JP2008292288A - Bearing diagnostic device for reduction gear - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a bearing diagnostic device which quantitatively diagnoses abnormalities caused at each bearing with sufficient accuracy in reduction gear which consists of two or more bearings, like an elevator winding machine. <P>SOLUTION: The bearing diagnostic device comprises a filter processing section which extracts signal waveforms of a plurality of frequencies from diagnostic signal waveforms which shows the temporal variations of amplitude values representing an extent of vibration that is generated from a bearing to be diagnosed; an exponentiating processing section which exponentiates the amplitude values of the signal waveforms of a plurality of frequencies; a frequency analyzing section which applies Fourier transformation on exponentiated waveforms which shows temporal variations of the exponentiated amplitude values for finding frequency spectra, while it also calculates the amplitude values for every frequency which are resulting from the structure and revolution speed of the bearing; a criterion storing section which stores criteria values for every frequency that are resulting from the structure and revolution speed of the bearing; a comparison operation section which compares the amplitude values calculated in frequency analyzing section for every frequency with the criteria value stored in the criterion storing section; and a result display section which displays the result of decision, based on the result of comparison in the comparison operation section. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to a bearing diagnostic apparatus for quantitatively and accurately detecting an abnormality of a bearing used in a reduction gear such as an elevator hoisting machine.

  Conventionally, an abnormality judgment of a bearing used in a reduction gear such as an elevator hoisting machine is often judged by relying on a sensory evaluation of an on-site inspection operator. Judgment by sensory evaluation has the advantage that it can be easily performed without the need for a special measuring instrument or diagnostic device. However, the judgment varies depending on the operator, and it is necessary to gain experience to make an accurate judgment. For this reason, an inexperienced worker cannot make an accurate judgment, and the abnormality may be discovered after the abnormality of the bearing has progressed, and repairing with a short delivery time may be required. In addition, it is difficult to make a quantitative judgment because sensory evaluation is entrusted to the five officials of the worker, and even when periodic inspections are carried out, the difference from the previous inspection cannot be clearly determined. It was.

  In a conventional bearing diagnostic device, sounds and vibrations generated from the bearing to be diagnosed are detected by various sensors, only the necessary frequency band is extracted from the detected signal by various filters, and the extracted signal is envelope processed. Various devices are known that perform FFT processing to calculate a frequency spectrum and compare it with the periodicity obtained from the rotational speed and dimensions of the bearing.

  In an example of a conventional bearing diagnosis device, sound or vibration generated from a member to be diagnosed for abnormality is detected, and the magnitude of the fundamental frequency of the frequency resulting from the abnormality of the member to be diagnosed for abnormality and the magnitude of the frequency component that is a natural number multiple thereof. It comprises a judging means for comparing and using the comparison result as a judgment criterion for abnormality. This diagnostic apparatus has an advantage that a reference value can be set without using past measurement data, and diagnosis can be performed without complicating diagnostic processing (see, for example, Patent Document 1).

  In another example of a conventional bearing diagnosis apparatus, a sensor is fixed to a rotating component or a stationary member, and the identification corresponding to the natural frequency of any of the rotating component, the stationary member, and the sensor is performed from the waveform of the signal detected by the sensor. A filter unit that extracts the frequency band, an envelope processing unit that detects the absolute value of the filtered waveform, a frequency analysis unit that analyzes the frequency of the envelope-processed waveform, and a frequency resulting from damage to the rotating component to be diagnosed And a comparison / collation unit that compares the frequency based on the actual measurement data, and an abnormality determination unit that identifies the presence / absence of an abnormality and an abnormal part based on the comparison result. According to this diagnostic disconnection, it is possible to inspect the degree of defects and damage of multiple parts at the same time in actual operation without disassembling the device in which the rotating parts are incorporated, and also correspond to the natural frequency of the machine parts. Since the frequency band is extracted, there is an advantage that measurement with high sensitivity and high S / N ratio is possible (for example, refer to Patent Document 2).

