Abstract: The fault diagnosis method of hydraulic pump bearing based on integrated BP network is studied. The frequency domain and the scrambling frequency domain are used for feature extraction. The integrated BP network is used for fault diagnosis and identification, which solves the problem that the fault characteristics of the hydraulic pump bearing are difficult and the fault identification is difficult. The test results show that the integrated BP network can effectively diagnose and identify the multi-fault mode of the hydraulic pump bearing, and it has strong robustness.
Key words: hydraulic pump; bearing fault; fault diagnosis; integrated BP network
In the aerospace industry, the performance of the hydraulic system directly affects the safety of the aircraft and the life of the passenger. The hydraulic pump is the power source of the hydraulic system, so it is especially important for the status monitoring and fault diagnosis of the hydraulic pump. Bearing failure is one of the common failure modes of hydraulic pumps. Because the additional vibration caused by bearing failure is weak relative to the natural vibration of the hydraulic pump, it is difficult to separate the fault information from the signal. So far, there is a lack of effective methods for troubleshooting faults in hydraulic pump bearings. This paper proposes feature extraction in frequency domain and frequency domain, aiming at solving the problem of difficult bearing feature extraction and using integrated BP network to solve multi-fault diagnosis and identification and robustness problems.
1 Feature extraction of hydraulic pump bearing faults
For mechanical systems, if there is a fault, it will definitely cause additional vibration of the system. The vibration signal is a dynamic signal, and it contains a wealth of information, which is very suitable for fault diagnosis. However, if the additional vibration signal is overwhelmed by the inherent signal or external interference to the fault signal, how to extract the useful signal from the vibration signal is very important.
According to the tribology theory, when a damage occurs on the inner ring, the outer ring raceway and the roller of the bearing flow surface, the surface of the raceway is smoothly damaged, and whenever the roller rolls over the damage point, a vibration is generated. Assuming that the bearing parts are rigid, regardless of the influence of contact deformation, the rollers are pure rolls along the raceway.
The Hilbert transform is used in signal analysis to find the envelope of the time domain signal to smooth the power spectrum to highlight fault information. Define the signal: the best envelope. The cepstrum envelope model essentially performs cepstrum analysis on the signal obtained from the sensor, and then extracts the cepstrum signal from the sensor, thereby highlighting the fault information and providing the fault feature with low SNR. in accordance with.
2 Principle of integrated BP network for fault diagnosis
The organizational structure of the neural network is determined by the domain characteristics of the problem being solved. Due to the complexity of the fault diagnosis system, the application of neural networks to the design of obstacle diagnostic systems will be a problem of organization and learning of large-scale neural networks. In order to reduce the complexity of the work and reduce the learning time of the network, this paper decomposes the fault diagnosis knowledge set into several logically independent sub-sets, each sub-set is decomposed into several rule subsets, and then the network is organized according to the rule subset. Each rule subset is a logically independent mapping of sub-networks, and the associations between the subsets of rules are represented by the weight matrix of the sub-network. Each sub-network independently uses the BP learning algorithm to perform learning training separately. Since the decomposed sub-network is much smaller than the original network and the problem is localized, the training time is greatly reduced. The information processing capability of hydraulic pump bearing fault diagnosis using integrated BP network is derived from the nonlinear mechanism characteristics of neurons and BP algorithm.
3. Research on robustness of neural networks
The robustness of a neural network refers to the fault tolerance of a neural network to faults. As we all know, the human brain is fault-tolerant, and the damage of individual neurons in the brain does not seriously degrade its overall performance. This is because every concept in the brain is not only stored in one neuron but scattered in many nerves. Yuan and its connections. The brain can re-express the knowledge that is forgotten by the damage of some neurons by re-learning in the remaining neurons. Since the neural network is a simulation of the biological neuron network, the biggest feature of the neural network is the "associative memory" function, that is, the neural network can be combined with previous knowledge, in the case of partial information loss or partial information uncertainty. The remaining feature information makes a correct diagnosis. Table 2 shows the success rate of correct diagnosis and identification of some of the six characteristic information of the bearing in case of incorrect or uncertain conditions.
Table 1 Neural Network Robustness Statistics
Input feature uncertainty element diagnostic success rate
One characteristic parameter is undefined 100%
Two characteristic parameters are uncertain 94%
Three characteristic parameters are undefined 76%
Four characteristic parameters are uncertain 70%
Five characteristic parameters are undefined 20%
Six characteristic parameters are undefined 8%
It can be seen from Table 1 that the fault diagnosis using integrated neural network can achieve a fairly high success rate (76%~100%) and thus integration in the case of losing a large amount of information (nearly half of the characteristic parameters are uncertain). Neural network has strong ability
5 Conclusion
Because neural networks have many functions such as self-learning, self-organization, and associative memory, the neural network method is very suitable for fault diagnosis research. In this paper, the vibration signals in frequency domain and frequency domain are used as characteristic parameters, and the integrated BP network is used to realize the fault diagnosis and identification of hydraulic pump bearings. The experimental results show that the method has high success rate and robustness.
