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Prognostics of Machine Condition Using Energy Based Monitoring Index and Computational Intelligence

机译：基于能量监测指标和计算智能的机器状态预测

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摘要

A study is presented on applications of computational intelligence (CI) techniques for monitoring and prognostics of machinery conditions. The machine condition is assessed through an energy-based feature, termed as "energy index," extracted from the vibration signals. The progression of the "monitoring index" is predicted using the CI techniques, namely, recursive neural network (RNN), adaptive neurofuzzy inference system (ANFIS), and support vector regression (SVR). The proposed procedures have been evaluated through benchmark data sets for one-step-ahead prediction. The prognostic effectiveness of the techniques has been illustrated through vibration data set of a helicopter drivetrain system gearbox. The prediction performance of SVR was better than RNN and ANFIS. The improved performance of SVR can be attributed to its inherently better generalization capability. The training time of SVR was substantially higher than RNN and ANFIS. The results are helpful in understanding the relationship of machine conditions, the corresponding indicating feature, the level of damage or degradation, and their progression.
机译：提出了有关计算智能（CI）技术在机械状况监视和预测中的应用的研究。通过从振动信号中提取的基于能量的功能（称为“能量指数”）评估机器状态。使用CI技术，即递归神经网络（RNN），自适应神经模糊推理系统（ANFIS）和支持向量回归（SVR），可以预测“监视索引”的进程。通过基准数据集对建议的程序进行了评估，以进行一步一步的预测。通过直升机传动系统变速箱的振动数据集说明了该技术的预后效果。 SVR的预测性能优于RNN和ANFIS。 SVR性能的提高可以归因于其固有的更好的泛化能力。 SVR的训练时间大大高于RNN和ANFIS。结果有助于理解机器状况，相应的指示特征，损坏或退化的程度及其进展之间的关系。

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