JPS61114134A - Diagnosing method of rotary machine - Google Patents

Abstract

PURPOSE:To reduce noises and to calculate an accurate index of deterioration by measuring the oscillation of rotary equipment, expressing its measurement signal as an autoregressive model, and calculating a power spectrum, and then calculating the probability of deterioration from the ratio (deterioration index) of effective values of the intensity and power spectrum at a specific frequency. CONSTITUTION:The oscillation of the bearing 3 of a motor is detected by a vibration meter 8, whose output signal is inputted to an arithmetic processor 10 through an AD converter 9 to perform specific arithmetic. Namely, the detection signal is composed of a superposition signal of various vibrations and an autoregressive expression is set to calculate an optimized power spectrum by a method of interaction; and the ratio of effective values of the intensity and power spectrum at the specific frequency of the power is calculated as the index of deterioration to calculate the probability of deterioration from the index of deterioration. Consequently, vibrations (noise) except vibrations due to abnormality are removed to calculate the accurate index of deterioration.

Description

DETAILED DESCRIPTION OF THE INVENTION (Field of Industrial Application) The present invention relates to a method for diagnosing an abnormal state in a rotating machine such as a reducer 5 motor, a blower, or a rotating machine element such as a bearing 5 gear.

[Prior art]

Rotating machines such as motors and blowers, or rotating machine elements such as rolling bearings and gears, are prone to wear, damage, and damage due to rotation.
Otherwise, damage may occur due to poor lubrication or poor assembly. For this reason, it is necessary to detect and deal with damage to rotating machines and the like at an early stage.

The method for diagnosing abnormalities in rotary #B machines, etc. is wear.

Vibration methods that capture vibrations caused by abnormalities such as damage are common. Conventional vibration methods measure the vibrations of rotating machinery, etc., and perform fast Fourier transform on the vibrations to convert the vibrations, which are irregular functions expressed in the time domain, into functions in the frequency domain, and calculate their power spectrum. was calculated, and a deterioration index was calculated from the obtained power spectrum to determine abnormal conditions in rotating machinery, etc.

[Problem that the invention seeks to solve]

Vibration in rotating machinery, etc. includes vibrations (noise) other than vibrations caused by abnormalities such as wear and damage, especially transient vibrations, so conventional vibration methods cannot completely eliminate these noises. Therefore, it was not possible to calculate an accurate deterioration index.

Furthermore, it is not possible to accurately calculate the deterioration index even when the spectra of interest are close to each other.

[Means for solving problems]

The present invention has been made in view of the above circumstances, and it is an object of the present invention to provide a method for diagnosing rotating machines that can remove vibrations (noise) other than those caused by abnormalities and can calculate accurate deterioration indexes. purpose. The present invention measures vibrations in a rotating machine, calculates a power spectrum by applying an autoregressive model to the measured signal to remove transient noise, and then calculates the power spectrum and the intensity at a specific frequency of this power spectrum. It is characterized in that the ratio to the effective value is calculated as a deterioration index, and the deterioration probability is calculated from the calculated deterioration + 1 number.

(Principle) The principle of the method of the present invention will be explained below.
A detection signal of vibration in a rotating machine is shown.

Now, if the magnitude of vibration at time [ is X], it is considered that vibrations due to various factors are superimposed on this signal. Therefore, this signal is called a superimposed signal of various vibrations,
An autoregressive equation shown in equation (1) is set.

) i, = ij, α1 +a 2α2+°°“°゛→11
t= Σ a1α: 10u t...fl
li = 1 aI Nijistem parameter α,; Quantized vibration component u in the clock vibration: White noise (random signal with a constant power spectral density independent of frequency) h Nijistem order here Let us consider the system parameters and system order. First, in the case of system order M-1, equation (1) is expressed as follows.

X4=a1α1+u, ...(2)(2
) In the equation, if Xj is known (measured value), then uj =
xt aI α1 (2)', and if a1α is considered as an estimated value, uj represents an error.

Therefore, the Nk common value of aI is determined by the least squares method. In other words, the value of al that minimizes the expected value E (ut 2) of ut2 becomes the al value. Then, based on the obtained value a, the expected value E(ut)M-1 of uL is calculated.

Next, considering the case of system order M = 2, (1)
The formula is: The expected value E(ut
)+,=2 is calculated. Then, the previously calculated expected value E(ut)M=+ of U is compared with this E(ut)+1=2. Following the same procedure, the expected value E (ut)
Find M = 1, E(ut)M=+-+ and E(ut)M
i when the difference from =+ is less than a predetermined small value
Let be the system order M.

