English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The simply multi-segment averages of mechanical fault features lead to the loss of signal uncertainty probability statistics which may contribute to fault diagnosis. To solve the problem, a probability box modeling method of mechanical fault features is proposed. Three similar dimensionless factors (peak factor,form factor, margin factor) in time domain were used to obtain probability boxes. The algorithm of modeling was then proposed. Firstly, the mechanical fault raw data was divided into several segments according to the sampling frequency. Secondly, the dimensionless features in time domain were obtained for every segment. After verifying the normal distribution probability distribution type, the row vector of features was divided into several sets. From these sets, the range of the mean and variance were obtained. Finally, the DSS of mean and variance were integrated into the probability modeling algorithm of normal distribution to get the probability box. The rolling bearing’s faults raw data was used to verify this modeling method. The results show that the probability box modeling method can effectively reflect the uncertainties of fault state, which can be used for fault diagnosis. KeywordsProbability box theory; modeling; Uncertainty; Mechanical Fault; fault feature

A Probability Box Modeling Method of Dimensionless Mechanical Fault Features in Time Domain