- Open Access
Fault detection for hydraulic pump based on chaotic parallel RBF network
© Lu et al; licensee Springer. 2011
- Received: 27 December 2010
- Accepted: 30 August 2011
- Published: 30 August 2011
In this article, a parallel radial basis function network in conjunction with chaos theory (CPRBF network) is presented, and applied to practical fault detection for hydraulic pump, which is a critical component in aircraft. The CPRBF network consists of a number of radial basis function (RBF) subnets connected in parallel. The number of input nodes for each RBF subnet is determined by different embedding dimension based on chaotic phase-space reconstruction. The output of CPRBF is a weighted sum of all RBF subnets. It was first trained using the dataset from normal state without fault, and then a residual error generator was designed to detect failures based on the trained CPRBF network. Then, failure detection can be achieved by the analysis of the residual error. Finally, two case studies are introduced to compare the proposed CPRBF network with traditional RBF networks, in terms of prediction and detection accuracy.
- Fault detection
- Chaotic parallel radial basis function (CPRBF)
- Hydraulic pump
- Residual error generator
- Time series prediction
Fault detection is becoming important because of the complexity of modern industrial systems and growing demands on quality, cost efficiency, reliability, and safety. Early fault detection is an essential prerequisite for further development of automatic supervision. The interest on fault detection techniques would be increasing correspondingly.
Hydraulic pump is the power source of a hydraulic system in aircraft. Its performance has a direct impact on the stability of the hydraulic system and even on the entire system. It has been proved based on statistical data that hydraulic pump has a higher fault probability over other mechanical systems, thus, it is specifically necessary to investigate and conduct fault detection techniques for hydraulic pump. In this article, considering the complexity of hydraulic system and its severe working conditions, the data-driven fault detection method is suggested and applies to its online fault detection.
Generally, data-driven based fault detection consists of the following aspects: data measurement, data processing, data comparison, and data assessment . Usually, the vibration signal of hydraulic pump is used for fault detection in practice, and artificial neural network (ANN) models have also been widely applied to intelligent fault diagnosis owing to their intrinsic parallel, adaptability, and robustness [2, 3].
Current data-driven based fault detection methods for hydraulic pump pay more attentions to not only linear characteristics but also nonlinear ones. In addition, owing to the universal presence of chaotic phenomena and the intrinsic characteristics and complex operation conditions of hydraulic system, strong nonlinearity and chaotic features can be clearly found from the vibration signals of hydraulic pump. Therefore, the research works on chaos-based fault detection for hydraulic pump should have a high engineering application value. Currently, chaotic correlation dimension has been applied well for condition monitoring and fault diagnosis of hydraulic pump. In addition, some research works based on Duffing oscillator and Lyapunov exponent have been employed to qualitatively or quantitatively solve the incipient fault recognition for hydraulic pump, with good diagnosis performance. However, the method based on neural network in conjunction with chaos theory has rarely appeared, especially for the fault detection of hydraulic pump [4–7].
Among several types of neural networks, radial basis function (RBF) network has relatively high convergence speed, and can approximate to any nonlinear functions. It has been proved that RBF network has a very high performance, in terms of nonlinear time series prediction, fault diagnosis in industrial systems, sensor and flight control systems, etc. [8–14].
A CPRBF network for fault detection of hydraulic pump is presented in this article. This CPRBF network was first trained using the dataset from the normal state without fault of hydraulic pump, and then a residual error generator was designed to detect several types of failures of hydraulic pump based on the trained CPRBF network with one-step prediction of chaotic time series. The proposed model, based on Camastra and Colla's approach  and Yang et al.' method , is able to reduce the effect of cumulative error and improve the prediction accuracy of RBF.
This article is divided into three sections as follows: Section "Phase-space reconstruction of chaotic time series" describes the chaotic theory on phase space reconstruction employed to obtain the estimation of correlation dimension. Section "Model of chaotic time series prediction and fault detection" proposes a new CPRBF network for chaotic time series prediction, and a residual error generator based on CPRBF network was also designed to detect fault. Then, Section "Case studies" gives several case studies, including simulation results of one-step iterative prediction and experimental results of fault detection for hydraulic pump.
