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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Condition monitoring is one of the routine tasks in all major processindustries. The mechanical parts such as a motor, gear, bearings are the majorcomponents of a process industry and any fault in them may cause a totalshutdown of the whole process, which may result in serious losses. Therefore,it is very crucial to predict any approaching defects before its occurrence.Several methods exist for this purpose and many research are being carried outfor better and efficient models. However, most of them are based on theprocessing of raw sensor signals, which is tedious and expensive. Recently,there has been an increase in the feature based condition monitoring, whereonly the useful features are extracted from the raw signals and interpreted forthe prediction of the fault. Most of these are handcrafted features, wherethese are manually obtained based on the nature of the raw data. This of courserequires the prior knowledge of the nature of data and related processes. Thislimits the feature extraction process. However, recent development in theautoencoder based feature extraction method provides an alternative to thetraditional handcrafted approaches; however, they have mostly been confined inthe area of image and audio processing. In this work, we have developed anautomated feature extraction method for on-line condition monitoring based onthe stack of the traditional autoencoder and an on-line sequential extremelearning machine(OSELM) network. The performance of this method is comparableto that of the traditional feature extraction approaches. The method canachieve 100% detection accuracy for determining the bearing health states ofNASA bearing dataset. The simple design of this method is promising for theeasy hardware implementation of Internet of Things(IoT) based prognosticssolutions.

A Stacked Autoencoder Neural Network based Automated Feature Extraction Method for Anomaly detection in On-line Condition Monitoring