随着铁路运输的不断发展,机车的安全性和可靠性变得尤为重要。据此提出了一种基于差异分析 + 箱线图的机车故障检测算法,用于实时监测柴油机运行状态。该算法通过对比机车柴油机12个气缸的缸头排温数据,利用差异分析 + 箱线图的方法识...随着铁路运输的不断发展,机车的安全性和可靠性变得尤为重要。据此提出了一种基于差异分析 + 箱线图的机车故障检测算法,用于实时监测柴油机运行状态。该算法通过对比机车柴油机12个气缸的缸头排温数据,利用差异分析 + 箱线图的方法识别潜在的故障和异常情况。通过建立差异矩阵,结合箱线图,能够快速定位与发现异常的柴油机气缸,提高故障检测的灵敏度和准确性。实验结果表明,该算法不仅可以有效地识别机车柴油机的故障,还能够提供早期预警,辅助预防性维护,提高机车的可靠性和寿命。与传统的故障检测方法相比,文章提出的差异分析 + 箱线图的方法在实际应用中展现出了显著的优势,为机车系统的安全稳定运行提供了一种新颖且高效的技术手段。With the continuous development of railway transportation, the safety and reliability of locomotives have become increasingly important. This paper proposes a locomotive fault detection algorithm based on differential analysis combined with boxplot, aimed at real-time monitoring of diesel engine operating conditions. The algorithm compares the cylinder head exhaust temperature data of 12 cylinders of a locomotive diesel engine and uses relative difference analysis/boxplot to identify potential faults and anomalies. By establishing a differential matrix combined with boxplot, the method can quickly locate abnormal cylinders, enhancing the sensitivity and accuracy of fault detection. Experimental results demonstrate that the algorithm can effectively identify faults in locomotive diesel engines, provide early warnings, and assist in preventive maintenance, thereby improving the reliability and lifespan of locomotives. Compared with traditional fault detection methods, the differential analysis and boxplot method proposed in this paper shows significant advantages in practical applications, offering an innovative and efficient technical means for the safe and stable operation of locomotive systems.展开更多
文摘随着铁路运输的不断发展,机车的安全性和可靠性变得尤为重要。据此提出了一种基于差异分析 + 箱线图的机车故障检测算法,用于实时监测柴油机运行状态。该算法通过对比机车柴油机12个气缸的缸头排温数据,利用差异分析 + 箱线图的方法识别潜在的故障和异常情况。通过建立差异矩阵,结合箱线图,能够快速定位与发现异常的柴油机气缸,提高故障检测的灵敏度和准确性。实验结果表明,该算法不仅可以有效地识别机车柴油机的故障,还能够提供早期预警,辅助预防性维护,提高机车的可靠性和寿命。与传统的故障检测方法相比,文章提出的差异分析 + 箱线图的方法在实际应用中展现出了显著的优势,为机车系统的安全稳定运行提供了一种新颖且高效的技术手段。With the continuous development of railway transportation, the safety and reliability of locomotives have become increasingly important. This paper proposes a locomotive fault detection algorithm based on differential analysis combined with boxplot, aimed at real-time monitoring of diesel engine operating conditions. The algorithm compares the cylinder head exhaust temperature data of 12 cylinders of a locomotive diesel engine and uses relative difference analysis/boxplot to identify potential faults and anomalies. By establishing a differential matrix combined with boxplot, the method can quickly locate abnormal cylinders, enhancing the sensitivity and accuracy of fault detection. Experimental results demonstrate that the algorithm can effectively identify faults in locomotive diesel engines, provide early warnings, and assist in preventive maintenance, thereby improving the reliability and lifespan of locomotives. Compared with traditional fault detection methods, the differential analysis and boxplot method proposed in this paper shows significant advantages in practical applications, offering an innovative and efficient technical means for the safe and stable operation of locomotive systems.
