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Reducer fuzzy diagnosis rejection matrix membership degree table fuzzy vector figure classification number document identification code introduction At present, the factory uses a large number of reducers, and most of the reducer use environment is very bad, due to their widespread existence, rarely cause people Pay attention to, improper maintenance, and the inside is prone to failure.
Usually people only use the auditory sense of hearing, according to the vibration of the size of the vibration to roughly determine the fault location of the fault, often resulting in misdiagnosis and missed diagnosis.
The traditional diagnostic method generally measures the vibration signal of the reducer housing, observes its peak value or RMS value, or performs fault diagnosis through general spectrum analysis.
However, since there is noise and other signal interference when the signal is measured, and some faults have the same fault characteristics, it is difficult to make an accurate judgment.
At the same time, the relationship between the fault signal characteristics and faults measured is ambiguous.
Therefore, in addition to the diagnosis of faults, if you need to further analyze the type of fault location, fuzzy fault diagnosis technology is a method.
In this paper, the time domain and frequency domain analysis of the fault signal of the reducer are used to determine the characteristic parameters reflecting its typical fault.
According to the on-site inspection data and the reference data provided by the Anshan Iron and Steel Fault Diagnosis Center, the membership degree table of each characteristic parameter is obtained, and the fuzzy diagnosis matrix is ​​used. The fuzzy evaluation method is used to calculate the maximum and minimum weighted average models in the fuzzy calculation method. Get the diagnosis.
Deceleration of fault characteristics of reducer The reducer used in the mine is usually divided into two types of single-stage transmission and multi-stage transmission.
Regardless of the type of transmission reducer, the components that make up its internal structure are the same, and are composed of a bearing gear coupling housing.
When an abnormality occurs in the reducer, it is generally caused by the failure of these components.
Therefore, the fault diagnosis of the reducer is also the fault diagnosis for these types of parts.
The most common faults are imbalances, such as loose shafts, broken shafts, and so on.
By analyzing the vibration signal of the reducer, the fault diagnosis of the reducer can be basically realized.
By analyzing the vibration signal of the reducer in the time domain and the frequency domain, the following conclusions are obtained: when the bearing fails, the high frequency signal will increase significantly, and the peak factor is obviously increased.
(4) The gear fails, its meshing frequency fz, and the peak at the meshing frequency multiplier increase significantly.
According to the above conclusions, the characteristic parameters reflecting the typical faults in the time domain and the frequency domain have the dimensionless factor reflecting the low frequency characteristics. The peak factor reflecting the fundamental frequency characteristics reflects the dimensionless factor of the multiplier frequency characteristic and the multiple axis. The dimensionless factor of frequency characteristics The dimensionless factor reflecting the meshing frequency characteristics of the gears The non-dimensional factors that reflect the characteristics of the gear meshing harmonics are calculated as follows: (1) When the gearbox has an unbalanced fault, the fundamental frequency of the vibration signal (f0) is Larger and larger peaks.
2) When the gearbox fails to be misaligned, a large peak occurs at the fundamental frequency and the fundamental frequency of the vibration signal.
The date is known as the peak of the private part, and the denominator represents the peak under normal conditions.
Yue Er Miao, which is the time domain signal, is the root mean square value.
It is the peak at the fundamental frequency.
The second positive is the peak at the base frequency.
Where are the peaks of the multiplier.
The second is the peak at the gear meshing frequency.
From now.
The fuzzy fuzzy comprehensive evaluation is based on the fuzzy fault diagnosis theory. The fuzzy set of fault causes and the fuzzy sets of their various feature elements have the following relationship. The sighs are given. The fault cause vector set fault feature vector set fuzzy diagnosis matrix .
The type of fault vector to be diagnosed has an imbalance. A coupling is not centered on a bearing, so a gear is eccentric and a tooth surface fault is rubbed.
The fuzzy diagnosis of the failure of the fault vector in the first phase of the Wang Dan reducer is extracted in the time domain frequency domain. The fuzzy diagnostic matrix is ​​determined by reference to the relevant literature and the relevant information of the Anshan Iron and Steel Fault Diagnosis Center and the investigation of the site inspectors. The fuzzy relational matrix is ​​as follows. The fuzzy parameter matrix characteristic parameters are verified by the two examples. The experiment is carried out on two speed reducers to be inspected in a workshop of Angang Chemical Plant.
The portable data collector is used to measure the vibration signal on each bearing housing of the reducer. After high-pass filtering, the characteristic parameters are extracted and the membership degree of the fault mode belongs to the fault set.
The subordinate value of the characteristic parameters and the fault state of the subordinates in the actual measured fault state are given below. The analysis results are consistent with the actual maintenance results, indicating the feasibility of the method.
The first reducer characteristic parameter value two membership degree forest two uses the weighted average type to calculate the state membership degree vector of the measuring point. The second seems to belong to the bearing failure.
The characteristic value of the second reducer belongs to the degree of the tooth surface fault.
Conclusion In this paper, the time domain and frequency domain of the fault signal of the reducer are analyzed, and the required data is obtained. Then the fuzzy comprehensive evaluation principle in fuzzy diagnosis theory is used to obtain the final result through fuzzy calculation, and compared with the on-site inspection results. It confirms the accuracy of the fuzzy diagnosis results and also demonstrates the feasibility of the fuzzy fault diagnosis principle.
