Introduction to Novel Rolling

In this study, we proposed a novel rolling bearing fault diagnosis strategy based on multi-channel convolution neural network(MCNN) combining multi-scale clipping fusion(MSCF) data augmentation technique.

This article focuses on the development and implementation a novel rolling horizon robust online scheduling framework that utilizes stochastic optimization within a model-based feedback scheme to tackle the uncertainties in electricity prices, electric power demands, water inflows and plant model parameters.

In order to solve the difficulty of compound fault diagnosis of rolling bearings, a novel rolling bearings fault diagnosis method based on improved tunable Q-factor wavelet transform (TQWT) is proposed in this paper.

In order to diagnose rolling bearing fault accurately, a novel rolling bearing fault diagnosis method based on adaptive feature selection and clustering is proposed.

We develop a novel rolling horizon algorithm to solve this challenging problem in real-time, which explicitly considers the limited OC capacities and use of a back-up delivery capacity (company-owned or third party provided) to ensure the service quality.

RESULTS This study showed that, irrespective of the approach used to quantify the physical demands (traditional [average measures per minute] and novel rolling average time epoch [most demanding scenarios]), during the HALF condition players covered less and performed a lower number of high-intensity accelerations and decelerations than in HTRAN (Bayesian factor > 10 and standardized effect size > 0.

A novel rolling experiment was developed, in which the grid method was employed to capture the tiny metal flow along the width.

To solve this problem, this paper presents a novel rolling bearing vibration signal fault feature extraction and fault pattern recognition method based on variational mode decomposition (VMD), permutation entropy (PE) and support vector machines (SVM).

Our method first removes the high‐frequency signals from the curvature tensor field of an input freeform surface by a novel rolling guidance tensor filter, which results in a more regular and smooth curvature tensor field, then deforms the input surface to match the smoothed field as much as possible.

R-TPI employs a novel rolling enrollment scheme, which allows concurrent patient enrollment that is faster than cohort-based enrollment.

A novel rolling process characterized by multi-pass bi-axial reduction was proposed.

In the present study, a novel rolling method is proposed, which is called Multi-Rotational Flat Rolling (MRFR).

The results may be beneficial for understanding the fundamental behaviors of novel rolling conducting rotary joint.

To improve the fault identification accuracy of rolling bearing and effectively analyze the fault severity, a novel rolling bearing fault diagnosis and severity analysis method based on the fast sample entropy, the wavelet packet energy entropy, and a multiclass relevance vector machine is proposed in this paper.