Fault Diagnosis of Reciprocating Compressor Based on the Prediction of Comprehensive Index Extracted from the Expansion Process in Indicator Diagram

The fault of vulnerable parts in the cylinder accounts for a large proportion of the fault of the reciprocating compressor. In order to find the fault of the compressor in time, parameter monitoring is usually applied to give forewarning or warning of the compressor fault. Because the setting of forewarning value relies on experiences, over protection and delayed warning events often occur. Recently, the research of fault diagnosis methods combined with artificial intelligence technology is gradually rising. However, most of the fault diagnosis methods based on artificial intelligence technology rely on large number of fault data for fault learning. The problem is that it is almost impossible to obtain the sample data including all of the fault types. To solve above problems, a fault diagnosis method for reciprocating compressor based on the prediction of comprehensive index extracted from the expansion process in indicator diagram is proposed. In this method, a model predicting the comprehensive index of expansion process under normal working conditions is established. The expansion process will change significantly when faults occur, which could result in a deviation between the predicted value and the actual value of the comprehensive index. This deviation is utilized in this paper for fault diagnosis. Fault experiments are carried out to verify the effectiveness of the proposed method. An obvious advantage of this method is that no fault data is needed to establish the fault diagnosis model. It would relatively save the cost of the fault diagnosis and be conducive to application. The accuracy of this method for discriminating fault conditions of an individual vulnerable part is more than 94%. The combined fault conditions with multiple parts under fault state could also be successfully distinguished from normal conditions by this method.