Anomaly detection for advanced military aircraft using neural networks

Automated Prognostics and Health Management (PHM) is a requirement for the advanced military aircraft. PHM is the key to achieving true condition-based maintenance. PHM processing strategies include modules for the detection, diagnosis and prognosis of known fault conditions. However in real operations there will also occur faults and other off-nominal operations that were never anticipated nor ever encountered before. We call these events anomalies. Missing the presence of an anomaly could potentially be catastrophic with the loss of the pilot and aircraft. We have developed a neural net approach for performing anomaly detection. The neural net anomaly detector 'learns' to recognize consistent sets of multiple input sensor signal patterns from known nominal data. It is generic and has been applied to a variety of aircraft subsystems and for fusion with other detectors with excellent results. Presented are a description of the neural net anomaly detector and the application to advanced military aircraft.

[1]  Patrick K. Simpson,et al.  Fuzzy min-max neural networks - Part 2: Clustering , 1993, IEEE Trans. Fuzzy Syst..

[2]  T. Brotherton,et al.  Classification and novelty detection using linear models and a class dependent-elliptical basis function neural network , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[3]  Patrick K. Simpson,et al.  Fuzzy min-max neural networks. I. Classification , 1992, IEEE Trans. Neural Networks.

[4]  Ryan Mackey,et al.  Generalized cross-signal anomaly detection on aircraft hydraulic system , 2001, 2001 IEEE Aerospace Conference Proceedings (Cat. No.01TH8542).

[5]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[6]  Patrick K. Simpson,et al.  Fuzzy neural network machine prognosis , 1995, Defense, Security, and Sensing.

[7]  R. Mackey,et al.  Anomaly detector fusion processing for advanced military aircraft , 2001, 2001 IEEE Aerospace Conference Proceedings (Cat. No.01TH8542).

[8]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[9]  T. Brotherton,et al.  Application of neural nets to feature fusion , 1992, [1992] Conference Record of the Twenty-Sixth Asilomar Conference on Signals, Systems & Computers.

[10]  D.R. Hush,et al.  Progress in supervised neural networks , 1993, IEEE Signal Processing Magazine.

[11]  T. Brotherton,et al.  Automated rule extraction for engine vibration analysis , 1999, 1999 IEEE Aerospace Conference. Proceedings (Cat. No.99TH8403).

[12]  P. K. Simpson.  Fuzzy Min-Max Neural Networks-Part 1 : Classification , 1992 .