In other words, a1α1. a2α2. ...+ aMαh are considered to be vibration components with different causes, and if the vibration components are minute or less than a predetermined value, they are treated as white noise. Therefore, X expressed with a predetermined order M.

is considered to be a value representing the vibration from which noise has been removed at time t.

A similar method is used to remove noise from vibration signals extracted at predetermined time intervals.

Based on the vibration signal obtained in this way from which noise has been removed, its power spectrum is determined. The power spectrum is expressed by the following equation (4).

However, f: Frequency σu2: Variance of statistical estimation The power spectrum is obtained by equation (4), but if there is an abnormality in the rotating machine or the like, the intensity of the power spectrum will partially increase due to the abnormality.

Therefore, if the intensity of the power spectrum is partially increased, it can be inferred that an abnormality has occurred in the rotating i8I machine, etc., but depending on the type of rotating machine, etc. or the type of abnormality, The frequency characteristics of the prominent state of the power spectrum intensity due to the difference in frequency characteristics are different. Therefore, as a deterioration index representing the degree of abnormality in the rotating machine, the ratio between the effective value of the power spectrum intensity in the entire frequency range and the power spectrum I/raw intensity at the characteristic frequency defined by each abnormality is used.

FIGS. 2A to 2E show the relationship between power spectrum intensity and characteristic frequency caused by each abnormality in the rotating machine. Second
In the diagram (a), the rotating shaft of the rotating machine is in an unbalanced state, and fo indicates the frequency of the rotating shaft. The deterioration index F in this case is SIF, -□ (5) rms where SI: power spectrum intensity at frequency fo, 5 rvs: expressed as an effective value at all frequencies 14 of the power spectrum.

Figure 2 (b) shows misalignment I/I in a rotating machine.
Or it is a state where the shaft has deteriorated and the tit
The number F2 is rms S2. S3: Represented by power spectrum intensity at frequencies 2fo and 3fo.

FIG. 2(c) shows a case where the rotating machine has backlash, and the deterioration index F3 is expressed by the power spectrum intensity at rms So, s: frequency %fo.

FIG. 2(d) shows a case where an abnormality has occurred in the bearing.

In a bearing, the outer ring, inner ring, and rolling member each have power spectrum intensities as shown in FIG. 2 (d), but the frequency characteristics of each are different. K in the diagram
l (1 = 1 to 3) is a constant corresponding to each part when there is an abnormality in either the outer ring 9, the inner ring, or the rolling element, K1 is the outer ring abnormality, K2 is the inner ring abnormality, and K3 is the rolling element abnormality. Each case is shown. K, , , , , 3 is expressed as follows.

n d However, n: Number of rolling elements d: Diameter of rolling element 8 Diameter of pinch circle of rolling element α: Contact angle of rolling element Each deterioration index of the outer ring, inner ring, and rolling element is expressed as follows.

K41: Outer ring deterioration index F, 2: Inner ring deterioration index F4] Two rolling element deterioration index s, s2. S3: Frequency Kifo (1-+,
2.

3) Power spectrum intensity at 2 Kifo, 3 Kifo Figure 2 (e) shows the relationship between the power spectrum intensity and characteristic frequency when the gear is in a deteriorated state. fG is the meshing frequency of the gear, and f = Z fo
-(11) Z: Number of teeth fo: Rotation speed of the shaft. And the deterioration index F5 of this gear is SI+
82' frequency rG, each power spectrum at 2 rG]
- Raw strength This deterioration index is used to recognize an abnormal state, and to further evaluate the degree of abnormality, the probability of deterioration is determined.

In general, in reliability theory, the random failure probability R(t) is found as λ t R → = 1 - e (13) and is assumed to follow an exponential distribution, and the deterioration probability is determined by the deterioration index F. It is expressed as a function by the following equation (14). In other words, if the probability of deterioration is P (F) and the deterioration index is F, then Lo; + if no abnormality is considered to have occurred;
Maximum value of deterioration index of P (F) = O1 Mo: Maximum value of deterioration index of life fP (F) = 1.0 + Xo: Deterioration index when deterioration probability becomes 0.5 = slope of exponential curve (Determined by the type of abnormality, etc.) When equation (14) is expressed graphically, it becomes a curve as shown in FIG.