JP 2003-232674 A JP 2005-62154 A

However, in the diagnostic device disclosed in Patent Document 1, in the diagnosis, a signal in a frequency band necessary for the diagnosis is extracted from the detected signal and processed, but there are a plurality of bearings such as an elevator hoist. In a reduction gear composed of gears and the like, since the vibration includes various frequencies, the determination of the frequency band necessary for the diagnosis cannot be uniquely determined because it differs depending on the configuration of the bearing or gear to be diagnosed.
In addition, since the diagnostic apparatus is advantageous in that it is possible to set a reference without using past measurement data, a frequency band necessary for diagnosis cannot be obtained based on the measurement data. For this reason, accuracy cannot be improved in the diagnosis at the initial stage of bearing abnormality.

  In the diagnostic device disclosed in Patent Document 2, a filter unit extracts a plurality of frequency bands corresponding to the natural frequency of a machine part. The natural frequency is a double row tapered roller bearing that is a rotating part. Any of the gears and wheels, the bearing box, which is a stationary member, and an abnormality detection sensor, is vibrated by the striking method as the object to be measured, and the frequency detector analyzes the sound generated by the vibration detector attached to the object to be measured. However, in the case of a reducer composed of multiple bearings that cannot be easily disassembled because the equipment is installed on site, such as an elevator hoisting machine, the impact method is applied to bearings and gears that are internal components. The method of obtaining the natural frequency by applying vibration is not time-consuming and time-consuming, and when the components are combined, the detected vibration frequency band is not necessarily the natural frequency of each component. Therefore, no proper frequency band is obtained.

  The object of the present invention is to eliminate variations in judgment criteria caused by sensory inspection for a reduction gear composed of a plurality of bearings such as an elevator hoisting machine, without relying on judgment based on the experience of the operator. An object of the present invention is to provide a bearing diagnostic apparatus that quantitatively and accurately diagnoses an abnormality occurring in each bearing.

  A bearing diagnostic apparatus according to the present invention is a bearing diagnostic apparatus for diagnosing a bearing abnormality, and includes a plurality of diagnostic target signal waveforms indicating changes on the time axis of amplitude values indicating the magnitude of vibration generated from a bearing to be diagnosed. A filter processing unit that extracts a signal waveform of a plurality of frequencies, a power processing unit that raises the amplitude values of the signal waveforms of the plurality of frequencies, and a Fourier transform to a power waveform that indicates changes on the time axis of the raised amplitude values To obtain the frequency spectrum, and to store the frequency analysis unit that calculates the amplitude value for each frequency attributed to the bearing structure and the rotational speed, and the judgment reference value for each frequency attributed to the bearing structure and the rotational speed Based on the comparison result in the comparison calculation unit, the comparison calculation unit that compares the amplitude value calculated in the frequency analysis unit for each frequency with the determination reference value stored in the determination reference storage unit. Z It includes a determination result display unit for displaying the determination result.

  The effect of the bearing diagnostic device according to the present invention is based on the experience of the operator by eliminating the variation in the judgment criteria caused by the sensory test for the reduction gear composed of a plurality of bearings such as an elevator hoisting machine. An abnormality occurring in each bearing can be diagnosed quantitatively and accurately without depending on the judgment.

Embodiment 1 FIG.
1 is a functional block diagram of a bearing diagnostic apparatus according to Embodiment 1 of the present invention.
A bearing diagnostic apparatus according to Embodiment 1 of the present invention will be described with reference to FIG. In this description, the elevator hoist 1 will be described as an example of the reduction gear to be diagnosed.
The bearing diagnosis apparatus according to the first embodiment of the present invention detects a vibration generated in the elevator hoist 1 and outputs an analog electric signal, and an amplification that amplifies the electric signal output from the vibration sensor 2 Unit 3, an A / D converter 4 that converts the amplified analog electrical signal into a digital electrical signal, wavelet transform is performed on the digital electrical signal, and a signal waveform of a plurality of frequencies is extracted. A wavelet transform processing unit 5 that performs mean-square processing of the real part and imaginary part, a power processing unit 6 that raises the amplitude values of a plurality of signal waveforms calculated by the wavelet transform processing unit 5, and an amplitude value output from the power processing unit 6 A frequency spectrum is obtained by performing Fourier transform on the signal waveform that has been subjected to the power process, and the specific frequency resulting from the structure and rotation speed of the bearing from the frequency spectrum Frequency analysis unit 7 for calculating the amplitude value of the bearing, determination criterion storage unit 8 for storing the determination reference value for each specific frequency resulting from the structure and rotation speed of the bearing, and the amplitude value and determination criterion storage calculated by the frequency analysis unit 7 The comparison operation unit 9 compares the determination reference value stored in the unit 8 to determine a bearing abnormality, and the determination result display unit 10 displays the determination result based on the comparison operation result in the comparison operation unit 9.

