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Abstract
A smart factory is a highly digitized and networked production facility based on smart manufacturing. A smart manufacturing plant is the result of intelligent systems deployed in the factory. Smart factories have higher production volumes and are prone to machine failures when operating in almost all applications on a daily basis. With the growing concept of smart manufacturing required for Industry 4.0, intelligent methods for detecting and classifying bearing faults have become a subject of scientific research and interest. In this paper, a deep learning-based 1-D convolutional neural network is proposed using the time-sequence bearing data from the Case Western Reserve University (CWRU) bearing database. Four different sets of data are used. The proposed method achieves state-of-the-art accuracy even with a small amount of training data. For the sensitivity analysis of the proposed method, metrics such as precision, recall, and f-measure are determined. Next, we compare the proposed method with a 2-D CNN that uses two-dimensional image illustrations of raw data as input. This method shows the effectiveness of using 1-D CNNs over 2-D CNNs for time-sequence data. The proposed method is computationally inexpensive and outperforms the most complex and computationally intensive algorithms used for bearing fault detection and diagnosis.
Highlights
Electrical machines are used ubiquitously in industrial applications nowadays
The dataset used for this research was the CWRU bearing dataset [48], one of the most popular bearing datasets, which is publicly provided by Case Western Reserve University on their website [2]
Regarding other parameters used in the network, a rectified linear unit (ReLU) was used as an activation unit for all the convolution layers, and for the classification, SoftMax was used
Summary
Electrical machines are used ubiquitously in industrial applications nowadays. With the development and advancement in science and technology, modern industries are developing rapidly. Cepstrum analysis is computationally expensive, and it accuracies were stated [7,8] These conventional signal processing methods generates many undesired large peaks near the zero point, making the output complex to carry some time-domain uses theregarding natural properties of frethe interpret. PrerequisiteThe of some experiencemethod and knowledge the resonance vibration signals in band the time domain, suchanalysis as rootchallenging mean square, crestMoreover, factor, quadratic quency and filtering makes envelope to use These characteristics can be used in dynamic system monitoring transform remains weak in the selection of an appropriate mother wavelet, decomposition applications to effectively reflect transient conditions assuming a stationary level, and respective frequency band, whichmachine is necessary information for fault analysis signal. DL-based algorithms are ubiquitously used in almost every field
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