferably fixed to a flat portion of a bearing box that fixes the rolling bearing 12 (see FIG. 7). The temperature sensor may be a temperature fuse of a type in which, when the temperature reaches a certain specified value, the bimetal contact is separated or the contact is blown to cause no conduction. As a result, when a temperature equal to or higher than a specified value is detected, the temperature fuse is not turned on, so that an abnormality can be detected.

  The controller 30 including the signal processing unit 32 and the control unit 34 is configured by a microcomputer (IC chip, CPU, MPU, DSP, etc.), and receives an electrical signal from the sensor 22 via the data transmission means 24. .

  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 comparison determination unit 58, and an internal data storage unit 60. 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 AD 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.

  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 58. . 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 filter processing unit 54 extracts only a predetermined frequency band corresponding to the natural frequency from the vibration signal based on the natural frequency of the rolling bearing 12, which is a rotating component, a gear, a wheel, or the like, and extracts an unnecessary frequency band. Remove. This 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 rolling bearing, the 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 analysis unit 56 performs frequency analysis of vibration generated in the bearing 12, the gears, and the wheels based on the output signal from the sensor 22. Specifically, the vibration analysis unit 56 is 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 comparison determination unit 58. Further, the vibration analysis unit 56 may perform absolute value processing and envelope processing as preprocessing for performing FFT, and convert only to frequency components necessary for diagnosis. The vibration analysis unit 56 also outputs the envelope data after the envelope processing to the comparison determination unit 58 as necessary.

  The comparison determination unit 58 compares the frequency spectrum of vibration by the vibration analysis unit 56 with a reference value used for abnormality diagnosis calculated from the frequency spectrum, and extracts a peak component larger than the reference value from the frequency spectrum, Calculate the frequency value between peaks. On the other hand, from the relational expressions shown in FIG. 4 and FIG. , The rolling element scratch component Sb and the cage component Sc), the scratch component Sg corresponding to the meshing of the gears, the wear or unbalance component Sr of the rotating body such as the wheel, and the frequency value between the vibration generating frequency component and the peak is obtained. Compare. Furthermore, the comparison / determination unit 58 specifies the presence / absence of an abnormality and an abnormal part based on the determination result.

  In addition, the calculation of the vibration generation frequency component may be performed before this, and when the same diagnosis has been performed before, the data may be stored in the internal data storage unit 60 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 58 may be stored in the internal data storage unit 60 such as a memory or 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 31 detects the vibration of each rotating component (step S101). The detected vibration signal is converted into a digital signal by the A / D converter (step S102), amplified by a predetermined amplification factor (step S103), and then corresponding to the natural frequency of the rotating component by the filter processing unit 54. Filter processing for extracting only the 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 expressions shown in FIG. 4 and FIG. 5, the frequency components (bearing flaw component Sx (inner ring flaw component Si, outer ring flaw component So, The moving body scratch component Sb and the cage component Sc), the scratch component Sg corresponding to the meshing of the gears, the wear and unbalance component Sr) of the rotating body such as the wheel are obtained (step S107).

  On the other hand, a reference value (for example, sound pressure level or voltage level) used for abnormality diagnosis is calculated from the frequency spectrum obtained by the vibration analysis unit 56 (step S108). The reference value may be an effective value or peak value of a digital signal of actually measured spectrum data at an arbitrary time, or may be calculated based on these values.

  Next, a peak component larger than the reference value calculated in step S108 is extracted from the frequency spectrum obtained in step S106, and a frequency value between peaks is calculated (step S109). Then, the frequency value between the peaks and the vibration component frequency component of the rotating component in step S107 are compared (step S110). If 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 this embodiment, a peak of a spectrum that is larger than the reference value calculated based on the spectrum obtained by frequency analysis is extracted, and the frequency caused by damage to the rotating component calculated based on the frequency between the peaks and the rotation speed signal. Compared with the component, and the presence or absence and abnormal part of the rotating parts are identified based on the result of the comparison, so when the actual rotational speed cannot be directly captured, the rotational speed data used for the calculation is the actual rotational speed. Even if there is a deviation from the speed, the presence / absence of an abnormality and the identification of an abnormal part can be accurately performed.