Next, the operation of the bearing diagnostic apparatus according to Embodiment 1 of the present invention will be described.
The vibration sensor 2 is fixed by a magnet attachment or the like at a position above the bearing of the elevator hoisting machine 1 constituted by a plurality of bearings.
The mechanical vibration is converted into an analog electric signal by the vibration sensor 2 and output, amplified by the amplifying unit 3, and converted into a digital electric signal by the A / D conversion unit 4. A diagnosis target vibration waveform is obtained in which the amplitude value of the digital electric signal indicates the magnitude of vibration. FIG. 2 shows examples of the diagnosis target vibration waveform (A) at the time of health and the diagnosis target vibration waveform (B) at the time of abnormality measured in this way. In the vibration waveform to be diagnosed in this state, no remarkable feature is observed when there is an abnormality compared to when it is healthy.

The wavelet transform processing unit 5 performs wavelet transform, which is a kind of time-frequency analysis, on the diagnosis target vibration waveform, and extracts a time axis waveform for each frequency band from the diagnosis target vibration waveform including various frequency components. The frequency band extracted here is the periodicity determined by the bearing structure and the number of rotations depending on the damaged part of the bearing in the actual data measured when an abnormality occurred in the bearing of the same type hoisting machine in advance. The frequency band that appears in. When there are a plurality of frequency bands in which the periodicity appears in the amplitude change, a plurality of time axis waveforms are extracted.
From the calculation result of each frequency band by wavelet transform, the real part and the imaginary part are squared and added to obtain a square root to obtain a time-axis waveform of each frequency band. About the diagnosis target vibration waveform (A) at the time of health and the diagnosis target vibration waveform (B) at the time of abnormality shown above, the time axis waveform in the specified frequency band extracted by the above procedure (when the sound time is healthy (A1), FIG. 3 shows an abnormal time (B1). From this result, it can be seen that a periodicity of 8.5 times / second appears in the time axis waveform at the time of abnormality, which coincides with the periodicity generated by the damaged portion of the bearing to be diagnosed. Conversely, a healthy bearing does not show this periodicity feature.

  Next, the time-axis waveform calculated by the wavelet transform processing unit 5 is subjected to a process of raising the amplitude value by the power-processing unit 6 to obtain a time-axis waveform obtained by performing the power process for each of a plurality of frequency bands. Compared to the time axis waveform (A1, B1) in the specific frequency band shown above, a time axis waveform obtained by squaring the amplitude value of the time axis waveform (when healthy (A2), when abnormal (B2)) FIG. 4 shows a time-axis waveform that is raised to the third power (when healthy (A3), when abnormal (B3)). Since the amplitude has a periodic characteristic at the time of abnormality and not at the time of soundness, it can be seen that the characteristic of the periodic amplitude that appears at the time of abnormality becomes more prominent by the power process of the amplitude value.

Next, the frequency analysis unit 7 individually performs a Fourier transform on the time axis waveform obtained by performing the power process on the amplitude value for each frequency band by the power processing unit 6 and outputs a frequency spectrum. From this frequency spectrum, an amplitude value at a frequency corresponding to the periodicity generated due to the structure of the bearing and the rotational speed is calculated.
The frequency spectrum (the healthy time is (A2 ′) and the abnormal time is (B2 ′)) of the time axis waveform (healthy time is (A2), abnormal time is (B2)) shown in FIG. FIG. 6 shows the frequency spectrum of the time-axis waveform (healthy condition is (A3), abnormal condition is (B3)) shown in FIG. 5 (healthy condition is (A3 ′), abnormal condition is (B3 ′). ) Is shown in FIG.
For reference, the frequency spectrum of the time axis waveform (when healthy (A1) and abnormal (B1)) (when healthy is (A1 ') when the power process is not performed on the waveform is abnormal. FIG. 8 shows the time (B1 ′).

