Abstract: A fully automatic feedforward neural network structural and weight learning algorithm is described. The Augmentation by Training with Residuals, ATR, requires neither guess of initial weight values ...

Abstract: This paper describes a method to diagnose the most frequent faults of a screw compressor and assess magnitude of these faults by tracking changes in compressor’s dynamics. To determine the ...

Abstract: For automatic detection and diagnosis of localized defects in rolling element bearings, bicoherence spectra are used to derive features that signify the condition of a bearing. These features ...

Abstract: A new sensor has been developed to automatically detect face-milling insert fracture, chipping, and wear. With a befitting PVDF (polyvinylidene fluoride) sensor, and an optical transmitter based ...

Abstract: This paper describes an automated localized modeling method to identify continuous nonlinear dynamic systems from their operating data. Using a method similar to finite element method’s automatic ...

Abstract: For automatic detection/diagnosis of localized defects in bearings, a pattern recognition analysis scheme was developed for investigating vibration signals of bearings. Two normalized and dimensionless ...

Abstract: This paper describes a new learning algorithm for time-varying recurrent neural networks whose weights are functions of time instead of scalars. First, an objective functional that is a function ...

Abstract: This paper presents a learning self-tuning (LSTR) regulator which improves the tracking performance of itself while performing repetitive tasks. The controller is a self-tuning regulator based on ...