  In addition, according to the abnormality diagnosis device and abnormality diagnosis method for a mechanical device of the present invention, it is possible to specify the presence or absence of abnormality and the location of the abnormality without disassembling a mechanical device incorporating a rotating component with a simple configuration. It is possible to reduce the time and effort required for disassembling and assembling the apparatus, and it is possible to prevent damage to the parts due to disassembly and assembling.

  Furthermore, according to the abnormality diagnosis device and abnormality diagnosis method for a mechanical device of the present embodiment, the signal processing unit is configured by a microcomputer, so that the signal processing unit is unitized, and the abnormality diagnosis device can be downsized and modules Can be achieved.

(Second Embodiment)
Next, with reference to FIG. 6, the abnormality diagnosis apparatus and abnormality diagnosis method for mechanical equipment 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.

(Third embodiment)
Next, with reference to FIGS. 8-10, the abnormality diagnosis apparatus and abnormality diagnosis method of a mechanical installation which concern on 3rd Embodiment of this invention are demonstrated in detail.

  As shown in FIG. 8, one rail vehicle 100 is supported by two front and rear chassis, and four wheels 104 are attached to each chassis. A vibration sensor 101 as a detection unit composed of a piezoelectric acceleration sensor or the like is attached to the bearing box of each wheel 104 and outputs vibration acceleration in a direction perpendicular to the ground.

  The output of the vibration sensor 101 is processed by an abnormality diagnosis module 102 which is a signal processing unit installed on the control panel of the vehicle 100. As shown in FIG. 9, the abnormality diagnosis module 102 includes a digital processing module 105 and performs abnormality diagnosis by digital processing. A vibration waveform detected by the vibration sensor 101 is converted into a discrete value by an AD converter (ADC) 108 via a low-pass filter (LPF) 107 and input to the CPU 111. Here, the frequency of vibration generated from the flat, which is an abnormality of the wheel 104, is concentrated in a frequency band lower than 1 kHz and spreads over a range higher than 1 kHz. The low-pass filter 107 attenuates a frequency of 1 kHz or more with a large noise component, and improves the S / N ratio.

  Further, the pulse signal detected by the rotation speed sensor 106 such as an encoder is pulse-shaped by the waveform shaping circuit 109, and the rotation speed signal is input to the CPU 111 by performing the pulse count by the timer counter (TCNT) 110, The CPU 111 performs abnormality diagnosis based on the vibration waveform and the rotation speed signal.

  The diagnosis result diagnosed by the CPU 111 is transmitted to the communication line 103 via the line driver 114 from a serial interface (SIO) 113 such as a USB, for example, based on the communication protocol IP112 constituting the transmission means. Therefore, in this embodiment, the AD converter 108, the timer / counter 110, the CPU 111, the communication protocol IP 112, the serial interface 113, and the line driver 114 constitute the digital processing module 105.

The CPU 111 has a waveform in which the sampling frequency fs and the sampling number Ns are constant when the rotational speed signal detected by the rotational speed sensor 106 is a substantially constant predetermined speed (185 to 370 min ^{−1 in} this embodiment). Block data is processed to detect the flatness of the wheel 104. Specifically, if fs = 2 kHz and Ns = 2000, the block data section length is 1 sec. The flat is detected by comparing the number of times that the vibration waveform pulse due to the flat is counted per second and the number of times the wheel 104 rotates per second from the vehicle speed detected by the rotational speed sensor 106.

  The vibration acceleration in the state where the flatness is generated in the wheel 104 is large, and the value of the vibration acceleration caused by the vibration of the normal vehicle is often smaller than that. Further, the vibration of the rail joint becomes a level of vibration acceleration equivalent to or larger than that of the flat. Furthermore, the level of vibration acceleration resulting from the friction between the rail and the wheel 104 in the rail curve is equivalent to that due to the flat or rail joint.