  The frequency spectrum in which the power process shown in FIG. 8 was not performed (health is (A1 ′) and abnormal is (B1 ′)), and the frequency spectrum in which the square process shown in FIG. 6 is performed (health is (A2 ′)). The frequency generated by the damaged portion included in the abnormal data from the abnormal spectrum (B2 ′) and the frequency spectrum subjected to the cube process shown in FIG. 7 (healthy (A3 ′), abnormal (B3 ′)) Table 1 shows the result of calculating the amplitude at a frequency of 8.5 Hz corresponding to the sex.

  As a result, by performing the power process, the numerical value difference between the normal state and the abnormal state is increased, so that it is understood that the accuracy of the abnormality determination is increased. Since the feature that occurs at the time of abnormality becomes remarkable, even when the value of the amplitude that periodically appears in the initial stage of the abnormality is small, the feature of the abnormality can be detected prominently.

Next, an amplitude value at a frequency resulting from the structure and rotation speed of the bearing calculated from the frequency spectrum by the frequency analysis unit 7 is output to the comparison calculation unit 9.
In addition, the determination reference value for each specific frequency caused by the bearing structure and the rotation speed stored in advance in the determination reference storage section 8 for each bearing structure and rotation speed is output to the comparison calculation section 9.

The determination reference value stored in the determination reference storage unit 8 is calculated in consideration of variations based on actual vibration data measured in advance for a plurality of healthy hoisting machines. From the actual vibration data detected in advance for a plurality of healthy hoisting machines, the amplitude value obtained by the frequency analysis unit 7 at each periodicity determined from the bearing structure and the rotational speed is calculated.
Next, the average of the amplitude value and the standard deviation are calculated for the calculated amplitude value, and a value obtained by multiplying the average by multiplying the standard deviation by an integer is set as a determination reference value. The determination reference value can be arbitrarily set according to the state of the diagnosis target. However, in the case of the elevator hoist 1, accuracy can be obtained even if a value that is five times the standard deviation is set as an average.
Since the determination reference value is set in consideration of the variation of the same type of model, it is possible to accurately diagnose the bearing of the same type in the field where vibration is measured for the first time.

  Next, the comparison operation unit 9 compares the amplitude value (x) output from the frequency analysis unit 7 with the determination reference value (y) output from the determination reference storage unit 8. In the comparison operation, the determination reference value (y) and the amplitude value (x) are compared for each specific frequency resulting from the bearing structure and the rotational speed. At each specific frequency, when the amplitude value (x) is larger than the determination reference value (y), it is determined abnormal, and when the amplitude value (x) is smaller than the determination reference value (y), it is determined sound.

Next, the calculation result in the comparison calculation unit 9 is output to the determination result display unit 10. The output contents are a determination reference value (y), an amplitude value (x), and a determination result at each frequency. If there is even one abnormality determination result at each frequency, the determination result display unit 10 displays the abnormality. In addition, the determination result display unit 10 displays that it is healthy when it is determined that all are healthy.
Further, the quotient (z) obtained by dividing the amplitude value (x) of each frequency by the determination reference value (y) is displayed. Since each frequency depends on the bearing structure and the number of rotations, the quotient (z) can be used to identify the damaged part of the bearing that caused the anomaly. Can be grasped.

Embodiment 2. FIG.
In the bearing diagnostic device according to the first embodiment, the elevator hoist 1 is described as an example. However, the elevator operates in an irregular manner because the elevator operates according to a user's operation. In addition, the acceleration state is set when the operation is started, the operation is performed at a constant speed, the deceleration state is set when the operation is stopped, and the operating speed is irregular. When diagnosing bearings by vibration, data effective for diagnosis cannot be measured at the time of stopping, and the vibration state is not constant even during acceleration and deceleration, so vibration data at a constant speed at which the vibration state is stable is measured. There is a need to. When the operator performs vibration measurement manually, the operator must perform measurement while confirming the operating state of the elevator that operates irregularly, and the operation is not easy.