Phase-space reconstruction of chaotic time series
where D is the dimension of system attractor. In order to obtain a correct system embedding dimension, starting from the time series, it is necessary to estimate the attractor dimension D.
The algorithm plots a cluster of lnC m (r)-ln(r) curves through increasing m until the slope of the curve's linear part is almost constant. Then, the correlation dimension estimation D can be attained using least square regression.
Model of chaotic time series prediction and fault detection
In practice, it is difficult to get the exact estimation value of the minimum embedding dimension through G-P algorithm. Furthermore, a single RBF network uses the estimation value of minimum embedding dimension as the number of its input, usually resulting in an inaccurate output due to the inaccurate estimation of embedding dimension from human factor. Therefore, a PRBF network consisting of multiple RBF subnets is proposed to increase the system performance with decreased error.
Structure of CPRBF
The CPRBF consists of n RBF subnets, which are denoted as sub-RBF i (i = 1,2,...,n), respectively. Each sub-RBF subnet realizes one-step prediction independently at t + 1. After the training of sub-RBF by historical dataset, one-step predicted value can be obtained. The final predicted value of PRBF can be achieved through proper weighted combination of .
Input nodes of subnet
Estimation value of the minimum embedding dimension is regarded as the number of input nodes in the central subnet, and each of other subnets uses different numbers (calculated based on m) as its input size.
In this article, each subnet RBF i uses the default parameters: the number of hidden layer is one, and the number of hidden nodes is equal to the number of input vectors.
Calculation of weighted factors
where N is the number of samples.
Residual error generator
Evaluation of residual error
Residual error evaluation is an important step of fault detection. In this article, threshold selector is adopted to evaluate the residual error. The concept of threshold selector is firstly introduced systematically in  to solve the residual error evaluation problem of LTI systems with model uncertainty. The diagnostic decision is obtained based on the following rule:
reval > J th → fault state detected
reval ≤ J th → normal state
where reval is a function related to residual error signal and employed to measure its deviation value, Jth is the threshold.
The corresponding standard of threshold value can also be determined based on diagnostic experiences in conjunction with different working conditions.
Process of fault detection
The detailed process is described as below:
Step 1. Normalize the original time series from diagnosed system
Step 2. Determine the number of input nodes of each subnet in CPRBF according to G-P algorithm and Takens' theory
Step 3. Determine weighted factor ω based on the one-step prediction result of each subnet
Step 4. Calculate the final one-step prediction output of CPRBF
Step 5. Construct a residual error generator, and calculate the residual error according to the predicted output and the corresponding system output
Step 6. Choose a residual error evaluation function with a threshold standard
Step 7. Fault can be detected based on the evaluation function, with a fault alarm, once the residual error exceeds the threshold value.
Verification results of one-step iterative prediction
Considering the lack of practicability from a common one-step prediction method, one-step iterative prediction should be adopted to verify the prediction performance instead. In general, each predicted result at Step 4 is consecutively used as the next input data to achieve one-step iterative prediction. The future trend of actual case (Lorenz's attractor, hydraulic pump) can be obtained gradually with the repetition of Steps 3 and 4, and the loop times depends on the length of actual expected data.
Simulation of Lorenz's attractor
where σ = 16, r = 45.95, b = 4. 1,000 points of X-component Lorenz time series data were first normalized and used for the following prediction.
One-step iterative prediction based on CPRBF network has good prediction performance
Comparing with RBF network, CPRBF network has better performance on iterative prediction, in terms of convergence and stability.
Experimental result using real data of hydraulic pump
Lyapunovs of hydraulic pump's sample data
Comparing with RBF network, PRBF model has higher prediction accuracy, without the effect of error accumulation.