  On the other hand, a flat impact occurs once per rotation, whereas an impact caused by a rail joint occurs at a longer cycle, and an impact caused by rail friction occurs irregularly. Therefore, in this embodiment, paying attention to the regularity of shock (pulse) generation exceeding the threshold of vibration acceleration peculiar to flat, the number of shock waves per unit time at a substantially constant speed is counted, and the counted number is almost equal to that of the wheel. If it matches the rotational speed, an abnormality diagnosis is performed assuming that there is a high possibility that a flat has occurred.

  Further, in the present embodiment, an algorithm for repeatedly performing diagnosis processing on the same wheel 104 using the sensors 101 and 106 mounted on the vehicle 100 and the abnormality diagnosis module 102 is designed, and variations in the number of pulses and noise are detected. Improve the reliability of abnormality diagnosis using statistical judgment methods that take into account the impacts.

  A machine apparatus abnormality diagnosis method that performs such processing will be described in detail with reference to the flowchart of FIG.

First, a signal detected by the vibration sensor 101 is converted into a digital signal by the AD converter 108 (step S200), and a rotation speed signal is input from the rotation speed sensor 106. The abnormality diagnosis of the present embodiment is executed in a section where the rotational speed is running at a substantially constant speed between 185 and 370 min ⁻¹ , so the rotational speed in the section length of the data changes by 15% or more due to sudden acceleration / deceleration. It is determined whether or not (step S201). If it changes by 15% or more, the internal output “N” is output and abnormality diagnosis is not performed (step S202).

  On the other hand, if it is determined that the vehicle is traveling at a substantially constant speed, the digital signal converted by the AD converter 108 is converted into an absolute value to obtain a full-wave rectified waveform (step S203), and data exceeding the threshold is peak-held. The process holds the value exceeding the threshold for a certain time (τ) (step S204). This holding time (τ) is determined by the rotational speed of the wheel, and is shorter than one rotation of the wheel. This peak hold processing that converts the absolute value and holds it for a certain period of time enables stable peak measurement.

  Then, the number of times that the pulse has exceeded the threshold is counted as an event count process (step S205), and it is determined whether or not the count number matches the rotation speed of the wheel (step S206). If it is recognized that the count number matches the rotation speed of the wheel, it is determined that there is a flat and the internal output “F” (Flat) is output (step S207). If it does not match, it is determined that there is no flat. "G" (Good) is output to the outside (step S208). In this embodiment, since the rail joint may be affected, it is assumed that the count number of (wheel rotation speed + 1) also matches the wheel rotation speed.

For example, the rotational speed of the wheel is substantially constant 185 min ⁻¹ , that is, about 3 revolutions per second, and FIG. 11A shows a state where three shock waves are generated with a waveform of 1 second. In this abnormality diagnosis, the peak holding time τ is set to 30 ms, and once the absolute value of the shock wave exceeds the threshold value, the value exceeding the threshold value is held for 30 ms regardless of the original data. The same processing is repeated when 30 ms have passed since the time when the threshold value was first exceeded, and when the data reaches one second, the number of times the threshold value was exceeded is counted from the converted waveform (threshold holding waveform). The waveform in FIG. 11B is obtained by performing absolute value processing and peak hold processing on the waveform in FIG.

Furthermore, in the present embodiment, in order to obtain a highly reliable diagnosis result, for example, simple statistical determination based on one of the following conditions is performed using the output obtained once per second ( Step S209).
(1) “F” was output three times in succession.
(2) Out of the past 10 valid data, “F” was output 6 times or more.
In the case of corresponding to (1) and (2), it is determined that the wheel is surely flat, and finally "F" is output as an external output (step S210), (1), ( In cases other than 2), "G" is output as an external output (step S211).

  Note that “F” may be output even if the flat is not raised, but it is propagated to the normal wheel through the influence of noise such as the rail friction noise of the wheel, or from the flat wheel to the axle or rail. This is the case due to influence. In this case, since the frequency of outputting “F” is less than that of a flat wheel, accurate determination is possible by a plurality of statistical processes such as (1) and (2) above.

