The training of Artificial Intelligence algorithms for machine diagnosis often requires a huge
amount of data, which is scarcely available in industry. This work shows that convolutional …

Deep learning algorithms often require thousands of training instances to generalize well.
The presented research demonstrates a novel algorithm, Predict-Evaluate-Correct K-fold …

Detection and isolation of single and mixed faults in a gearbox are very important to
enhance the system reliability, lifetime, and service availability. This paper proposes a …

Most statistical analysis technologies use detection thresholds for fault diagnosis, which
often cannot effectively characterize some specific faults in a statistical manner. However …

A hierarchical structure based on a Deep LSTM Supervised Autoencoder Neural Network
(Deep LSTM-SAE NN) is presented for the detection and classification of faults in industrial …

The massive high-dimensional measurements accumulated by distributed control systems
bring great computational and modeling complexity to the traditional fault diagnosis …

A new temporal-correlated feature learning method, multiblock temporal convolutional
network (MBTCN), is proposed for supervised fault diagnosis of multivariate processes in …

This paper proposes the stiffness nonlinearities and asymmetric SD (smooth and
discontinuous) oscillator under time-delayed feedback control with the fractional derivative …

The concept of Internet of Things (IoT) was designed to change everyday lives of people via
multiple forms of computing and easy deployment of applications. In recent years, the …

Reliability is a cumbersome problem in High Performance Computing Systems and Data
Centers evolution. During operation, several types of fault conditions or anomalies can arise …