Research on Fault Diagnosis of Hydraulic Pump Bearing Based on Neural Network

Abstract: The fault diagnosis method of hydraulic pump bearing based on integrated BP network is studied. Using the frequency domain and the inverse frequency domain for feature extraction, an integrated BP network is adopted to diagnose and identify the fault, which solves the problems of the fault feature of the hydraulic pump bearing and the difficulty of multi-fault identification. The experimental results show that the integrated BP network can effectively diagnose and identify multi-failure modes of hydraulic pump bearings and has strong robustness. In the aviation industry, the working performance of the hydraulic system directly affects the safety of the aircraft and the lives of passengers. The hydraulic pump is the power source of the hydraulic system. Therefore, the hydraulic pump The status monitoring and troubleshooting is especially important. Bearing failure is one of the common failure modes of hydraulic pumps. Since the additional vibration caused by the bearing failure is weaker than the inherent vibration of the hydraulic pump, it is very difficult to separate the fault information from the signal. So far, there is still a lack of a very effective way to diagnose the fault of hydraulic pump bearing. In this paper, feature extraction in the frequency domain and the inverse frequency domain is proposed to solve the problem of bearing feature extraction difficult and to solve the problem of multi-fault diagnosis and identification and robustness by using integrated BP network. A characteristic of hydraulic pump bearing fault extraction Mechanical system, any fault must be caused by additional vibration of the system. The vibration signal is a dynamic signal, it contains a wealth of information, it is suitable for fault diagnosis. However, if the additional vibration signals are submerged due to the interference of the inherent signals or outside interference to the fault signals, how to extract the useful signals from the vibration signals is very crucial. According to tribology theory, when the bearing inner surface of the flow surface, the outer ring raceway and roller there is a damage, the smooth surface of the raceway is damaged, whenever the roller rolls over the injury point, there will be a vibration. Assuming that the bearing part is a rigid body and does not consider the influence of contact deformation, the roller is purely rolled along the raceway, then the following damage vibration frequency: When the inner raceway has a damage, the characteristic frequency of the vibration pulse is: fI = frZ + dcosα / D) / 2 (1) When the outer raceway has a damage, the vibration pulse frequency is: fo = frZ (1-dcosα / D) / 2 (2) When there is a damage on the roller, The characteristic frequency of the vibration pulse is: fR = frD (1 - d2cosα / D2) / d where: fr - speed of inner ring revolution; pitch circle diameter of D- bearing; d - diameter of roller; Z-roller number. In order to overcome the difficulty that bearing fault signal is weak and easily submerged by inherent vibration of hydraulic pump, the following characteristics with strong anti-interference ability are selected as fault diagnosis characteristic parameters. (1) Average Energy Characteristics of Vibration The vibration acceleration signal measured on the pump body of the hydraulic pump is: a (t) = {a1 (t), a2 (t), ..., an Fault signal to pump the signal after transmission. According to statistical theory, the root mean square of vibration reflects the time domain information of vibration: the characteristic parameter has its effective value representing the vibration signal and reflects the average energy of vibration. (2) Peak characteristic of vibration signal Pp = max {a (t)} (5) It is a characteristic quantity that reflects the periodic pulsation in the vibration signal. (3) Cepstral envelope feature Let f (t) be the fault excitation signal and h (t) be the impulse response of the transmission channel. Their corresponding Fourier transforms have the following relations: Transform (6) as follows: In the type, τ is called the frequency inversion; (τ) is cepstrum. It can be seen from the above equation that the characteristics of the fault excitation signal and the transmission channel are separated from each other. In general, the fault excitation signal and the transmission channel signal occupy different back-off sections, which can highlight the characteristics of the faulty vibration signal. The Hilbert transform is used to find the envelope of the time-domain signal in signal analysis, in order to smooth the power spectrum and highlight the fault information. Definition signal: the best envelope. Cepstral envelope model is the essence of the signal obtained from the sensor cepstrum analysis, and then its cepstrum signal envelope extraction, which dual highlighted the fault information for the signal to noise ratio of small fault feature extraction provided in accordance with. 2 integrated BP network troubleshooting principle Neural network organization structure is determined by solving the problem of the field characteristics. Due to the complexity of fault diagnosis system, the application of neural network to the design of fault diagnosis system will be a problem of organization and learning of large-scale neural network. In order to reduce the complexity of the work and reduce the learning time of the network, this dissertation decomposes the fault diagnosis knowledge set into several logically independent sub-sets, each sub-set is subdivided into a number of rule subsets, and then the network is organized according to the rule subsets. Each rule subset is a logically independent mapping of subnetworks, the relations between the subsets of rules, represented by the matrix of rights of the subnetworks. Each sub-network independently uses BP learning algorithm to study and train respectively. 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 fault diagnosis of hydraulic pump using integrated BP network is derived from the nonlinear mechanism characteristics and BP algorithm of neurons, as shown in Fig.1. Fig.1 Schematic diagram of BP network fault diagnosis Each subnetwork in Fig. 2 is a BP network. Each subnetwork is learned by BP algorithm and the result of learning is integrated by control network. The learning algorithm of BP network is as follows: Figure 2 Schematic diagram of integrated BP network Map the value of each selected feature parameter (including energy feature, amplitude feature and cepstral envelope feature) x to a single node in the input / output layer of neural network, (X) = 0.8 (x-xmin) / (xmax-xmin) +0.1 (8) The purpose of regressing the eigenparameters to (0.1,0.9) is to avoid the sigmoid function output value The problem of extreme learning that can not be converged. For the regular value obtained from (8), the following operation is performed to obtain the weighted value of each neuron and the threshold: where j represents the current layer, i represents the previous layer, wij represents the connection weight, cj represents the current node threshold ; Fj on behalf of the output. 3, Neural Network Robustness Neural network robustness refers to the neural network fault tolerance. It is well known that the human brain is fault tolerant and that the damage of individual neurons in the brain does not seriously degrade its overall performance because each of the concepts in the brain is not stored in just one neuron but rather in many nerves Yuan and its connection. By learning again, the brain can re-express knowledge forgotten due to the damage of a part of neurons in the remaining neurons. As the neural network is a simulation of the biological neural network, the most important feature of the neural network is the function of "associative memory". That is to say, the neural network can be composed of the past knowledge, and under the condition of partial information loss or partial information uncertainty, The remaining feature information to make the correct diagnosis. Table 2 shows the success rate of correctly diagnosing and identifying some of the six characteristic information of the bearing with incorrect or indeterminate input characteristics. Table 2 Robustness statistics of neural network Input characteristics Uncertainties Diagnosis success rate One characteristic parameter Uncertainty 100% Two characteristic parameters Uncertainty 94% Three characteristic parameters Uncertainty 76% Four characteristic parameters Uncertainty 70% Five Characteristic parameter uncertainty 20% Six characteristic parameter uncertainty 8% As can be seen from Table 2, the use of integrated neural network fault diagnosis can still make the right judgment in the case of losing a large amount of information (nearly half of the characteristic parameters are uncertain) Of the success rate is very high (76% ~ 100%) Therefore, the integrated neural network has a strong ability 5 Conclusion Since the neural network with self-learning, self-organization, associative memory and other functions determine the neural network method is very suitable for fault diagnosis the study. In this paper, the vibration signals in the frequency domain and the scrambling frequency domain are taken as the characteristic parameters, and the multi-fault diagnosis and identification of the hydraulic pump bearing is realized by the integrated BP network. Experimental results show that this method has high success rate and robustness.