  In addition, when “F” is output as an external output in step S210, an abnormality signal is output from the serial interface 113 and the line driver 114 through the communication line 103, and a wheel flat or the like is output from an output device such as an alarm device. Alarm for abnormal occurrence.

  Therefore, according to the abnormality diagnosis apparatus and abnormality diagnosis method of the present embodiment, the wheel 104 is calculated based on the vibration acceleration waveform by the vibration sensor 101 attached to the bearing housing of the wheel 104 and the rotation speed signal of the wheel 104 by the rotation speed sensor 106. In the waveform of the vibration acceleration per unit time that is low-pass filtered during the time of N rotation, when the preset threshold value is exceeded, the waveform that maintains the state exceeding the threshold value for a certain time according to the rotation speed , It counts the number of times the threshold is exceeded, and recognizes that the number of counts coincides with the number of rotations of the wheel. It is possible to accurately identify a component abnormality.

  Further, in this embodiment, the abnormality diagnosis is performed based on the absolute wave full-wave rectified waveform without converting the flat waveform into the envelope detection waveform. it can.

  In this embodiment, the low-pass filter (LPF) 107 is inserted between the vibration sensor 101 and the AD converter 108. However, in the type in which the LPF is built in the sensor, the LPF 107 is simply an LC filter or the like. In addition, in the case of suppressing frequency components other than flat, a digital filter can be provided in the digital processing module 105. In that case, the digital filter can also be realized as CPU software.

(Fourth embodiment)
Next, the abnormality diagnosis apparatus and abnormality diagnosis method of the fourth embodiment of the present invention will be described in detail with reference to FIG. In the third embodiment, peak hold processing by bit processing is performed on the digital signal after A / D conversion processing, whereas in this embodiment, peak hold processing is performed at the analog signal stage before A / D conversion processing. . In addition, the same code | symbol is attached | subjected about the part equivalent to 3rd Embodiment, and description is abbreviate | omitted or simplified.

  The diagnostic module 120 of the fourth embodiment has a configuration in which an analog processing envelope circuit 115 is inserted between the vibration sensor 101 and the ADC 108 as shown in the block diagram of the diagnostic module of FIG. The envelope circuit 115 includes a low-pass filter, a full-wave rectifier 117 as an absolute value circuit, an analog peak hold circuit 118, and the like.

  Therefore, in this embodiment, the absolute value processing and peak hold processing in step S203 and step S204 are performed before A / D conversion (step S200), and the digital processing unit 119 performs steps S201 and S202 in the third embodiment. The same processing as S205 to S211 is performed, the number of times the threshold value is exceeded within a certain time is counted, and if it is a value corresponding to the rotational speed of the wheel, a warning signal is output as flat.

  In this embodiment, an analog circuit is separately required as compared with the third embodiment, but the processing after digitization is simplified, and the A / D conversion in the AD converter 108 including the peak hold circuit is performed. The sampling rate can also be lowered.

  The wheel flat has an impact waveform having a band up to about 1 kHz. Therefore, if the waveform is only passed through the low-pass filter 107 as in the third embodiment, the impact acceleration must be obtained unless a sampling rate of about 2 kHz is taken. However, if the peak hold circuit 118 is inserted in the analog circuit in the previous stage of the AD converter 108 as in the present embodiment, the detection of the wheel flat is possible even when sampling at about 200 Hz. Can be fast enough.

  The time constant (τ) of the peak hold circuit 118 in this case is also appropriately selected according to the vehicle speed range between several ms to several tens of ms. Note that it is desirable to insert a low-pass filter 107 before the AD converter 108 to cut noise from the waveform detected by the full-wave rectifier circuit 117.

  In the present embodiment, a high-pass filter (HPF) 116 is provided before the envelope circuit 115. The high-pass filter 116 is inserted to remove a DC component and a low-frequency component very close to it, and may be a simple AC coupling capacitor. The high-pass filter 116 can suppress ripples due to the DC component of the envelope waveform.

