Abstract
Incomplete diagnostic information, inadequate multisource sensor information, weak diagnosis models, and subjective experience result in difficulty in predicting rotating machinery faults. To overcome these limitations, we proposed a multiple domain and heterogeneous information entropy fusion model based on an optimisation of bearing fault diagnosis. The spatiotemporal approach uses a multiscene domain fusion strategy based on heterogeneous sensors (HSMSF) to extract feature fusion strategies and analyses the characteristics of the bearing fault features by multichannel processes with convolutional neural networks to vibration signals. After the mapping of multiple quality characteristics, the high-quality features are combined with each other, and the adaptive entropy weighted fusion method is used to analyse and make decisions on sensor information from different detection points. Nineteen key model parameters that were required for HSMSF construction were selected by adaptive optimisation using the chaos elitist modified sparrow search algorithm (CEI-SSA), and a self-learning diagnostic model that is suitable for multiple detection points was constructed. The validity and feasibility of the proposed fault diagnosis method were verified experimentally on two common reference-bearing datasets, CWRU and IMS, and compared with other fault diagnosis methods.
Highlights
Incomplete diagnostic information, inadequate multisource sensor information, weak diagnosis models, and subjective experience result in difficulty in predicting rotating machinery faults
We proposed a multiple domain and heterogeneous information entropy fusion model based on an optimisation of bearing fault diagnosis. e spatiotemporal approach uses a multiscene domain fusion strategy based on heterogeneous sensors (HSMSF) to extract feature fusion strategies and analyses the characteristics of the bearing fault features by multichannel processes with convolutional neural networks to vibration signals
5 3 3 15 22 38 55 “Relu” “Relu” “Relu6” “Relu” “Maxpool” “Avgpool” “Avgpool” 50 0.001 RMSprop 48 algorithms are shown in Figures 22(a) and 22(b), and a visualisation of the support vector machine (SVM) classification results of the shallow learning algorithm is shown in Figures 22(c) and 22(d). is proves that the unique HSMSF design and the chaos elitist modified sparrow search algorithm (CEI-SSA) adaptive model optimisation method proposed in this paper can use multisensor information for fault analysis, remove redundant structures, and improve diagnostic efficiency and stability for practical application with reduced labour, time, and other costs
Summary
E purpose was to optimise the problem of feature singularity in the original 1D domain by describing details such as the point, line, image colour, or cross boundary In this HSMSF study, the high-dimensional mapping of data samples has five conversion modes: time domain (TD), continuous wavelet transform (CWT), time-frequency domain [13], Gramian angular field [14], Markov transition field (MTF) [15], and recurrence plot field (RPF) [16]. 32 3 × 3 convolution blocks and 32 2 × 2 max-pooling blocks were added to the front end as a preconvolution layer in the complex feature domain, and the LSTM that was used for the spatiotemporal feature decomposition adopted a singlelayer network structure Such feature extractors are mostly models selected based on human experience, and the determination of internal parameters depends on extensive expert experience for debugging.
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