基于稀疏编码空间金字塔匹配和GA-SVM的列车故障自动识别

针对货车运行故障动态图像中车辆挡键、集尘器和安全链锁紧螺栓的故障检测,提出一种基于稀疏编码空间金字塔匹配和遗传算法优化的支持向量机相结合的通用故障自动识别算法。首先在不同尺度空间对样本图像进行划分,对每个部分提取尺度不变特征变换特征,利用随机抽取样本的SIFT特征通过迭代学习生成字典并进行稀疏编码;其次利用主成分分析定义编码后的特征对故障识别准确率的贡献值,并据此对编码特征进行降维;然后利用编码降维后的特征结合遗传算法对线性SVM分类器进行训练;最后用训练好的分类器模型对挡键、集尘器和安全链锁紧螺栓的故障进行识别。实验结果表明,本文提出的算法能较好的应用于3种不同类型的故障识别,识别率分别为97.25%、99.00%和97.50%,同时对噪声和光照变化具有一定的鲁棒性,能满足车辆故障的实际检测需求。

Automatic fault recognition algorithm for key parts of train based on sparse coding based spatial pyramid matching and GA-SVM

A general automatic fault recognition algorithm based on sparse-coding-based spatial pyramid matching and Genetic Algorithm Optimized Support Vector Machine (GA-SVM) was proposed for fault detection of the bogie block key, dust collector, and fastening bolt in the Trouble of moving Freight car Detection System (TFDS). First, the image of a sample was divided into patch areas in different scale spaces, and the Scale-Invariant Feature Transforms (SIFT) of each patch area was extracted. Sparse coding was then performed by iteratively learning dictionaries using the SIFT features of randomly extracted samples. Second, principal component analysis was used to define the contribution of the encoded features towards fault recognition accuracy and reduce the dimensionality of the coding features. Then, the SVM classifier was trained using the reduced dimension features after coding and optimization with the genetic algorithm. Finally, the trained classifier was used to detect the bogie block key, dust collector, and fastening bolt faults from their images. The experimental results show that the algorithm can adaptively recognize the three different kinds of faults. The fault recognition rates were 97.25%, 99.00%, and 97.50% for bogie block key, dust collector, and fastening bolts, respectively. This technique is robust to noise and illumination changes and can meet the actual detection requirements of a vehicle's structural faults.