Further, in the waveform shown by the dotted line in FIG. 13, malfunction may occur in counting the number of times exceeding the threshold due to the influence of ripples. To avoid this, the threshold is increased to V _H and falling V _L. It is desirable to change the threshold height. In the present embodiment, as shown in FIG. 13, across the V _H at the rising edge, then Once across the V _L which is set lower than the V _H at the fall, if the first one count, dotted waveform as Can be counted accurately. Of course, such processing is equivalent even if counted by hardware.
Other configurations and operations are the same as those of the third embodiment.

(Fifth embodiment)
Next, the abnormality diagnosis apparatus and abnormality diagnosis method of the fifth embodiment of the present invention will be described in detail with reference to FIG. In the present embodiment, the envelope circuit in the fourth embodiment is replaced by digital processing. In addition, the same code | symbol is attached | subjected about a part equivalent to 4th Embodiment, and description is abbreviate | omitted or simplified.

  As shown in FIG. 14, in the diagnosis module 130 of the fifth embodiment, the digital processing unit 131 at the subsequent stage of the AD converter 108 is configured by a high-speed processor such as a DSP, and a low-frequency component is obtained by a digital high-pass filter (HPF) 135. And the amplitude is demodulated by an amplitude demodulator 134 that calculates the square root of the sum of squares from the complex signal of the real part and imaginary part by the Hilbert transform filter 133 of the envelope processing circuit 132 to obtain an envelope waveform, Further, the remaining noise is cut by the digital LPF 136, the number of times is counted by the threshold count 137, and the presence or absence of a flat is determined by the diagnosis unit 138.

The digital processing unit 131 of the present embodiment configured as described above can execute software that obtains an envelope waveform using a high-speed processor such as a DSP in real time without affecting the diagnosis time. is there. A waveform obtained by generating an envelope waveform by the envelope processing 132 and removing noise by the low-pass filter 136 with respect to the input waveform shown in FIG. 15A from which the low-frequency component is removed by the high-pass filter 135 in the previous stage is shown in FIG. It is a waveform of (b). The threshold value 137 and the diagnosis unit 138 perform determination processing such as flat on the waveform processed in this manner, as in the fourth embodiment. Specifically, it can be seen from the waveform shown in FIG. 15B that three shock waves are generated per second.
Other configurations and operations are the same as those in the fourth 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.
The mechanical equipment of the present invention may be anything provided with rotating or sliding parts that are objects of abnormality diagnosis, and includes a railway vehicle bearing device, a windmill bearing device, a machine tool main shaft bearing device, and the like.

  For example, as shown in FIG. 7, the railway vehicle bearing device 90 supports the axle shaft 90 rotatably with respect to a bearing box 92 constituting a part of the railway vehicle carriage via the double-row tapered roller bearing 12. In addition, the detection units 20 and 20 are fixed to the radial load area of the bearing housing 92 and the vibration of the bearing housing 92 is detected to perform abnormality diagnosis. If the bearing raceway surface is damaged, the collision force generated when the rolling element passes through the damaged portion is larger in the load zone than in the no-load zone, so the detection unit 20 is fixed on the load zone side. This makes it possible to detect vibration with high sensitivity.

  The rotating or sliding parts may be rotating parts such as rolling bearings, gears, axles and ball screws, and sliding parts such as linear guides and linear ball bearings. Any part may be used. In addition, a rotational speed signal is used as a speed signal for calculating a frequency component resulting from damage to the rotating part, but a moving speed signal is used as a speed signal in the case of a sliding part.

  Furthermore, the signals detected by the detection unit include sound, vibration, ultrasonic waves (AE), stress, distortion, etc., and these signals are used when there is a defect or abnormality in mechanical equipment including rotating or sliding parts. , Including a signal component indicating the defect or abnormality.

  Specific examples of abnormality diagnosis of rotating parts using the machine equipment abnormality diagnosis apparatus and method of the present invention will be described below.

