A deep learning-based recognition method for degradation monitoring of ball screw with multi-sensor data fusion. (August 2017) - Zhang, Li; Gao, Hongli; Wen, Juan; Li, Shichao; Liu, Qi
Abstract: In this paper, a novel intelligent ball screw degradation recognition method based on deep belief networks (DBN) and multi-sensor data fusion is proposed. First, the derived method calculates frequency spectrums of raw signals, and the fused frequency spectrums are calculated by the multi-sensor data fusion. Then, a deep learning-based recognition model that can estimate the degradation condition of ball screw automatically is established with the fused dataset. The effectiveness of the proposed method is validated using dataset collected from the degradation test of ball screw. The dataset contains massive samples involving 7 degradation stages under 9 working conditions by 3 accelerometers. The classification results indicate that the proposed DBN-based method is able to mine intrinsic characteristics from the fused frequency spectrums adaptively and obtain a superior recognition accuracy. Finally, two comparative studies are performed to show the advantage of the proposed DBN-based method in ball screw degradation condition recognition. Highlights: A novel intelligent ball screw degradation recognition method based on DBN and multi-sensor data fusion is proposed. The proposed method is able to mine intrinsic characteristics adaptively and obtain a superior recognition accuracy. The fused signals calculated by multi-sensor data fusion can better reflect the degradation of ball screw.