Usually people only use the auditory sense of hearing, according to the vibration of the size of the vibration to roughly determine the fault location of the fault, often resulting in misdiagnosis and missed diagnosis.
The traditional diagnostic method generally measures the vibration signal of the reducer housing, observes its peak value or RMS value, or performs fault diagnosis through general spectrum analysis.
However, since there is noise and other signal interference when the signal is measured, and some faults have the same fault characteristics, it is difficult to make an accurate judgment.
At the same time, the relationship between the fault signal characteristics and faults measured is ambiguous.
Therefore, in addition to the diagnosis of faults, if you need to further analyze the type of fault location, fuzzy fault diagnosis technology is a method.
In this paper, the time domain and frequency domain analysis of the fault signal of the reducer are used to determine the characteristic parameters reflecting its typical fault.
According to the on-site inspection data and the reference data provided by the Anshan Iron and Steel Fault Diagnosis Center, the membership degree table of each characteristic parameter is obtained, and the fuzzy diagnosis matrix is ​​used. The fuzzy evaluation method is used to calculate the maximum and minimum weighted average models in the fuzzy calculation method. Get the diagnosis.
Deceleration of fault characteristics of reducer The reducer used in the mine is usually divided into two types of single-stage transmission and multi-stage transmission.
Regardless of the type of transmission reducer, the components that make up its internal structure are the same, and are composed of a bearing gear coupling housing.
When an abnormality occurs in the reducer, it is generally caused by the failure of these components.
Therefore, the fault diagnosis of the reducer is also the fault diagnosis for these types of parts.
The most common faults are imbalances, such as loose shafts, broken shafts, and so on.
By analyzing the vibration signal of the reducer, the fault diagnosis of the reducer can be basically realized.
By analyzing the vibration signal of the reducer in the time domain and the frequency domain, the following conclusions are obtained: when the bearing fails, the high frequency signal will increase significantly, and the peak factor is obviously increased.
(4) The gear fails, its meshing frequency fz, and the peak at the meshing frequency multiplier increase significantly.
According to the above conclusions, the characteristic parameters reflecting the typical faults in the time domain and the frequency domain have the dimensionless factor reflecting the low frequency characteristics. The peak factor reflecting the fundamental frequency characteristics reflects the dimensionless factor of the multiplier frequency characteristic and the multiple axis. The dimensionless factor of frequency characteristics The dimensionless factor reflecting the meshing frequency characteristics of the gears The non-dimensional factors that reflect the characteristics of the gear meshing harmonics are calculated as follows: (1) When the gearbox has an unbalanced fault, the fundamental frequency of the vibration signal (f0) is Larger and larger peaks.
2) When the gearbox fails to be misaligned, a large peak occurs at the fundamental frequency and the fundamental frequency of the vibration signal.
The date is known as the peak of the private part, and the denominator represents the peak under normal conditions.
Yue Er Miao, which is the time domain signal, is the root mean square value.
It is the peak at the fundamental frequency.
The second positive is the peak at the base frequency.
Where are the peaks of the multiplier.
The second is the peak at the gear meshing frequency.
From now.
The fuzzy fuzzy comprehensive evaluation is based on the fuzzy fault diagnosis theory. The fuzzy set of fault causes and the fuzzy sets of their various feature elements have the following relationship. The sighs are given. The fault cause vector set fault feature vector set fuzzy diagnosis matrix .
The type of fault vector to be diagnosed has an imbalance. A coupling is not centered on a bearing, so a gear is eccentric and a tooth surface fault is rubbed.
The fuzzy diagnosis of the failure of the fault vector in the first phase of the Wang Dan reducer is extracted in the time domain frequency domain. The fuzzy diagnostic matrix is ​​determined by reference to the relevant literature and the relevant information of the Anshan Iron and Steel Fault Diagnosis Center and the investigation of the site inspectors. The fuzzy relational matrix is ​​as follows. The fuzzy parameter matrix characteristic parameters are verified by the two examples. The experiment is carried out on two speed reducers to be inspected in a workshop of Angang Chemical Plant.
The portable data collector is used to measure the vibration signal on each bearing housing of the reducer. After high-pass filtering, the characteristic parameters are extracted and the membership degree of the fault mode belongs to the fault set.
The subordinate value of the characteristic parameters and the fault state of the subordinates in the actual measured fault state are given below. The analysis results are consistent with the actual maintenance results, indicating the feasibility of the method.
The first reducer characteristic parameter value two membership degree forest two uses the weighted average type to calculate the state membership degree vector of the measuring point. The second seems to belong to the bearing failure.
The characteristic value of the second reducer belongs to the degree of the tooth surface fault.
Conclusion In this paper, the time domain and frequency domain of the fault signal of the reducer are analyzed, and the required data is obtained. Then the fuzzy comprehensive evaluation principle in fuzzy diagnosis theory is used to obtain the final result through fuzzy calculation, and compared with the on-site inspection results. It confirms the accuracy of the fuzzy diagnosis results and also demonstrates the feasibility of the fuzzy fault diagnosis principle.
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