Example 1
FIG. 16 shows the results of frequency analysis after envelope processing of housing vibration when a single-row deep groove bearing with a defect on the outer ring raceway surface is rotated at 1500 min ⁻¹ . In the figure, the solid line indicates the envelope frequency spectrum based on the actually measured vibration data, and the dotted line indicates the reference value.

  From the result of FIG. 16, there is a peak component exceeding the reference value in the frequency spectrum, and the frequency value between the peaks coincides with the frequency component (64.4 Hz) caused by the outer ring damage. It can be diagnosed that the outer ring of the bearing is damaged.

(Example 2)
FIG. 17 shows the result of frequency analysis after housing the vibration of the housing when a normal single row deep groove bearing is rotated at 1500 min ⁻¹ . As a result, it can be seen that there is no peak component exceeding the reference value in the frequency spectrum, and there is no abnormality in the bearing.

(Example 3)
FIG. 18 shows a result of frequency analysis after envelope processing of housing vibration when a single row deep groove bearing with a defect on the outer ring raceway surface actually rotates at 2430 min ⁻¹ . However, the rotational speed data used for the calculation is 2400 min ^−1, which is different from the actual rotational speed, and the alternate long and short dash line indicates the frequency component due to the outer ring damage based on the rotational speed 2400 min ⁻¹ .

  As can be seen from FIG. 18, if the difference between the actual rotational speed and the rotational speed used for diagnosis is large, a large shift occurs in the high and long wave components of the generated frequency, which affects the diagnostic accuracy. However, if the diagnostic apparatus and method of the present invention are applied, the presence / absence of abnormality and the location are identified using the frequency value between the peaks, so the influence of deviation from the actual rotational speed is reduced and the diagnosis is accurate. Can be seen.

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 processing flow of 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 figure for demonstrating the relational expression of the abnormal vibration frequency which generate | occur | produces by meshing | engagement of a gearwheel. It is the schematic of the abnormality diagnosis apparatus which is 2nd Embodiment of this invention. It is sectional drawing of the bearing apparatus for rail vehicles which is a mechanical installation with which the detection part of the abnormality diagnosis apparatus was integrated. It is a block diagram of the abnormality diagnosis apparatus which is 3rd Embodiment of this invention. It is a block diagram of the abnormality diagnosis module shown in FIG. It is a process flowchart of the abnormality diagnosis module shown in FIG. It is a figure for demonstrating the processing waveform of the abnormality diagnosis of 3rd Embodiment. It is a block diagram of the abnormality diagnosis module which is 4th Embodiment of this invention. It is explanatory drawing of malfunctioning of the abnormality diagnosis module shown in FIG. It is a block diagram of the abnormality diagnosis module which is 4th Embodiment of this invention. It is a figure which shows the processing waveform of the digital processing part shown in FIG. FIG. 6 is a diagram for explaining abnormality diagnosis according to the first embodiment. FIG. 6 is a diagram for explaining an abnormality diagnosis of Example 2. FIG. 6 is a diagram for explaining an abnormality diagnosis of Example 3.

Explanation of symbols

10,70 Machine 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 storage / distribution unit 52 rotation analysis unit 54 filter processing unit 56 vibration analysis unit 58 comparison determination unit 60 internal data storage unit 90 railcar bearing 92 Bearing box 100 Railway vehicle 101 Vibration sensor 102, 120, 130 Abnormality diagnosis module 103 Communication line 104 Wheel 105 Digital processing module 106 Rotational speed sensor 107, 136 LPF
108 ADC
109 Waveform shaping circuit 110 TCNT
111 CPU
112 Communication protocol IP
113 SIO
114 Line driver 115 Envelope circuit 116, 135 HPF
117 Full-wave rectifier circuit 118 Peak hold 119, 131 Digital processing unit 132 Envelope processing 133 Hilbert transform 134 Amplitude demodulation 137 Threshold count 138 Diagnosis unit

Claims (15)

