JPS5994018A - Trouble diagnosing apparatus for rotary machine - Google Patents

Abstract

PURPOSE:To obtain a trouble diagnosing apparatus of a rotary machine which enables automatic decision on the cause of a trouble by extracting a feature value from a vibration detection signal to read out a data base which determines a cause-effect relation between the memorized feature value. CONSTITUTION:A vibration signal of a rotary machine from a signal input section 3 undergoes a degree ratio analysis, an amplitude probability density analysis, a phase analysis or the like with a cause-effect estimate section 4 to extract a feature value such as linear spectrum value, vibration effective value ratio, ratio between a harmonic spectrum value and the linear spectrum value. Based on the feature value, a conversion parameter and a weight coefficient are read out for a data base which determines a cause-effect relation between the feature value and the cause of a trouble from a cause-effect list memory section 5. Based on the read value, an estimate section 4 calculates a deterioration index classified by the cause of troubles and the cause of a trouble is automatically decided on and indicated on a display 6.

Description

DETAILED DESCRIPTION OF THE INVENTION The present invention relates to a failure diagnosis device for a rotating machine that has a function of automatically determining the cause of a failure of the rotating machine using vibration signals generated from the rotating machine.

In general, in order to analyze vibration signals obtained from rotating machines, frequency analysis is performed on digital signals that are A/D converted at intervals synchronized with changing rotation, and characteristic vibration signals that represent the operating state of the rotating machine are analyzed. It is necessary that the frequency components are disturbed in signal/noise (S/N) ratio as a result of order ratio analysis. Furthermore, it is desirable that the cause of the failure can be automatically estimated based on the feature values obtained from the order ratio analysis, without being affected by the type of rotating machine to be diagnosed, the number of rotations, or the load condition.

Conventionally, diagnosis of rotating machines has been performed using general-purpose signal analysis equipment, and it has been difficult to collect data in a form that allows order ratio analysis. Furthermore, in order to evaluate the state of machine deterioration based on the results obtained from normal frequency analysis and probability density analysis of vibration amplitude, a great deal of knowledge regarding equipment diagnosis accumulated over many years of experience is required. The drawback was that the judgments varied depending on the model, size, or individual differences.

SUMMARY OF THE INVENTION An object of the present invention is to provide a diagnostic device for a rotating machine that has a signal input section that can collect vibration signals of the rotating machine in a form that allows order ratio analysis, and that can automatically determine the cause of failure based on various analysis results.

The present invention will be explained below based on the drawings.

FIG. 1 is a block diagram showing one embodiment of the present invention. In FIG. 1, 1 is a vibration signal input terminal, 2 is a rotation pulse signal input terminal, 3 is a signal input section, 4 is a failure cause estimation section,
Reference numeral 5 indicates a failure cause and effect notation boundary section, and 6 indicates a result display section. In this fault diagnosis device, an analog signal of vibration from a rotating machine (not shown) to be diagnosed, such as a motor, a blower, a gear, etc. is input from a vibration signal input terminal 1, and a rotation pulse signal is input from a rotation pulse input terminal 2 to a signal input section 3.
Import into. FIG. 2 shows an example of 70 vibration analog signals taken in from the vibration signal input terminal 1 and an example of a rotation pulse signal 8 taken in from the rotation pulse input terminal 2. As will be described later, the signal input section 3 generates a vibration signal 7 using a sampling pulse whose frequency is multiplied in synchronization with the rotation pulse signal 8.
is A/D converted, and the vibration data SL is output to the failure cause estimating section 4. The failure cause estimating unit 4 performs frequency analysis, that is, order ratio analysis, on the input and stored vibration data S1.

Performs signal processing such as amplitude probability density analysis and calculates multiple vibration features. FIG. 3 shows a display example of the order analysis result as an example of signal processing. Next, calculate the probability of each of the multiple possible failure causes. A database stored in the failure cause and effect notation section 5 is used for this calculation, and the result regarding the subtracted failure cause is displayed on a result display section 6 which is constituted by a CRT, printer or the like.

Next, details of each component and its operation will be explained.

FIG. 4 is a block diagram showing details of the signal input section 3.

The rotation pulse signal 8 input from the rotation pulse signal input terminal 2 is synchronous multiplication #! It becomes a frequency-multiplied pulse signal at I12. The multiplication ratio at this time is determined by selecting the highest analysis order when performing order ratio analysis. What is degree 1?
If the frequency corresponds to the frequency of rotation, it is called first-order, and if you try to analyze it up to the nth-order frequency component, it is called 2n of the rotation frequency.
The vibration signal must be sampled at a factor of 2. Since the frequency of the rotational pulse signal 8 may vary over time, the synchronous multiplier circuit 12 generates a signal having a set multiplication ratio while being synchronized with the rotational pulse signal 8. This pulse frequency information is transmitted to the cut-off frequency control circuit 1:
3 and controls the cut-off frequency of the low-pass filter 14. The low-pass filter 14 is composed of, for example, a switched capacitor filter whose cut-off frequency is controlled by a pulse frequency or an active filter whose cut-off frequency is controlled by a voltage, and is capable of continuously changing the cut-off frequency. The vibration signal input from the vibration signal input terminal 1 passes through a low-pass filter I4 to reduce the aliasing effect caused by sampling, is A/D converted by an A/D converter 15, and is used to estimate the cause of failure. It is output to section 4. A/
As the sampling pulse of the D converter 15, a pulse generated by the synchronous multiplier circuit 12 is used.

