Neural Networks
Robust model-based fault diagnosis for dynamic systems
Robust model-based fault diagnosis for dynamic systems
Features-based decision aggregation in modular neural network classifiers
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Fault diagnosis in power plant using neural networks
Information Sciences: an International Journal - Intelligent manufacturing and fault diagnosis (II). Soft computing approaches to fault diagnosis
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
Computational Intelligence in Fault Diagnosis (Advanced Information and Knowledge Processing)
Computational Intelligence in Fault Diagnosis (Advanced Information and Knowledge Processing)
A Fast Fourier Transform for High-Speed Signal Processing
IEEE Transactions on Computers
Expert Systems with Applications: An International Journal
Dynamic Growing Self-organizing Neural Network for Clustering
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Adaptive growing-and-pruning neural network control for a linear piezoelectric ceramic motor
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Self-adaptive neural networks based on a Poisson approach for knowledge discovery
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The role of lossless systems in modern digital signal processing: atutorial
IEEE Transactions on Education
Fuzzy reasoning spiking neural P system for fault diagnosis
Information Sciences: an International Journal
Information Sciences: an International Journal
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Fault detection and diagnosis have gained widespread industrial interest in machine monitoring due to their potential advantage that results from reducing maintenance costs, improving productivity and increasing machine availability. This article develops an adaptive intelligent technique based on artificial neural networks combined with advanced signal processing methods for systematic detection and diagnosis of faults in industrial systems based on a classification method. It uses discrete wavelet transform and training techniques based on locating and adjusting the Gaussian neurons in activation zones of training data. The learning (1) provides minimization in the number of neurons depending on cost error function and other stopping criterions; (2) offers rapid training and testing processes; (3) provides accuracy in classification as confirmed by the results on real signals. The method is applied to classify mechanical faults of rotary elements and to detect and isolate disturbances for a chemical process. Obtained results are analyzed, explained and compared with various methods that have been widely investigated for fault diagnosis.