Due to the complexities of compressors and the influence of varied operational factors, a gradual decline in their performance status is inevitable, ultimately leading to compressor stalls. Compressor stalls can inflict substantial damage, thus, it is imperative to detect anomalies promptly and issue early warnings as soon as initial signs of reduced performance or suboptimal operation become apparent. Existing techniques commonly anticipate the onset of compressor stall by detecting spike inception; however, they have not successfully prevented stall effectively. The reason is that once spike inception is identified, it tends to quickly evolve into extensive stall cells within the span of just a few rotations. This article proposes a novel method for early warning of compressor stall, utilizing wavelet features and a Multi-Scale Convolutional Recurrent Encoder–Decoder (MSCRED). The approach extracts wavelet features from the compressor data and fuses information from sensors placed at three critical locations, feeding this data into the Convolutional Long Short-Term Memory (ConvLSTM) network. Furthermore, it utilizes the auto-thresholding technique to establish a pre-stall threshold. This method effectively analyzes precursor signs of compressor stalls, thereby significantly improving the timing of stall warnings for the compressor. The experimental outcomes demonstrate that the MSCRED method excels in the early warning of compressor stall compared to conventional approaches. By conducting a quantitative assessment of monotonicity, robustness, and correlation, the superior performance of the MSCRED method across different operating conditions has been validated.

中文翻译：

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基于小波特征和多尺度卷积递归编码器-解码器的压缩器失速警告


由于压缩机的复杂性和各种运行因素的影响，其性能状态的逐渐下降是不可避免的，最终导致压缩机停机。压缩机失速会造成重大损害，因此，必须及时检测异常情况，并在性能下降或运行欠佳的初始迹象出现时立即发出早期警告。现有技术通常通过检测尖峰开始来预测压缩机失速的发生;然而，他们并没有成功地有效地防止 Stand Stall。原因是，一旦确定了尖峰开始，它往往会在短短几次旋转的范围内迅速演变成广泛的失速细胞。本文提出了一种利用小波特征和多尺度卷积递归编码器-解码器 （MSCRED） 进行压缩机失速预警的新方法。该方法从压缩器数据中提取小波特征，并融合来自放置在三个关键位置的传感器的信息，并将这些数据馈送到卷积长短期记忆 （ConvLSTM） 网络中。此外，它利用自动阈值技术来建立预失速阈值。这种方法有效地分析了压缩机失速的前兆迹象，从而显著改善了压缩机失速警告的时间。实验结果表明，与传统方法相比，MSCRED 方法在压缩机失速的早期预警方面表现出色。通过对单调性、稳健性和相关性进行定量评估，验证了 MSCRED 方法在不同操作条件下的卓越性能。