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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The vibration analysis of rotating machinery indicates the condition of potential faults such as unbalance, bent shaft, shaft crack, bearing clearance, rotor rub, misalignment, looseness, oil whirl and whip, and other malfunctions. More than one fault can occur in a rotor. This paper describes the application of an artificial neural network (ANN) and wavelet transform (WT) for the prediction of the effect of the combined faults of unbalance and shaft crack on the frequency components of the vibration signature of the rotating machinery. The experimental data of the frequency components and the corresponding root mean square (RMS) velocity (amplitude) data are used as inputs to train the ANN, which consists of a three-layered network. The ANN is trained using an improved multilayer feed forward back propagation Levenberg-Marquardt algorithm. In particular, the overall success rates achieved were 99.78% for unbalance, 99.81% for shaft crack, and 99.45% for the combined faults of unbalance and shaft crack. The wavelet transform approach enables instant to instant observation of different frequency components over the full spectrum. A new technique combining the WT with ANN performs three general tasks: data acquisition, feature extraction, and fault identification. This method is tested successfully for the individual and combined faults of unbalance and shaft crack at a success rate of 99.9%.

Role of an Artificial Neural Network and a Wavelet Transform for Condition Monitoring of the Combined Faults of Unbalance and Cracked Rotors