Key words: hydraulic pump; bearing fault; fault diagnosis; integrated BP network
In the aerospace industry, the performance of the hydraulic system directly affects the safety of the aircraft and the life of the passenger. The hydraulic pump is the power source of the hydraulic system, so it is especially important for the status monitoring and fault diagnosis of the hydraulic pump. Bearing failure is one of the common failure modes of hydraulic pumps. Because the additional vibration caused by bearing failure is weak relative to the natural vibration of the hydraulic pump, it is difficult to separate the fault information from the signal. So far, there is a lack of effective methods for troubleshooting faults in hydraulic pump bearings. This paper proposes feature extraction in frequency domain and frequency domain, aiming at solving the problem of difficult bearing feature extraction and using integrated BP network to solve multi-fault diagnosis and identification and robustness problems.
1 Feature extraction of hydraulic pump bearing faults
For mechanical systems, if there is a fault, it will definitely cause additional vibration of the system. The vibration signal is a dynamic signal, and it contains a wealth of information, which is very suitable for fault diagnosis. However, if the additional vibration signal is overwhelmed by the inherent signal or external interference to the fault signal, how to extract the useful signal from the vibration signal is very important.
According to the tribology theory, when a damage occurs on the inner ring, the outer ring raceway and the roller of the bearing flow surface, the surface of the raceway is smoothly damaged, and whenever the roller rolls over the damage point, a vibration is generated. Assuming that the bearing parts are rigid, regardless of the influence of contact deformation, the rollers are pure rolls along the raceway.
The Hilbert transform is used in signal analysis to find the envelope of the time domain signal to smooth the power spectrum to highlight fault information. Define the signal: the best envelope. The cepstrum envelope model essentially performs cepstrum analysis on the signal obtained from the sensor, and then extracts the cepstrum signal from the sensor, thereby highlighting the fault information and providing the fault feature with low SNR. in accordance with.
2 Principle of integrated BP network for fault diagnosis
The organizational structure of the neural network is determined by the domain characteristics of the problem being solved. Due to the complexity of the fault diagnosis system, the application of neural networks to the design of obstacle diagnostic systems will be a problem of organization and learning of large-scale neural networks. In order to reduce the complexity of the work and reduce the learning time of the network, this paper decomposes the fault diagnosis knowledge set into several logically independent sub-sets, each sub-set is decomposed into several rule subsets, and then the network is organized according to the rule subset. Each rule subset is a logically independent mapping of sub-networks, and the associations between the subsets of rules are represented by the weight matrix of the sub-network. Each sub-network independently uses the BP learning algorithm to perform learning training separately. Since the decomposed sub-network is much smaller than the original network and the problem is localized, the training time is greatly reduced. The information processing capability of hydraulic pump bearing fault diagnosis using integrated BP network is derived from the nonlinear mechanism characteristics of neurons and BP algorithm.
3. Research on robustness of neural networks
The robustness of a neural network refers to the fault tolerance of a neural network to faults. As we all know, the human brain is fault-tolerant, and the damage of individual neurons in the brain does not seriously degrade its overall performance. This is because every concept in the brain is not only stored in one neuron but scattered in many nerves. Yuan and its connections. The brain can re-express the knowledge that is forgotten by the damage of some neurons by re-learning in the remaining neurons. Since the neural network is a simulation of the biological neuron network, the biggest feature of the neural network is the "associative memory" function, that is, the neural network can be combined with previous knowledge, in the case of partial information loss or partial information uncertainty. The remaining feature information makes a correct diagnosis. Table 2 shows the success rate of correct diagnosis and identification of some of the six characteristic information of the bearing in case of incorrect or uncertain conditions.
Table 1 Neural Network Robustness Statistics
Input feature uncertainty element diagnostic success rate
One characteristic parameter is undefined 100%
Two characteristic parameters are uncertain 94%
Three characteristic parameters are undefined 76%
Four characteristic parameters are uncertain 70%
Five characteristic parameters are undefined 20%
Six characteristic parameters are undefined 8%
It can be seen from Table 1 that the fault diagnosis using integrated neural network can achieve a fairly high success rate (76%~100%) and thus integration in the case of losing a large amount of information (nearly half of the characteristic parameters are uncertain). Neural network has strong ability
5 Conclusion
Because neural networks have many functions such as self-learning, self-organization, and associative memory, the neural network method is very suitable for fault diagnosis research. In this paper, the vibration signals in frequency domain and frequency domain are used as characteristic parameters, and the integrated BP network is used to realize the fault diagnosis and identification of hydraulic pump bearings. The experimental results show that the method has high success rate and robustness.
Salt Water Flake Ice Machine,Plate Ice Maker,Industrial Large Scale Ice Machine,Large Scale Ice Maker
Changshu Shenghai Electric Appliances Co., Ltd. , https://www.icefrostorm.com