Diagnosis method based on topology codification and neural network applied to an industrial camshaft

Since the last years, there is an increasing interest from the industrial sector to provide the electromechanical systems with diagnosis capabilities. In this context, this work presents a novel monitoring scheme applied to diagnose faults in the main rotatory element of an industrial packaging machine, the camshaft. The developed diagnosis method considers a coherent procedure to process the acquired measurement. First, the current signals acquired from the main motor are processed in a normalized time-frequency map. Next, the characteristics fault patterns are identified and numerically characterized. A double self-organized map structure is proposed to manage the information till compress it to just two features by means of a topology codification of the data space. Finally, a neural network based classification algorithm is used to classify the condition of the camshaft. The effectiveness of this condition monitoring scheme has been verified by experimental results obtained from industrial machinery.