FIG. 9 is a functional block diagram of a bearing diagnostic apparatus according to Embodiment 2 of the present invention. FIG. 10 is a functional block diagram of an automatic measurement processing unit according to Embodiment 2 of the present invention.
The bearing diagnostic apparatus according to the second embodiment of the present invention is different from the bearing diagnostic apparatus according to the first embodiment in that an automatic measurement processing unit 11 is added. The description is omitted.
The automatic measurement processing unit 11 includes a trigger level detection unit 12 that starts measurement of vibration by vibration level detection, an effective value calculation unit 13 that calculates an effective value of the measured vibration waveform, and the effectiveness of the vibration waveform from the effective value calculation result. It has a valid data judgment unit 14 for judgment.

Next, the operation of the bearing diagnostic apparatus according to Embodiment 2 of the present invention will be described.
The vibration sensor 2 is fixed to a location above the bearing of the elevator hoisting machine 1 with a magnet attachment or the like. The vibration sensor 2 detects vibration and outputs it as an electrical signal. The amplification unit 3 amplifies the electrical signal, and the A / D conversion unit 4 converts the electrical signal that is an analog signal into a digital signal.
A vibration waveform having the amplitude of vibration as an amplitude value is obtained as a digital signal, and the vibration waveform is output to the automatic measurement processing unit 11. The vibration waveform is input to the trigger level detection unit 12 of the automatic measurement processing unit 11, and the trigger level detection unit 12 compares the amplitude value of the vibration waveform with a preset trigger level, and the amplitude value of the vibration waveform is greater than the trigger level. If it is larger, vibration waveform measurement starts.

  The trigger level is set based on the vibration level generated when the electromagnetic brake is released when the elevator starts. When the electromagnetic brake at the time of starting the elevator is released, a vibration larger than the vibration generated during the operation of the elevator is generated at the vibration sensor fixing portion on the hoisting machine. The level of vibration generated in the hoisting machine when the electromagnetic brake is released is grasped in advance. Since the trigger signal is not taken from outside such as a control panel and vibration generated in the hoisting machine main body is used, the trigger signal can be easily obtained without requiring modification of the control device.

The measurement of the vibration waveform is started by detecting the trigger level, the vibration waveform for a predetermined time set in advance is measured, and then the vibration waveform is output to the effective value calculation unit 13.
In the effective value calculation unit 13, after the brake is released, the effective value (a) of the amplitude in a predetermined time zone in which the elevator is at a constant speed, the effective value (b) of the amplitude in the immediately preceding time zone, and the immediately following time zone. The effective value (c) of the amplitude is calculated and output to the valid data determination unit 14.

  The valid data judgment unit 14 judges the validity of data based on two kinds of judgment criteria. The first criterion is the vibration generated at a constant speed by comparing the effective value (a) with the effective value of a predetermined amplitude at which the elevator becomes a constant speed calculated from sound vibration data measured in advance as a reference value. Make sure that is occurring. When the effective value (a) is smaller than the reference value, there is a high possibility that the elevator is not in a constant speed state and is stopped or decelerated. In this case, the trigger standby state is entered again.

When the effective value (a) is larger than the reference value, the effectiveness is determined according to the second determination criterion. The quotient (d) obtained by dividing the effective value (b) by the effective value (c) is used for the determination.
When the elevator is at a constant speed, the amplitude value of the vibration is the same with some fluctuations, so the quotient (d) is around 1. The quotient (d) is small in the acceleration state and large in the deceleration state. Therefore, for example, when the quotient (d) is larger than 0.9 and smaller than 1.1 in consideration of the variation of the amplitude value, it is determined as effective data for diagnosis. If it is determined to be invalid, there is a possibility of an acceleration state or a deceleration state, so the data is discarded and the trigger standby state is entered again.
As described above, the bearing diagnostic apparatus according to the second embodiment can greatly improve the workability at the time of measurement in the field.

