The reciprocating compressor is, in general, a critical equipment in a process plant. For certain ultra-high-pressure process, if the reciprocating compressor fails, often it will cause serious impact to not just the compressor itself, but also the process surrounds it. To prevent compressors from failures, an expert diagnosis system is needed. However, the traditional rule-based expert system is quite inefficient and difficult to create.

For an expert prognosis system that is customized to meet needs of a specific process, one needs to refer to plant maintenance history, which is hard to come by due to the fact that most maintenance was poorly documented. This research attempt to demonstrate the feasibility of developing an expert prognosis system through implementation of association rules. Rather than mining from maintenance history, records of failure cases were collected from technical journal articles by extracting information containing failure symptoms and causes on failed components, that mimicking repair history. In total, 115 failure information out from 41 journal articles were gathered. Applications of this approach to practical use in a process plant is easy by replacing the failure information table with that from datamining the repair history. The failure information was first tabulated and then put through association analysis for support, confidence, and lift between two parameters. The demonstration program has been successful with 1-to-1, many-to-1, and many-to-many analysis among failed components, failure modes, and operation parameters.

通常，往复式压缩机是加工厂中的关键设备。对于某些超高压过程，如果往复式压缩机发生故障，通常不仅会对压缩机本身造成严重影响，而且还会对过程造成严重影响。为了防止压缩机故障，需要专家诊断系统。但是，传统的基于规则的专家系统效率很低并且很难创建。

对于为满足特定过程的需求而定制的专家预后系统，需要参考工厂维护历史记录，由于大多数维护记录不充分，因此很难获得该记录。这项研究试图证明通过实施关联规则来开发专家预测系统的可行性。与其从维护历史中进行挖掘，不如从技术期刊文章中收集故障案例的记录，方法是通过提取包含故障症状和故障原因的信息来模拟维修历史，这些信息包含故障症状和原因。总共从41篇期刊文章中收集了115条故障信息。通过将故障信息表替换为数据记录维修历史记录中的故障信息表，可以轻松地将此方法实际应用于过程工厂中。首先将故障信息制成表格，然后进行关联分析，以获取两个参数之间的支持度，置信度和提升度。该演示程序已经成功完成了对故障组件，故障模式和操作参数的一对一，多对一和多对多分析。