Abstract
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.
Highlights
The planetary gearbox is a key component in mechanical transmission systems and has been widely used in wind turbines, helicopters and other heavy machineries [1]
Aiming to address the two problems of multi-sensor data fusion mentioned above, this paper proposes an adaptive data fusion method based on deep convolutional neural networks (DCNN) and applies it to detect the health conditions of a planetary
Since a gradient decent algorithm is trapped into local optima, we introduce several enhancement methods, including stochastic gradient decent (SGD), cross-validation, and adaptive learning rate, to solve this problem
Summary
The planetary gearbox is a key component in mechanical transmission systems and has been widely used in wind turbines, helicopters and other heavy machineries [1]. The wide range of gear ratios, small room in power transmission line, and high transmission efficiency are the most significant advantages of planetary gearboxes [2]. The fault diagnosis of planetary gearboxes is necessary to guarantee a safe and efficient operation of mechanical transmission systems. Combining and analyzing these measurements together should be an appropriate approach to detect various types of faults of complex systems. This approach, Sensors 2017, 17, 414; doi:10.3390/s17020414 www.mdpi.com/journal/sensors
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