Kafeel Ayaz, Aziz Sumair, Awais Muhammad, Khan Muhammad Attique, Afaq Kamran, Idris Sahar Ahmed, Alshazly Hammam, Mostafa Samih M
Eco Pack Ltd. 112, Hattar Industrial State, Haripur 7040, Pakistan.
Department of Electronics Engineering, University of Engineering and Technology, Taxila 47040, Pakistan.
Sensors (Basel). 2021 Nov 15;21(22):7587. doi: 10.3390/s21227587.
Accurate and early detection of machine faults is an important step in the preventive maintenance of industrial enterprises. It is essential to avoid unexpected downtime as well as to ensure the reliability of equipment and safety of humans. In the case of rotating machines, significant information about machine's health and condition is present in the spectrum of its vibration signal. This work proposes a fault detection system of rotating machines using vibration signal analysis. First, a dataset of 3-dimensional vibration signals is acquired from large induction motors representing healthy and faulty states. The signal conditioning is performed using empirical mode decomposition technique. Next, multi-domain feature extraction is done to obtain various combinations of most discriminant temporal and spectral features from the denoised signals. Finally, the classification step is performed with various kernel settings of multiple classifiers including support vector machines, K-nearest neighbors, decision tree and linear discriminant analysis. The classification results demonstrate that a hybrid combination of time and spectral features, classified using support vector machines with Gaussian kernel achieves the best performance with 98.2% accuracy, 96.6% sensitivity, 100% specificity and 1.8% error rate.
准确且早期地检测机器故障是工业企业预防性维护中的重要一步。这对于避免意外停机以及确保设备可靠性和人员安全至关重要。对于旋转机器而言，其振动信号频谱中存在有关机器健康状况的重要信息。这项工作提出了一种利用振动信号分析的旋转机器故障检测系统。首先，从代表健康和故障状态的大型感应电动机获取三维振动信号数据集。使用经验模态分解技术进行信号调理。接下来，进行多域特征提取，以便从去噪信号中获得最具判别力的时间和频谱特征的各种组合。最后，使用包括支持向量机、K近邻、决策树和线性判别分析在内的多个分类器的各种内核设置执行分类步骤。分类结果表明，使用高斯核支持向量机进行分类的时间和频谱特征的混合组合实现了最佳性能，准确率为98.2%，灵敏度为96.6%，特异性为100%，错误率为1.8%。
