# Fault Diagnosis Method for Reciprocating Compressors Based on Spatio-Temporal Feature Fusion

## TL;DR

This paper introduces a new fault diagnosis method for reciprocating compressors using spatio-temporal feature fusion to improve accuracy.

## Contribution

The novel spatio-temporal feature fusion model (STFFM) integrates temporal and spatial features for fault diagnosis in compressors.

## Key findings

- The proposed STFFM achieved an average accuracy of 99.14% on an experimental dataset.
- The model effectively integrates spatio-temporal coupled fault features for precise identification.
- The method enhances generalisation through bidirectional attention and regularisation techniques.

## Abstract

Reciprocating compressors, which serve as core equipment in the petrochemical and natural gas transmission sectors, operate under prolonged variable loads and high-frequency impact conditions. Critical components, such as valves and piston rings, are prone to failure. Existing fault diagnosis methods suffer from inadequate spatio-temporal feature extraction and neglect spatio-temporal correlations. To address this, this paper proposes a spatio-temporal feature fusion-based fault diagnosis method for reciprocating compressors. This method constructs a spatio-temporal feature fusion model (STFFM) comprising three principal modules: First, a spatio-temporal feature extraction module employing a multi-layered stacked bidirectional gated recurrent unit (BiGRU) with batch normalisation to uncover temporal dependencies in long-term sequence data. A graph structure is constructed via k-nearest neighbours (KNN), and an enhanced graph isomorphism network (GIN) is integrated to capture spatial domain fault information variations. Second, the spatio-temporal bidirectional attention-gated fusion module employs a bidirectional multi-head attention mechanism to enhance temporal and spatial features. It incorporates a cross-modal gated update mechanism and learnable weight parameters to dynamically retain the highly discriminative features. Third, the classification output module enhances the model’s generalisation capability through multi-layer fully connected layers and regularisation design. Research findings demonstrate that this approach effectively integrates spatio-temporal coupled fault features, achieving an average accuracy of 99.14% on an experimental dataset. This provides an effective technical pathway for the precise identification of faults in the critical components of reciprocating compressors.