An apparatus for diagnosing abnormality of mechanical equipment, comprising at least one component that rotates or slides,
At least one detector that outputs a signal generated from the mechanical equipment as an electrical signal;
Perform frequency analysis of the waveform of the electrical signal, extract a peak of the spectrum that is larger than a reference value calculated based on the spectrum obtained by the frequency analysis, and based on the frequency between the peaks and the rotational speed signal or the moving speed signal A signal processing unit that compares and compares the calculated frequency component resulting from the damage of the component, and determines the presence / absence and abnormal part of the component based on the comparison result;
An apparatus for diagnosing abnormalities in mechanical equipment, comprising:   2. The machine equipment abnormality diagnosis device according to claim 1, wherein the signal processing unit performs at least one of amplification processing and filtering processing on the detected signal, and performs envelope processing on the processed waveform. . An apparatus for diagnosing abnormality of mechanical equipment, comprising at least one component that rotates or slides,
At least one detector that outputs a signal generated from the mechanical equipment as an electrical signal;
A signal processing unit for determining the presence / absence and abnormality of the component based on the frequency of the shock wave in which the waveform per unit time of the electrical signal exceeds a threshold, and the rotation speed signal or the movement speed signal;
An apparatus for diagnosing abnormalities in mechanical equipment, comprising:   The signal processing unit filters the waveform of the electrical signal and converts the waveform into a full-wave rectified waveform each time the threshold is exceeded, the threshold is exceeded for a predetermined time according to the rotational speed signal. 4. The waveform converted to be held at a value is formed, and the possibility that an abnormality has occurred in the component is notified by the number of times that the waveform exceeds the threshold per predetermined number of revolutions. Machine equipment abnormality diagnosis device.   The signal processing unit can determine whether the waveform has been converted to hold the threshold value by exceeding the threshold value per predetermined number of rotations, and whether or not the component has malfunctioned by a plurality of statistical judgments. The machine equipment abnormality diagnosis apparatus according to claim 4, wherein the determination is performed.   The abnormality diagnosis device for a mechanical facility according to any one of claims 1 to 5, wherein the signal processing unit is executed when a rotation speed of the component is substantially constant.   The detection unit includes a sensor for detecting vibrations generated from the mechanical equipment and an integrated sensor in which a sensor for detecting the temperature of the mechanical equipment is accommodated in a single casing. The abnormality diagnosis apparatus for mechanical equipment according to any one of claims 1 to 6. The mechanical equipment includes a bearing and a bearing box that fixes the bearing.
8. The abnormality diagnosis apparatus for machine equipment according to claim 7, wherein the integrated sensor is fixed to a flat portion of the bearing box.   The abnormality diagnosis apparatus according to claim 1, further comprising a data transmission unit configured to transmit a determination result by the signal processing 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 system.   The machine equipment abnormality diagnosis device according to claim 1, wherein the machine equipment is a railcar bearing device.   The mechanical equipment abnormality diagnosis device according to claim 1, wherein the mechanical equipment is a wind turbine bearing device.   The machine equipment abnormality diagnosis apparatus according to claim 1, wherein the machine equipment is a machine tool spindle bearing device. A machine equipment abnormality diagnosis method comprising at least one component that rotates or slides,
Detecting a signal generated from the mechanical equipment and outputting it as an electrical signal;
Analyzing the frequency of the waveform of the detected signal;
The peak of the spectrum that is larger than the reference value calculated based on the spectrum obtained in the analysis step is extracted, and the frequency component resulting from damage of the component calculated based on the frequency between the peaks and the rotational speed signal or the moving speed signal A process of comparing and comparing
Determining the presence / absence and abnormality of the part based on the comparison result in the comparison step;
An abnormality diagnosis method for mechanical equipment, comprising: A machine equipment abnormality diagnosis method comprising at least one component that rotates or slides,
Detecting a signal generated from the mechanical equipment and outputting it as an electrical signal;
Detecting the presence / absence of abnormality of the component based on the frequency of the shock wave in which the waveform per unit time of the electrical signal exceeds a threshold, and the rotation speed signal or the movement speed signal;
An abnormality diagnosis method for mechanical equipment, comprising:

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