Experimental results of fault detection for hydraulic pump
Construction of detection model using CPRBF
Residual error signals of hydraulic pump based on CPRBF network
Wear fault of valve plate
Dry friction is probably caused by fatigue crack, surface wear, or cavitation erosion, etc. In case of this failure, with the increasing of moment coefficient between rotor and valve plate, contact stress grows and oil film becomes thinner. Further, as a repetitive impact of the contact stress, the surface of valve plate is fatigued and spalls. As a result, dry friction appears, with an increment of motion gap of hydraulic pump and a decrement of volumetric efficiency. Meanwhile, the dry friction inevitably generates additional vibration signals in the valve plate's shell near the high pressure chamber.
Wear fault between swash plate and slipper
Dry friction, caused by oil impurities or small holes on plunger ball, etc., usually results in wear or burnout of the faying surface between swash plate and slipper, which probably causes the falling of slipper, and affects the performance of hydraulic pump.
Threshold value is a key point in fault decision-making, due to uncertainties in practical and external disturbances. The rate of fail-to-report increases if the threshold is too large, vice versa, the rate of false alarm would increase. Appropriate threshold should be selected according to the analysis, with the support of residual error evaluation function proposed, on hydraulic pump's normal and faulty data.
Variance values of residual error series
Residual error signal
Mean of variance
It can be seen obviously from Table 2 that, two magnitude levels of residual error's variance values between normal and fault states are clearly distinct. According to experience, the threshold can be determined with a standard of 10 times higher than the mean of variances under normal states. Here, Jth = 3.196e-005. It should be also noticed that, the threshold standard must be re-adjusted according to different working conditions.
The variance values of the above two cases are 3.7781e-004 and 1.7305e-004, respectively. These values are greater than Jth, thus, the fault can be detected based on the variance of residual error signal.
It is shown from the simulation results that, CPRBF network model, in conjunction with phase space reconstruction, show better capabilities and reliability in predicting chaotic time series, as well as a high performance of convergence ability and prediction precision on short-term prediction of chaotic time series.
The experimental results show that, CPRBF model has high ability in approximation to the output and state of a normal system, which is useful for fault detection. The CPRBF network can memorize various nonlinear states or interferences of a system with normal states, therefore, the actual system output will be different with the predicted output of CPRBF network once any anomaly occurs, and the system can be regarded as faulty state if the residual error exceeds the threshold. Thus, CPRBF network based method is effective to real-time fault detection. However, it is also shown from the experiments that different types of faults might represent the same fault form, accordingly, the proposed method is not suitable for performing fault location but for conducting condition monitoring. Further work will focus on how to isolate any type of fault and identify its fault classification.
This article mainly aims to discuss the feasibility and possibility of practical fault detection for hydraulic pump using neural network in conjunction with chaos theory. A commonly used neural network in the past and now, namely, RBF network was employed for fault detection for hydraulic pump in conjunction with chaos theory. Certainly, methods using chaos theory combined with other popular ANNs should be also our emphasis in the following works. As known, support vector machine (SVM) has been widely applied in many fields. Compared with other ANNs, SVM overcomes many defects, such as over-fitting, local convergence. In addition, SVM has advantages over other ANNs, in terms of robustness and prevention of curse of dimensionality, etc. Thus, our further work will focus on SVM in conjunction with chaos theory, especially for those modified SVM.
The research is supported by the National Natural Science Foundation of China (Grant Nos. 61074083, 50705005), as well as the Technology Foundation Program of National Defense (Grant No. Z132010B004). The authors are also very grateful to the reviewers and the editor for their valuable suggestions.