  Of the parts constituting the bearing diagnostic apparatus according to the first and second embodiments described above, parts other than the vibration sensor 2, the amplifying unit 3, and the A / D converting unit 4 can be processed by a computer. If a portable computer including a notebook computer or the like is used, it can be carried by an inspection worker who visits the site, and a bearing abnormality can be immediately judged on site.

  Further, if a determination result transmission unit for transmitting the determination result is provided, a bearing diagnosis apparatus can be installed at the site, and the determination result can be transmitted to determine a bearing abnormality at a monitoring office or the like. It is possible to reduce the load on the inspection worker who visits the site and to quickly determine the abnormality even when an abnormality occurs between the site visit intervals.

It is a functional block diagram of the bearing diagnostic apparatus by Embodiment 1 which concerns on this invention. It is a diagnostic object vibration waveform which expressed the magnitude of vibration of an elevator hoist as an amplitude value. It is a waveform showing the change on the time-axis of the amplitude of the vibration of the specific frequency band extracted from the diagnostic object vibration waveform of FIG. It is the waveform which carried out the square process of the amplitude of the vibration of the specific frequency band of FIG. It is the waveform which carried out the cube process of the amplitude of the vibration of the specific frequency band of FIG. It is a frequency spectrum of the waveform of FIG. It is a frequency spectrum of the waveform of FIG. It is a frequency spectrum of the waveform of FIG. It is a functional block diagram of the bearing diagnostic apparatus by Embodiment 2 which concerns on this invention. It is a functional block diagram of the automatic measurement process part by Embodiment 2 which concerns on this invention.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 1 Elevator winding machine, 2 Vibration sensor, 3 Amplification part, 4 A / D conversion part, 5 Wavelet conversion process part, 6 Power process part, 7 Frequency analysis part, 8 Judgment reference | standard storage part, 9 Comparison calculation part, 10 determination Result display unit, 11 Automatic measurement processing unit, 12 Trigger level detection unit, 13 RMS value calculation unit, 14 Valid data determination unit.

Claims (7)

A bearing diagnostic device for diagnosing an abnormality in a reduction gear bearing,
A filter processing unit for extracting a signal waveform of a plurality of frequencies from a diagnosis target signal waveform indicating a change in amplitude on the time axis of an amplitude value indicating a magnitude of vibration generated from a bearing to be diagnosed;
A power processor that raises the amplitude values of the signal waveforms of the plurality of frequencies,
A frequency analysis unit that calculates a frequency spectrum by performing Fourier transform on a power waveform that represents a change in time of the raised amplitude value on the time axis, and calculates an amplitude value for each specific frequency caused by the structure and rotation speed of the bearing When,
A criterion storage unit for storing the criterion value for each specific frequency;
A comparison operation unit that compares the amplitude value calculated by the frequency analysis unit for each specific frequency with the determination reference value stored in the determination reference storage unit;
A determination result display unit for displaying a determination result based on the comparison result in the comparison operation unit;
A bearing diagnosis apparatus comprising:   The filter processing unit performs wavelet transformation on the diagnosis target signal waveform to extract signal waveforms in a plurality of frequency bands, and calculates a signal waveform by a mean square process of a real part and an imaginary part. The bearing diagnostic apparatus according to 1.   The determination reference value is a value obtained by adding a value obtained by multiplying a standard deviation of the detected amplitude value by a predetermined number to an average of amplitude values detected from a plurality of healthy same-type reduction gears. Item 6. The bearing diagnostic device according to Item 1.   The bearing diagnostic apparatus according to any one of claims 1 to 3, wherein vibration generated when the electromagnetic brake of the speed reducer is released is used as a trigger signal for starting measurement of vibration.   The bearing diagnosis apparatus according to claim 4, further comprising an effective data determination unit that determines a diagnosis target signal waveform effective for diagnosis from a state of the diagnosis target signal waveform obtained by measurement started according to the trigger signal. .   The filter processing unit, the power processing unit, the frequency analysis unit, the determination criterion storage unit, the comparison operation unit, and the determination result display unit are configured by a computer. The bearing diagnostic apparatus according to claim 5.   The bearing diagnosis apparatus according to claim 6, further comprising a determination result transmission unit that transmits a determination result by the comparison operation unit.

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