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.