智能船舶动力系统设备的自适应阈值方法

Adaptive threshold method for intelligent ship power system equipment

[目的]针对智能船舶动力系统设备的状态监控报警不及时、阈值带宽过大、状态评估参数不准确等问题,提出自适应阈值的确定方法,用以对动力系统设备进行监控报警和状态评估。[方法]首先,采用模拟退火算法优化回归支持向量机(SVR)预测模型,对动力系统设备的常规状态特征参数进行建模;然后,对建模残差进行正态转化,并结合滑动时间窗来构建自适应阈值模型;最后,选取某船舶主推进柴油机的排烟温度作为研究对象进行实例验证。[结果]研究结果表明,相较于传统固定阈值,自适应阈值模型的带宽更为紧凑,具有良好的自适应性,能够提前识别动力系统设备的异常现象。[结论]所提方法提高了监控报警系统的效率和阈值精度,可为早期故障诊断和系统状态评估提供更准确的依据。

[Objective] In light of problems such as the untimely condition monitoring and alarm, excessively large threshold bandwidth and inaccurate condition evaluation parameters of intelligent ship power system equipment, an adaptive threshold method is proposed to monitor, alarm and evaluate the conditions of such equipment. [Method] First, a simulated annealing algorithm is used to optimize the support vector regression(SVR) machine prediction model to simulate the general state characteristic parameters of the power system equipment. Then, after the normal transformation of the modeling residual, combined with the sliding time window, the adaptive threshold model is constructed. Finally, the exhaust gas temperature of the ship’s main propulsion diesel engine is selected as the research object for example verification. [Results] The results show that compared with the traditional fixed threshold, the adaptive threshold model has more compact bandwidth and good adaptability, and can identify abnormal phenomena in power system equipment in advance. [Conclusion] This method improves the efficiency and threshold accuracy of monitoring and alarm systems, and provides an effective means of early fault diagnosis and a more accurate basis for system status evaluation.