Fault detection method for belt conveyor roller
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摘要: 针对现有带式输送机托辊故障检测方法采用接触式测量、不便于安装操作、不适合于井下大范围故障检测等问题，提出了一种基于小波去噪和BP-RBF神经网络的托辊故障检测方法。采集托辊运行时的音频信号，采用结合了软阈值法和硬阈值法的折中法对音频信号进行小波去噪处理；将每一层小波分解信号的能量和作为该层的特征值，通过处理系数对低频部分的特征值进行转换，以减小其在总能量中的占比，使故障特征更加明显；将提取的特征向量输入BP-RBF神经网络模型中进行故障检测。测试结果表明，对于正常托辊信号、托辊表面存在裂痕、托辊表面磨损3种情况，该方法的故障识别率达到96.7%。与传统的频谱分析诊断技术相比，该方法所需的工作量更少、准确率更高；相较于基于温度检测等的故障检测技术，该方法采用非接触安装方式，安装更方便，检测范围更大，具有良好的应用前景。Abstract: In view of problems that existing fault detection methods for belt conveyor roller use contact measurement, are not easy to install and operate, and are not suitable for underground large-scale fault detection, a roller fault detection method based on wavelet denoising and BP-RBF neural network is proposed. Audio signal during the operation of the roller is collected and denoised by a compromise method which combines soft threshold method and hard threshold method; energy sum of the wavelet decomposition signal of each layer is used as feature value of the layer, and low-frequency feature values are converted by processing coefficients to reduce their proportion in the total energy and make the fault feature more obvious;the extracted feature vectors are input into BP-RBF neural network model for fault detection. The test results show that fault recognition rate of the method reaches 96.7% for three cases of normal roller signal, crack failure on the roller surface, and wear failure on the roller surface. Compared with the traditional spectrum analysis and diagnosis technology, the proposed method requires less workload and has higher accuracy; compared with fault detection technologies based on temperature detection and other technologies, the proposed method uses a non-contact installation method, which is more convenient to install and has larger detection range and good application prospect.
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期刊类型引用(12)
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