Compressors in petrochemical plants are often crucial to process operations, and when a failure occurs, the outcome can be catastrophic. Many researches have been attempting to detect failure modes as early as possible to plan upfront repair and conceivably reduce maintenance time. A reciprocating compressor was selected as the target of this study, and a few years of historical records of maintenance parameters and maintenance work orders were gathered for analysis. The time history was divided into 13 events, and each event started with a normal operation and ended with a repair work order. Time-domain features and wavelet decomposition features of the parameters were extracted, and the patterns stored within each event were identified using the artificial neural network and support vector machine. Moreover, a set of reasoning algorithms were developed to detect anomalies, and responsible failure modes were identified. For a specific type of compressor, the vibration signal was found to be related to most of the anomalies and thus used for evaluation. Results showed a >90% detection rate for failure mode diagnosis based on historical test data.

中文翻译：

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小波分解与人工神经网络相结合的往复式压缩机故障预测

石化厂中的压缩机通常对过程操作至关重要，当发生故障时，后果可能是灾难性的。许多研究一直试图尽早检测故障模式，以计划前期维修并减少维护时间。本研究以一台往复式压缩机为研究对象，收集了几年维修参数和维修工单的历史记录进行分析。时间历程分为13个事件，每个事件以正常操作开始，以维修工单结束。提取参数的时域特征和小波分解特征，并使用人工神经网络和支持向量机识别每个事件中存储的模式。而且，开发了一套推理算法来检测异常情况，并确定了负责任的故障模式。对于特定类型的压缩机，发现振动信号与大多数异常有关，因此用于评估。结果表明，基于历史测试数据的故障模式诊断检测率 >90%。