Many industrial sectors have been collecting big sensor data. With recent
technologies for processing big data, companies can exploit this for automatic
failure detection and prevention. We propose the first completely automated
method for failure analysis, machine-learning fault trees from raw
observational data with continuous variables. Our method scales well and is
tested on a real-world, five-year dataset of domestic heater operations in The
Netherlands, with 31 million unique heater-day readings, each containing 27
sensor and 11 failure variables. Our method builds on two previous procedures:
the C4.5 decision-tree learning algorithm, and the LIFT fault tree learning
algorithm from Boolean data. C4.5 pre-processes each continuous variable: it
learns an optimal numerical threshold which distinguishes between faulty and
normal operation of the top-level system. These thresholds discretise the
variables, thus allowing LIFT to learn fault trees which model the root failure
mechanisms of the system and are explainable. We obtain fault trees for the 11
failure variables, and evaluate them in two ways: quantitatively, with a
significance score, and qualitatively, with domain specialists. Some of the
fault trees learnt have almost maximum significance (above 0.95), while others
have medium-to-low significance (around 0.30), reflecting the difficulty of
learning from big, noisy, real-world sensor data. The domain specialists
confirm that the fault trees model meaningful relationships among the
variables.

本文提出了第一个完全自动化的失效分析方法，该方法基于机器学习故障树从原始数据中提取连续变量，并且可以在实际数据集上工作。