Bearing Health Monitoring Using Relief-F-Based Feature Relevance Analysis and HMM

Nowadays, bearings installed in industrial electric motors are constituted as the primary mode of a failure affecting the global energy consumption. Since industries’ energy demand has a growing tendency, interest for efficient maintenance in electric motors is decisive. Vibration signals from bearings are employed commonly as a non-invasive approach to support fault diagnosis and severity evaluation of rotating machinery. However, vibration-based diagnosis poses a challenge concerning the signal properties, e.g., highly dynamic and non-stationary. Here, we introduce a knowledge-based tool to analyze multiple health conditions in bearings. Our approach includes a stochastic feature selection method, termed Stochastic Feature Selection (SFS), highlighting and interpreting relevant multi-domain attributes (time, frequency, and time–frequency) related to the bearing faults discriminability. In particular, a relief-F-based ranking and a Hidden Markov Model are trained under a windowing scheme to achieve our SFS. Obtained results in a public database demonstrate that our proposal is competitive compared to state-of-the-art algorithms concerning both the number of features selected and the classification accuracy.

A Monte Carlo Method for Simulating Stochastic Neutron Fields in Criticality Transients

In presence of a weak neutron source, the initial growth of neutron population in a supercritical system exhibits a significant stochastic feature, both initiation and burst waiting times are uncertain. As a result, the energy released during criticality excursions is stochastic, obeying a probability distribution. When criticality accidents and pulsed reactor experiments are analyzed, it is important to estimate this kind of stochastic feature, including assessing the initiation probability and then the fission energy probability distribution. Thus a Monte Carlo direct simulation method has been proposed and the corresponding code MES has been developed. By taking random factors during criticality excursions into account in dynamic Monte Carlo simulations, this method is capable of simulating the whole process from source injection to exponential growth of the neutron population, and finally to extinction of the neutron pulse. A set of static initiation probability problems and a figurative criticality excursion problem have been applied to validate this method and MES. Results demonstrate that with the proposed method MES is able to simulate stochastic transient neutron fields in multiplying systems during criticality excursions.

Imperfect mixing as a dominant factor leading to stochastic behavior: a new system exhibiting crazy clock behavior

The stochastic feature of the arsenous acid–periodate reaction is shown both experimentally and numerically to be originated from imperfect mixing and initial inhomogeneity.