用最新数据建模预测卡车部件故障，降本提效。
An Empirical Study on Predictive Maintenance for Component X in Heavy-Duty Scania Trucks
- 仅用最近观测值构建表格数据，适配传统机器学习模型。
- 在Scania部件X数据集上，成本低于当前最先进方法。
- 流程简化，配合AutoML适合工业落地场景。
近年来，基于状态的预测性维护（PdM）在卡车车队中日益流行。该策略通过监测车辆健康状况并根据状态采取主动措施，以最小化非计划停机时间并降低成本。然而，由于卡车生成的数据量庞大、故障检测复杂，以及解决方案实施中的成本效益权衡困难，其落地面临挑战。本文提出一种基于状态的PdM方法，假设被监测部件的磨损状态可表示为单调非减的时间序列。该方法仅选取时间序列中最新的观测值，并将其转换为表格格式，用于基于表格数据的机器学习模型分类。实验结果表明，在Scania Component X数据集上，该方法相比现有最先进（SOTA）方法降低了维护成本，同时通过AutoML简化了建模流程。
原文摘要 · Abstract (English)
Condition-based Predictive Maintenance (PdM) for truck fleets has gained momentum in recent years. This maintenance strategy aims to minimize unplanned downtimes and reduce costs by monitoring the health status of vehicles and taking proactive action based on their condition. However, the implementation of condition-based PdM systems is challenging due to the large volume of data generated by the trucks, the inherent complexity of detecting failures through sensor data and the difficulties in finding cost-effective trade-offs in the solution's implementation. In this paper, we define and validate a condition-based PdM methodology built on the assumption that the wear-and-tear state of the monitored component can be represented as a monotonically non-decreasing time series. It involves selecting only the most recent observations from the time series and transforming them into a tabular format for classification using machine learning (ML) models designed for tabular data. Our results indicate that the proposed methodology reduces costs on the Scania Component X dataset compared to current state-of-the-art (SOTA) approaches, while also simplifying the modeling process through AutoML.
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