改进深度时间聚类的无监督数据驱动汽车诊断

Peter Wolf, Alvin Chin, B. Bäker
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引用次数: 1

摘要

大多数用于汽车诊断的数据驱动故障检测方法都是以监督的方式进行训练的。然而,收集诊断数据集来训练监督故障检测模型仍然具有挑战性。输入数据和缺陷事件的精确关联是非常重要的,需要领域专家的广泛支持。此外,由罕见的故障事件引起的强烈不平衡导致模型偏向于非故障事件。因此,在这项工作中,我们提出了一种完全无监督的数据驱动诊断方法来检测高频车载数据中的故障。我们将静态数据的深度嵌入聚类的概念转移到多元车载时间序列中。我们通过修改神经网络结构来扩展该方法,并比较了聚类层中的三种相似性度量,即软动态时间翘曲,复杂性不变距离和欧几里得距离。我们进一步引入自适应目标分布来处理不平衡数据集。我们的方法在测试车辆的多变量高频电子控制单元数据上进行了评估,以检测高压涡轮增压汽油发动机的预点火。使用当前最先进的时间序列聚类方法作为性能比较的基准。结果表明,我们的方法能够在没有标签的情况下识别预点火,并且在准确性方面比基线高出10%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unsupervised Data-Driven Automotive Diagnostics with Improved Deep Temporal Clustering
The majority of data-driven fault detection approaches for automotive diagnostics are trained in a supervised manner. However, gathering diagnostics datasets to train supervised fault detection models is still challenging. Precise correlation of input data and defect events is non-trivial and requires extensive support of domain experts. Additionally, a strong imbalance caused by rare faulty events results in models which are biased towards non-faulty events. Hence, in this work, we propose a fully unsupervised data- driven diagnostics approach to detect faults in high frequency in-vehicle data. We transfer the concept of deep embedded clustering for static data to multivariate in- vehicle time series. We extend the approach by modifying the neural network architecture and comparing three similarity measures in the clustering layer, i.e., soft dynamic time warping, complexity invariant distance, and Euclidean distance. We further introduce an adapted target distribution to tackle imbalanced datasets. Our approach is evaluated on multivariate high frequency electronic control unit data of a test vehicle to detect pre-ignitions in high pressure turbocharged petrol engines. Current state-of-the-art time series clustering approaches are used as baselines for performance comparison. The results show that our approach is able to identify pre-ignitions without labels and outperforms the baselines by 10 percent in terms of accuracy.
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