Lu Jianguang, Zhang Huan, Tang Xianghong
Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China.
State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China.
Entropy (Basel). 2019 Jul 13;21(7):687. doi: 10.3390/e21070687.
In order to realize single fault detection (SFD) from the multi-fault coupling bearing data and further research on the multi-fault situation of bearings, this paper proposes a method based on features self-extraction of a Sparse Auto-Encoder (SAE) and results fusion of improved Dempster-Shafer evidence theory (D-S). Multi-fault signal compression features of bearings were extracted by SAE on multiple vibration sensors' data. Data sets were constructed by the extracted compression features to train the Support Vector Machine (SVM) according to the rule of single fault detection (R-SFD) this paper proposed. Fault detection results were obtained by the improved D-S evidence theory, which was implemented via correcting the 0 factor in the Basic Probability Assignment (BPA) and modifying the evidence weight by Pearson Correlation Coefficient (PCC). Extensive evaluations of the proposed method on the experiment platform datasets showed that the proposed method could realize single fault detection from multi-fault bearings. Fault detection accuracy increases as the output feature dimension of SAE increases; when the feature dimension reached 200, the average detection accuracy of the three sensors for bearing inner, outer, and ball faults achieved 87.36%, 87.86% and 84.46%, respectively. The three types' fault detection accuracy-reached to 99.12%, 99.33% and 98.46% by the improved Dempster-Shafer evidence theory (IDS) to fuse the sensors' results-is respectively 0.38%, 2.06% and 0.76% higher than the traditional D-S evidence theory. That indicated the effectiveness of improving the D-S evidence theory by evidence weight calculation of PCC.
为了从多故障耦合轴承数据中实现单故障检测（SFD）并进一步研究轴承的多故障情况，本文提出了一种基于稀疏自动编码器（SAE）特征自提取和改进的Dempster-Shafer证据理论（D-S）结果融合的方法。通过SAE对多个振动传感器的数据提取轴承的多故障信号压缩特征。根据本文提出的单故障检测规则（R-SFD），利用提取的压缩特征构建数据集来训练支持向量机（SVM）。通过改进的D-S证据理论获得故障检测结果，该理论通过修正基本概率分配（BPA）中的0因子并利用皮尔逊相关系数（PCC）修正证据权重来实现。在实验平台数据集上对所提方法进行的广泛评估表明，该方法能够实现多故障轴承的单故障检测。故障检测准确率随着SAE输出特征维度的增加而提高；当特征维度达到200时，三个传感器对轴承内圈、外圈和滚珠故障的平均检测准确率分别达到87.36%、87.86%和84.46%。通过改进的Dempster-Shafer证据理论（IDS）融合传感器结果，三种类型故障的检测准确率分别达到99.12%、99.33%和98.46%，分别比传统D-S证据理论高0.38%、2.06%和0.76%。这表明通过PCC证据权重计算改进D-S证据理论是有效的。