Next, a method for estimating the cause of failure will be explained.

The failure cause estimation section 4 performs signal processing such as order analysis, amplitude probability density analysis, two-phase analysis, etc. on the vibration data S1 inputted from the signal input section 3. From these analysis results, for example, the ratio between the primary spectrum value and the vibration effective value, and the value of the harmonic spectrum 9.10 and the primary spectrum 1 shown in Figure 3.
Ratio of 1 to 1 shift ((vo+Vto)/Vn)
, or calculate a plurality of feature quantities such as the ratio between the maximum amplitude value and the average value - The fault causal table storage unit 5 stores a database indicating the causal relationship between the vibration feature quantity and the cause of failure, that is, a fault causal cloth. There is. An example of such a database structure is shown in the table below.

This database contains vibration features for each assumed cause of failure, such as unbalance or misalignment of rotating machinery.
Parameter a for converting to a deterioration index normalized to 1,
Weight that indicates what kind of correlation there is between j+ bij (' is the subscript corresponding to the failure cause, ) is the subscript corresponding to the vibration feature) and one failure cause for which there are multiple deterioration indices. It takes a matrix structure having a coefficient W17. These parameters a2. .

b, and the weighting coefficient Wi, are simple artificial intelligence determined based on the physical properties of mechanical vibration and many years of experience.
The database in the failure cause table storage unit 5 is configured to be changeable as necessary.

The failure cause estimating unit 4 converts the feature quantity of the vibration signal calculated using the above-mentioned failure cause parameter into a deterioration index. For example, the conversion function P, , (x) is defined as shown in the following equation (1).

However, the feature quantity of the X-vibration signal in FIG. 5 shows this function. That is, the calculated feature amount X of the vibration signal is the reference value ai, and b7. When the value is between 0 and 1, the deterioration index becomes a variable that takes a value between 0 and 1. The deterioration index calculated in this way is the model of the machine.

This is a value that is not significantly affected by size. A value indicating the possibility of a particular failure cause is calculated by combining these deterioration indices. For example, if it is synthesized by linear combination, the failure degree PF' is expressed as the following equation (2).

PF7 = W71 P7. + Wi2piz +
...--(2) is calculated. By performing the calculation of equation (2) above for all possible causes of failure, it is easy to determine which cause of failure is most likely.

As is clear from the above description, according to the present invention, it is possible to easily perform order ratio analysis useful for fault diagnosis of rotating machines, and to obtain a fault diagnosis device that can automatically determine the cause of the fault.

[Brief explanation of the drawing]

FIG. 1 is a block circuit diagram showing one embodiment of the present invention, and FIG.
The figure shows an example of a vibration analog signal and rotational pulse signal taken in from a rotating machine, Figure 3 shows an example of displaying the results of the order ratio analysis performed in the failure cause estimation section shown in Figure 1, and Figure 4 shows the result of Figure 1. A block diagram showing details of the signal input section shown in FIG.
The figure is a graph showing an example of a conversion function from the feature quantity of a vibration signal to a deterioration index used in the failure cause estimating section shown in FIG. 1... Vibration signal input terminal, 2... Rotation pulse signal input terminal, 3... Signal input section, 4... Failure cause estimation section, 5
...Fault cause and effect table storage section, 6..Result display section, 7..
・Vibration analog signal, 8...Rotation pulse signal, 9.1
0...Harmonic spectrum, 11...1st order spectrum, 12...Synchronous multiplier circuit, 1
3. Cutoff frequency control circuit, 14...Low pass filter, 15. A/D variable 'lA
Device Patent Applicant Representative Patent Attorney Tomoyuki Yafuki (and 1 other person) Japan Radio Co., Ltd. 5-1-1 Shimotatsujaku, Mitaka City 0 Inventor Kiyohiko Tatebayashi Japan Radio Co., Ltd. 5-1-1 Shimotatsujaku, Mitaka City Within Co., Ltd.Applicant: Japan Radio Co., Ltd. 5-1-1 Shimotatsujaku, Mitaka City

Claims (2)

[Claims] (1) A signal input unit that converts an analog vibration signal obtained from a rotating machine that is the subject of failure diagnosis into a digital signal, and a causal relationship between the characteristic amount of the vibration signal indicating a failure of the rotating machine and the cause of the failure. Calculates a plurality of feature quantities from the time-series data of the vibration signal collected from the failure cause-and-effect table storage unit that stores the database, and the signal input unit, and calculates a plurality of feature quantities from the time-series data of the vibration signal collected from the failure-cause-effect table storage unit that stores the database, and based on the database stored in the failure cause-and-effect table storage unit. 1. A failure diagnosis device for a rotating machine, comprising a failure cause estimating section for estimating the causes of various failures, and a result display section for displaying the estimation results, and capable of automatically diagnosing the causes of failures. (2) The signal input unit is synchronized with the rotational pulse signal obtained from the rotating machine and generates a sampling pulse with a frequency multiplied by the rotational frequency, and is an analog/digital device under the control of a nuclear device. (A/D) conversion; a low-pass filter whose cutoff frequency changes in response to changes in the rotational frequency in order to reduce aliasing effects; and an A/D converter that performs the A/D conversion. A fault diagnosing device for a rotating machine according to claim 1, characterized in that it is comprised of: