Abstract
To improve the fault diagnosis performance for rotating machinery, an efficient, noise-resistant end-to-end deep learning (DL) algorithm is proposed based on the advantages of the wavelet packet transform in vibration signal processing (the capability to extract multiscale information and more spectral distribution features) and deep convolutional neural networks (good classification performance, data-driven design and high transfer-learning ability). First, a vibration signal is subjected to pyramid wavelet packet decomposition, and each sub-band coefficient is used as the input for each channel of a deep convolutional network (DCN). Then, based on the lightweight modeling requirements and techniques, a new DCN structure is designed for the fault diagnosis. The proposed algorithm is compared with the support vector machine algorithm and the published DL algorithms based on a bearing dataset produced by Case Western Reserve University. The experimental results show that the proposed algorithm is superior to the existing algorithms in terms of accuracy, memory space, computational complexity, noise resistance, and transfer performance, producing good results.
Highlights
Rotating machinery systems have been extensively applied in a number of engineering fields and play an increasingly pivotal role.Rotating machinery damage and faults severely affect the reliability and safety of the entire system and cause tremendous economic losses
Based on the differences in the feature extraction and fault diagnosis algorithms, rotating machinery fault diagnosis algorithms can be classified into two types, namely, vibration analysis and intelligent diagnosis [1,2,3,4,5]
deep CNNs (DCNNs) models that differ in structure have been proposed based on
Summary
Rotating machinery systems have been extensively applied in a number of engineering fields (e.g., aviation, ships and warships, machine tools and vehicles) and play an increasingly pivotal role. Zhao et al [19] proposed an LSTMNN-based fault diagnosis algorithm This type of algorithm is effective in detecting faults in time-series data and is able to discover problems that arise as time elapses, but it has a relatively high network complexity and a weak transfer-learning ability. DCNN models that differ in structure have been proposed, based on 1D CNNs, for predicting faults in various types of rotating machinery [21,22,23,24] Most of these models use the original signal as the input but neglect its frequency-domain characteristics. Zhao et al [28] proposed to use a wavelet packet-residual network hybrid algorithm for predicting faults in gearboxes This type of algorithm is effective in analyzing multidimensional data and can effectively extract local features. The available studies are deficient in lightweight DL (as shown in Table 1), cannot meet the new requirements of the IIoT, and fail to sufficiently consider the noise resistance and transfer-learning ability of algorithms
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