An important initial step in fault detection for complex industrial systems is gaining an understanding of their health condition. Subsequently, continuous monitoring of this health condition becomes crucial to observe its evolution, track changes over time, and isolate faults. As faults are typically rare occurrences, it is essential to perform this monitoring in an unsupervised manner. Various approaches have been proposed not only to detect faults in an unsupervised manner but also to distinguish between different potential fault types. In this study, we perform a comprehensive comparison between two residual-based approaches: autoencoders, and the input-output models that establish a mapping between operating conditions and sensor readings. We explore the sensor-wise residuals and aggregated residuals for the entire system in both methods. The performance evaluation focuses on three tasks: health indicator construction, fault detection, and health indicator interpretation. To perform the comparison, we utilize the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dynamical model, specifically a subset of the turbofan engine dataset containing three different fault types. All models are trained exclusively on healthy data. Fault detection is achieved by applying a threshold that is determined based on the healthy condition. The detection results reveal that both models are capable of detecting faults with an average delay of around 20 cycles and maintain a low false positive rate. While the fault detection performance is similar for both models, the input-output model provides better interpretability regarding potential fault types and the possible faulty components.

翻译：复杂工业系统故障检测中的一个关键初始步骤是了解其健康状态。随后，对这种健康状态的持续监测变得至关重要，以观察其演变过程、追踪随时间的变化并隔离故障。由于故障通常为罕见事件，因此必须在无监督方式下执行此监测。已有多种方法被提出，不仅用于以无监督方式检测故障，还可区分不同潜在故障类型。本研究对两种基于残差的方法进行了全面比较：自编码器，以及建立运行条件与传感器读数之间映射关系的输入-输出模型。我们探索了两种方法中传感器级残差和系统全局聚合残差。性能评估聚焦于三项任务：健康指标构建、故障检测和健康指标解读。为进行比较，我们采用了商用模块化航空推进系统仿真（C-MAPSS）动态模型，具体使用了包含三种不同故障类型的涡扇发动机数据子集。所有模型仅使用健康数据进行训练。通过基于健康状态确定的阈值实现故障检测。检测结果表明，两种模型均能以约20个周期的平均延迟检测故障，并保持较低的误报率。尽管两种模型的故障检测性能相似，但输入-输出模型在潜在故障类型和可能故障组件的可解释性方面表现更优。