Optimisation assisted CNN framework for bearing fault diagnosis
by Azim Naz M.; R. Sarath
International Journal of Networking and Virtual Organisations (IJNVO), Vol. 26, No. 4, 2022

Abstract: Nowadays, intellectual fault diagnosis mechanism with DL schemes was extensively deployed in production firms to develop the effectiveness of fault diagnosis. The rolling bearings connect the support and rotor and are considered as a critical element in rotating equipments. Nevertheless, the working state of bearing varies based on composite operation demand that may drastically corrupt the performances of the intellectual fault diagnosis technique. Thereby, this scheme develops novel fault diagnosis schemes that included the two most important stages like 'feature extraction' and 'classification'. Initially, the features namely, 'empirical wavelet transform', 'empirical mode decomposition' and 'wavelet transform' are extracted. Subsequent to this, the derived features are classified via 'optimised convolutional neural network' is employed. Further, to get better accuracy using adopted model, the weights of CNN is tuned via self-adaptive moth-flame optimisation. Eventually, the primacy of the offered scheme is proven regarding varied measures. Eventually, the proposed technique has obtained a superior value of 0.922, and it is 1.62%, 1.92%, 50.76%, and 57.34%, superior to existing MFO, FF, SVM and RF models for dataset 1.

Online publication date: Mon, 08-Aug-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Networking and Virtual Organisations (IJNVO):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com