Enhancing anomaly detection performance is essential for effective gas turbine condition monitoring and health maintenance. However, in industrial applications, gas turbine operating conditions frequently change, and fault data are scarce or even unavailable. Therefore, identifying anomalies in unknown conditions with training based only on normal data is challenging. Inspired by human communication, where listeners can identify a specific speaker in a crowd regardless of speech rate or intensity, this paper develops a semi-supervised automatic anomaly detection method for gas turbines based on Mel frequency mapping, called Mel Frequency Mapping Anomaly Detection (MFMAD). This method uses training data composed entirely of normal signals (semi-supervised) under single operating conditions to identify abnormal vibration behaviors in other operating conditions of gas turbines. Based on this concept, we developed the following key technologies: (1) Utilizing Mel frequency mapping technology to convert vibration signals from linear Hertz (Hz) frequency to nonlinear Mel frequency, and the fault characteristics under different working conditions are mapped to a unified space. (2) Through Convolutional autoencoder (CAE) semi-supervised learning, only Mel spectrograms of normal vibration signals are used to learn the normal spectral structure in the training stage. In the testing phase, the Structural Similarity Index (SSIM) between the original signal and the reconstructed signal is used as a discriminative indicator to identify abnormal signals. To verify the effectiveness of this method in anomaly detection, the state-of-the-art Area Under the Receiver Operating Characteristic (AUROC) metric is used to evaluate anomaly detection performance. The method achieved remarkable results on two laboratory datasets, with AUROCs of 0.997 and 0.983, respectively. Additionally, on the gas turbine real testbed dataset, the AUROC reached 0.868. This research provides a new solution for early fault warning and maintenance of gas turbines.

中文翻译：

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一种基于零故障样本的单条件训练的燃气轮机异常检测方法


提高异常检测性能对于有效的燃气轮机状态监测和健康维护至关重要。然而，在工业应用中，燃气轮机的运行条件经常变化，故障数据稀缺甚至不可用。因此，仅通过基于正常数据的训练来识别未知条件下的异常是具有挑战性的。受人类交流的启发，听众可以识别人群中的特定说话者，而不管语速或强度如何，本文开发了一种基于 Mel 频率映射的燃气轮机半监督自动异常检测方法，称为 Mel Frequency Mapping Anomaly Detection （MFMAD）。该方法使用单一运行条件下完全由正常信号（半监督）组成的训练数据来识别燃气轮机其他运行条件下的异常振动行为。基于这一概念，我们开发了以下关键技术：（1）利用梅尔频率映射技术将振动信号从线性赫兹（Hz）频率转换为非线性梅尔频率，并将不同工况下的故障特征映射到一个统一空间。（2） 通过卷积自编码器 （CAE） 半监督学习，在训练阶段仅使用正常振动信号的 Mel 频谱图来学习正常频谱结构。在测试阶段，原始信号和重构信号之间的结构相似性指数 （SSIM） 被用作识别异常信号的判别指标。为了验证这种方法在异常检测中的有效性，使用最先进的 Area Under the Receiver Operating Characteristic （AUROC） 指标来评估异常检测性能。 该方法在两个实验室数据集上取得了显著效果，AUROC 分别为 0.997 和 0.983。此外，在燃气轮机实际测试台数据集上，AUROC 达到 0.868。该研究为燃气轮机的早期故障预警和维修提供了新的解决方案。