A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment.

Purpose The purpose of this paper is to study the effects of these major parameters, including layer thickness, deposition velocity and infill rate, on product’s mechanical properties and explore the quantitative relationship between these key parameters and tensile strength of the part. Design/methodology/approach A VHX-1000 super-high magnification lens zoom three-dimensional (3D) microscope is utilized to observe the bonding degree between filaments. A temperature sensor is embedded into the platform to collect the temperature of the specimen under different parameters and the bilinear elastic-softening cohesive zone model is used to analyze the maximum stress that the part can withstand under different interface bonding states. Findings The tensile strength is closely related to interface bonding state, which is determined by heat transition. The experimental results indicate that layer thickness plays the predominant role in affecting bonding strength, followed by deposition velocity and the effect of infill rate is the weakest. The numerical analysis results of the tensile strength predict models show a good coincidence with experimental data under the elastic and elastic-softened interface states, which demonstrates that the tensile strength model can predict the tensile strength exactly and also reveals the work mechanism of these parameters on tensile strength quantitatively. Originality/value The paper establishes the quantitative relationship between main parameters including layer thickness, infill rate and deposition velocity and tensile strength for the first time. The numerically analyzed results of the tensile strength predict model show a good agreement with the experimental result, which demonstrates the effectiveness of this predict model. It also reveals the work mechanism of the parameters on tensile strength quantitatively for the first time.

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Extracting robust fault sensitive features of vibration signals remains a challenge for rotating machinery fault diagnosis under variable operating conditions. Most existing fault diagnosis methods based on the convolutional neural network (CNN) can only extract single-scale features, which not only loss fault sensitive information on other scales, but also suffer from the domain shift problem. In this work, a novel end-to-end deep learning network named adaptive weighted multiscale convolutional neural network (AWMSCNN) is proposed to adaptively extract robust and discriminative multiscale fusion features from raw vibration signals. The AWMSCNN consists of three main components: the denoising layer, the adaptive weighted multiscale convolutional (AWMSC) block, and the multiscale feature fusion layer. The AWMSC block can learn rich and complementary features on multiple scales in parallel. Then, an adaptive weight vector is introduced to modulate multiscale features to emphasize fault sensitive features and suppress features that are sensitive to operating conditions. The train wheelset bearing dataset and the bearing dataset provided by Case Western Reserve University (CWRU) are used to verify the superiority of the proposed model over the basic CNN and other multiscale CNN models. The experiment results show that the proposed model has strong fault discriminative ability and domain adaptive ability against variable operating conditions. INDEX TERMS Adaptive weighted multiscale feature learning, convolutional neural network, deep learning, fault diagnosis, rotating machinery, variable operating conditions.