深信不疑网络
人工智能
玻尔兹曼机
计算机科学
稳健性(进化)
模式识别(心理学)
深度学习
特征(语言学)
方位(导航)
融合
典型相关
断层(地质)
限制玻尔兹曼机
机器学习
地质学
哲学
基因
地震学
化学
生物化学
语言学
作者
Defeng Lv,Huawei Wang,Changchang Che
标识
DOI:10.1177/09544062211008464
摘要
Aiming at raw vibration signal of rolling bearing with long time series, a fault diagnosis model based on multimodal data fusion and deep belief network is proposed in this paper. First, multimodal data composed of artificial features and model features can be obtained by time-frequency domain analysis and unsupervised learning based on restricted Boltzmann machine (RBM). Second, canonical correlation analysis method is used to extract the typical feature pairs from the multimodal data to realize the feature-level multimodal data fusion. Third, deep belief network is applied to extract deep feature mapping between typical feature pairs and fault types. After greedy layer-wise pre-training and fine-tuning, it is available to achieve the trained model for fault diagnosis of rolling bearing. Typical rolling bearing datasets are used to testify the effectiveness of the proposed method. It is verified that the robustness and accuracy of the proposed method are superior to common methods.
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