Through basic experiments and confirmatory tests of the Feel I data, the inventor of the present application has found the results shown in FIGS.
) to (12) Formulas were obtained for each rotary machine or the like, or a deterioration probability curve for the deterioration ti number for various abnormalities in the rotary machine. This is shown in figures j84-8. Figure 4 shows the relationship between the deterioration index and the probability of deterioration when the rotating shaft is in an unbalanced state, and Figure 5 shows the relationship between the deterioration index and the probability of deterioration when the rotating machine is in a misaligned or bent shaft state. FIG. 6 shows the relationship between the deterioration index and the deterioration probability when there is play in the rotating machine. Figures 7 (a) to (c) show cases where an abnormality occurs in the bearing. Figure 7 (a) shows a case where an abnormality occurs in the outer ring of the bearing, and Figure 7 (b) shows a case where an abnormality occurs in the inner ring of the bearing. When an abnormality occurs, FIG. 7(c) shows the deterioration probability with respect to the deterioration fft number when an abnormality occurs in the rolling elements of the bearing. FIG. 8 shows the probability of deterioration versus the number m of deterioration when an abnormality occurs in the gear.

Therefore, by calculating the deterioration index using equations (5) to (12) in I1iI, the probability of deterioration can be determined from the graphs in Figures 4 to 8, and the specific degree of abnormality in each rotating machine can be understood. .

〔Example〕

FIG. 9 is a schematic block diagram of the apparatus used to carry out the method of the invention. In the figure, 1 is a motor, and the output shaft of the motor 1 is connected to a shaft 5 through a compression ring 2. The shaft 5 is rotatably supported at both ends by bearings 3.3, and a load is applied to the shaft 5 by a hydraulic cylinder 6.

A vibration pink-up 7 is attached to one of the bearings 3, which captures vibrations of the bearing 3 and outputs a predetermined signal to a vibration meter 8. The output of the vibrometer 8 is given to the arithmetic processing unit 10 via the A/D converter 29, and the arithmetic processing unit 10 performs the The vibrations of the bearing 3 are extracted, and the vibrations at each time are quantized to remove noise superimposed on the vibrations. Then, a power spectrum is calculated based on the vibration from which noise has been removed at each time, and each deterioration index is calculated. Furthermore, the deterioration probability is calculated from the relationship between the preset deterioration index and the deterioration probability. The calculation results of the calculation processing unit IO are displayed and printed by the display/print unit 11.

The power spectrum obtained by the autoregressive model when diagnosing a normal bearing using this device is the 10th
Furthermore, FIG. 11 shows a power spectrum obtained by an autoregressive model of a bearing with an artificial defect added to the outer ring.

Further, Table 1 shows the deterioration index of each bearing, normal and abnormal, and the deterioration probability determined from the deterioration index. As is clear from Table 1, the probability of deterioration of the outer ring is 100%, and it becomes clear that an abnormality has occurred in the outer ring.

Note that FIG. 12 shows the results of fast Fourier transform of the vibrations of a normal bearing, and FIG. 13 shows the results of fast Fourier transform of the vibrations of a bearing with an artificial defect on the outer ring.

From Table 1, Figures 12 and 13, it is clear that there are defects;
In particular, it is difficult to determine that a defect has occurred in the outer ring.

In this embodiment, the abnormality of the outer ring of the bearing has been explained, but the inner ring of the bearing and the rolling elements can be determined in the same way, and the state of the abnormality can also be determined for other rotating machines.

〔effect〕

According to the present invention, it is possible to clearly grasp about 5 types of abnormalities in rotating machinery without being affected by noise etc.
Diagnosis of rotating machinery, etc. can be performed with high accuracy.

[Brief explanation of drawings]

Figure 1 is a graph for explaining the method of the present invention, Figure WS2 (
A) to (E) are graphs for explaining the deterioration index calculation method, FIGS. 3 to 8 are graphs showing the relationship between the deterioration index and the deterioration probability, and FIG. Figure 1 is a schematic diagram of the equipment used;
FIG. 2 and FIG. 13 are graphs obtained by fast Fourier transforming the vibration of the bearing using the conventional method. I...Motor 2...Coupling 3...Bearing
5...Axis 7...Vibration pink amplifier patent Applicant Sumitomo Metal Industries Co., Ltd. Agent Patent attorney Noboru Kono !l!0 1Q cloudy? Shell 4 Reed 0 0: Sho 9 Stomach meter 0 ;!88 ni Raft e Yayo -

Claims (1)

[Claims] 1. Measure the vibration in a rotating machine, create a power spectrum by modeling the measured signal into an autoregressive model to remove transient noise, and calculate the ratio of the intensity at a specific frequency of this power spectrum to the effective value of the power spectrum. A method for diagnosing a rotating machine, comprising: calculating the deterioration index as a deterioration index, and calculating a deterioration probability from the calculated deterioration index.