Work

A compressor fault probability prediction and early warning method based on OCSSA–VMD–CNN–GRU-attention and chi-square residual mapping

Year: 2026

Type: article

Abstract: This study proposes a VMD-CNN-GRU-Attention-based fault probability prediction method for compressor monitoring signals characterized by strong noise, non-stationarity, multiscale behavior, and limited labeled fault samples. Compressor pressure and motor current are selected as the two key inputs. OCSSA-VMD is employed to adaptively decompose the raw sequences, thereby improving multiscale feature separability and suppressing noise. A one-step CNN-GRU-Attention predictor is then constructed, in which CNN extracts local time-varying patterns, GRU captures temporal dependencies, and temporal attention emphasizes the historical moments that contribute most to the current prediction. Based on prediction residuals in the healthy stage, a statistical baseline is established. The current deviation is quantified by the squared standardized residual and mapped to the instantaneous fault probability through the cumulative distribution function of a chi-square distribution with one degree of freedom. A moving average is further applied to obtain a smoothed fault probability curve, and online warning is achieved by combining a dynamic threshold with a consecutive exceedance criterion. In the final validation run, the proposed method achieves RMSE, MAE, R², and MAPE values of 0.4746, 0.2147, 0.9879, and 6.54%, respectively. Both fault events are successfully detected, with an event-level detection rate of 100% and an average early warning time of 23.47 h.

Source: Scientific Reports

DOI: 10.1038/s41598-026-68277-0