The ability to detect anomalies in time series is considered highly valuable in numerous application domains. The sequential nature of time series objects is responsible for an additional feature complexity, ultimately requiring specialized approaches in order to solve the task. Essential characteristics of time series, situated outside the time domain, are often difficult to capture with state-of-the-art anomaly detection methods when no transformations have been applied to the time series. Inspired by the success of deep learning methods in computer vision, several studies have proposed transforming time series into image-like representations, used as inputs for deep learning models, and have led to very promising results in classification tasks. In this paper, we first review the signal to image encoding approaches found in the literature. Second, we propose modifications to some of their original formulations to make them more robust to the variability in large datasets. Third, we compare them on the basis of a common unsupervised task to demonstrate how the choice of the encoding can impact the results when used in the same deep learning architecture. We thus provide a comparison between six encoding algorithms with and without the proposed modifications. The selected encoding methods are Gramian Angular Field, Markov Transition Field, recurrence plot, grey scale encoding, spectrogram, and scalogram. We also compare the results achieved with the raw signal used as input for another deep learning model. We demonstrate that some encodings have a competitive advantage and might be worth considering within a deep learning framework. The comparison is performed on a dataset collected and released by Airbus SAS, containing highly complex vibration measurements from real helicopter flight tests. The different encodings provide competitive results for anomaly detection.

在许多应用领域中，按时间序列检测异常的功能被认为具有很高的价值。时间序列对象的顺序性质造成了额外的功能复杂性，最终需要专门的方法来解决任务。如果没有将变换应用于时间序列，则位于时域之外的时间序列的基本特征通常很难通过最新的异常检测方法来捕获。受计算机视觉深度学习方法成功的启发，多项研究提出将时间序列转换为图像表示形式，用作深度学习模型的输入，并在分类任务中产生了非常有希望的结果。在本文中，我们首先回顾了文献中发现的信号到图像编码方法。其次，我们建议对它们的某些原始公式进行修改，以使其对大型数据集的可变性更加稳健。第三，我们在一个常见的无监督任务的基础上对它们进行比较，以说明在相同的深度学习体系结构中使用编码时，编码的选择如何影响结果。因此，我们提供了有和没有建议的修改的六种编码算法之间的比较。选定的编码方法为Gramian角场，Markov过渡场，递归图，灰度编码，频谱图和比例尺。我们还将获得的结果与原始信号用作另一个深度学习模型的输入进行比较。我们证明了某些编码具有竞争优势，在深度学习框架内可能值得考虑。比较是在由空中客车SAS收集并发布的数据集上执行的，其中包含来自实际直升机飞行测试的高度复杂的振动测量结果。不同的编码为异常检测提供了有竞争力的结果。