- Chen KY, Lim CP, Lai WK: Application of a neural fuzzy system with rule extraction to fault detection and diagnosis. J Intell Manuf 2005, 16: 679-691. 10.1007/s10845-005-4371-1View ArticleGoogle Scholar
- Hanna MM, Buck A, Smith R: Fuzzy petri nets with neural networks to model products quality from a CNC-milling machining centre. IEEE Trans Syst Man Cyber A 1996, 26: 638-645. 10.1109/3468.531910View ArticleGoogle Scholar
- Polycarpou MM, Helmicki AJ: Automated fault detection and accommodation: a learning system approach. IEEE Trans Syst Man Cyber 1995, 25: 1447-1458. 10.1109/21.467710View ArticleGoogle Scholar
- Jiang WL, Chen DN, Yao CY: Correlation dimension analytical method and its application in fault diagnosis of hydraulic pump. Chin J Sens Actuators 2004,17(1):62-65.Google Scholar
- Jiang WL, Zhang YM, Wang HJ: Hydraulic pump fault diagnosis method based on lyapunov exponent analysis. Mach Tool Hydraulics 2008,36(3):183-184.MathSciNetGoogle Scholar
- Wang QJ, Zhang XB, Zhang HP, Sun Y: Application of fractal theory of fault diagnosis for hydraulic pump. J Dalian Maritime Univ 2004,30(2):40-43.MathSciNetGoogle Scholar
- Cai YL, Liu HM, Lu C, Luan JH, Hou WK: Incipient fault detection for plunger ball of hydraulic pump based on Duffing oscillator. Aerosp Mater Technol 2009,39(suppl):302-305.Google Scholar
- Park J, Sandberg IW: Universal approximation using radial-basis-function networks. Neural Comput 1991,3(2):246-257. 10.1162/neco.1918.104.22.168View ArticleGoogle Scholar
- Poggio T, Girosi F: Networks for approximation and learning. Proc IEEE 1990,78(9):1481-1497. 10.1109/5.58326View ArticleGoogle Scholar
- Chen S: Nonlinear time series modeling and prediction using Gaussian RBF networks with enhanced clustering and RLS learning. Electron Lett 1995,31(2):117-118. 10.1049/el:19950085View ArticleGoogle Scholar
- Potts MAS, Broomhead DS: Time series prediction with a radial basis function neural network. SPIE Adapt Signal Process 1991, 1565: 255-266.View ArticleGoogle Scholar
- Narendra KG, Sood VK, Khorasani K, Patel R: Application of a radial basis function (RBF) neural network for fault diagnosis in a HVDC system. IEEE Trans Power Syst 1998,13(1):177-183. 10.1109/59.651633View ArticleGoogle Scholar
- Yu DL, Gomm JB, Williams D: Sensor fault diagnosis in a chemical process via RBF neural networks. Control Eng Practice 1999,7(1):49-55. 10.1016/S0967-0661(98)00167-1View ArticleGoogle Scholar
- Chen YM, Lee ML: Neural networks-based scheme for system failure detection and diagnosis. Math Comput Simul 2002,58(2):101-109. 10.1016/S0378-4754(01)00330-5MathSciNetView ArticleGoogle Scholar
- Camastra F, Colla AM: Neural short-term prediction based on dynamics reconstruction. Neural Process Lett 1999, 9: 45-52. 10.1023/A:1018619928149View ArticleGoogle Scholar
- Yang HY, Ye H, Wang GZ, Zhong MY: A PMLP based method for chaotic time series prediction. Proceedings of the 16th IFAC World Congress, Mo-E11-To/2 2005.Google Scholar
- Packard N, Crutcheld J, Farmer J, Shaw R: Geometry from a time series. Phys Rev Lett 1980, 45: 712-716. 10.1103/PhysRevLett.45.712View ArticleGoogle Scholar
- Takens F: Detecting strange attractors in turbulence. Dynamical Systems and Turbulence, Warwick 1980, Lecture Notes in Mathematics 898 (Springer, Berlin) 1981, 366-381.View ArticleGoogle Scholar
- Grassberger P, Procaccia I: Measuring the strangeness of strange attractors. Physica 1983, D9: 189-208.MathSciNetGoogle Scholar
- Emami-Naeini A, Akhter MM, Rock SM: Effect of model uncertainty on failure detection: the threshold selector. IEEE Trans Autom Control 1988, 33: 1106-1115. 10.1109/9.14432View ArticleGoogle Scholar
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