A Training Algorithm for Locally Recurrent NN Based on Explicit Gradient of Error in Fault Detection Problems

In this work a diagnostic approach for nonlinear systems is presented. The diagnosis is performed resorting to a neural predictor of the output of the system, and by using the error prediction as a feature for the diagnosis. A locally recurrent neural network is used as predictor, after it has been trained on a reference behavior of the system. In order to model the system under test a novel training algorithm that uses an explicit calculation of the cost function gradient is proposed. The residuals of the prediction are affected by the deviation of the parameters from their nominal values. In this way, by a simple statistical analysis of the residuals, we can perform a diagnosis of the system. The Rossler hyperchaotic system is used as benchmark problem in order to validate the diagnostic neural approach proposed.

[1]  Rolf Isermann,et al.  Fault diagnosis of machines via parameter estimation and knowledge processing - Tutorial paper , 1991, Autom..

[2]  Rolf Isermann.  FAULT DIAGNOSIS OF MACHINES VIA PARAMETER ESTIMATION AND KNOWLEDGE PROCESSING , 1992 .

[3]  George D. Magoulas,et al.  Nonmonotone BFGS-trained recurrent neural networks for temporal sequence processing , 2011, Appl. Math. Comput..

[4]  Augusto Montisci,et al.  A Neural Based Approach and Probability Density Approximation for Fault Detection and Isolation in Nonlinear Systems , 2011, EANN/AIAI.

[5]  Enrico Zio,et al.  A data-driven approach for predicting failure scenarios in nuclear systems , 2010 .

[6]  O. Nelles.  Nonlinear System Identification , 2001 .

[7]  O. Nelles.  Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models , 2000 .

[8]  Guangyi Wang,et al.  A new modified hyperchaotic Lü system , 2006 .

[9]  James H. Garrett,et al.  Engineering applications of neural networks , 1993, J. Intell. Manuf..

[10]  Jie Chen,et al.  Robust Model-Based Fault Diagnosis for Dynamic Systems , 1998, The International Series on Asian Studies in Computer and Information Science.

[11]  O. Rössler.  An equation for hyperchaos , 1979 .

[12]  Ah Chung Tsoi,et al.  Locally recurrent globally feedforward networks: a critical review of architectures , 1994, IEEE Trans. Neural Networks.

[13]  Robert M. Pap,et al.  Fault Diagnosis , 1990, Bayesian Networks in Fault Diagnosis.

[14]  Yen-Ming Chiang,et al.  A two‐step‐ahead recurrent neural network for stream‐flow forecasting , 2004 .

[15]  M. Marchesi,et al.  Learning of Chua's circuit attractors by locally recurrent neural networks , 2001 .

[16]  K. Patan.  Stability Analysis and the Stabilization of a Class of Discrete-Time Dynamic Neural Networks , 2007, IEEE Transactions on Neural Networks.

[17]  Bhaskar D. Rao,et al.  On-line learning algorithms for locally recurrent neural networks , 1999, IEEE Trans. Neural Networks.

[18]  Rolf Isermann,et al.  Trends in the Application of Model Based Fault Detection and Diagnosis of Technical Processes , 1996 .