Deep Learning Based Intelligent Industrial Fault Diagnosis Model
نویسندگان
چکیده
In the present industrial revolution era, mechanical system becomes incessantly highly intelligent and composite. So, it is necessary to develop data-driven monitoring approaches for achieving quick, trustable, high-quality analysis in an automated way. Fault diagnosis essential process verify safety reliability operations of rotating machinery. The advent deep learning (DL) methods employed diagnose faults machinery by extracting a set feature vectors from vibration signals. This paper presents Intelligent Industrial Diagnosis using Sailfish Optimized Inception with Residual Network (IIFD-SOIR) Model. proposed model operates on three major processes namely signal representation, extraction, classification. uses Continuous Wavelet Transform (CWT) preprocessed representation original signal. addition, ResNet v2 based extraction applied generate high-level features. Besides, parameter tuning carried out sailfish optimizer. Finally, multilayer perceptron (MLP) as classification technique proficiently. Extensive experimentation takes place ensure outcome presented gearbox dataset motor bearing dataset. experimental indicated that IIFD-SOIR has reached higher average accuracy 99.6% 99.64% simulation ensured attained maximum performance over compared methods.
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ژورنال
عنوان ژورنال: Computers, materials & continua
سال: 2022
ISSN: ['1546-2218', '1546-2226']
DOI: https://doi.org/10.32604/cmc.2022.021716