Predictive Maintenance applications are increasingly complex, with
interactions between many components. Black box models are popular approaches
based on deep learning techniques due to their predictive accuracy. This paper
proposes a neural-symbolic architecture that uses an online rule-learning
algorithm to explain when the black box model predicts failures. The proposed
system solves two problems in parallel: anomaly detection and explanation of
the anomaly. For the first problem, we use an unsupervised state of the art
autoencoder. For the second problem, we train a rule learning system that
learns a mapping from the input features to the autoencoder reconstruction
error. Both systems run online and in parallel. The autoencoder signals an
alarm for the examples with a reconstruction error that exceeds a threshold.
The causes of the signal alarm are hard for humans to understand because they
result from a non linear combination of sensor data. The rule that triggers
that example describes the relationship between the input features and the
autoencoder reconstruction error. The rule explains the failure signal by
indicating which sensors contribute to the alarm and allowing the
identification of the component involved in the failure. The system can present
global explanations for the black box model and local explanations for why the
black box model predicts a failure. We evaluate the proposed system in a
real-world case study of Metro do Porto and provide explanations that
illustrate its benefits.

提出了一种神经符号架构，使用在线规则学习算法解释黑盒模型预测故障的方法，并使用无监督的自编码器实现异常检测。

一种用于罕见事件的神经符号解释器：基于预测维护的案例研究

A Neuro-Symbolic Explainer for Rare Events: A Case Study on Predictive  Maintenance

We propose the Neural Logic Machine (NLM), a neural-symbolic architecture for
both inductive learning and logic reasoning. NLMs exploit the power of both
neural networks---as function approximators, and logic programming---as a
symbolic processor for objects with properties, relations, logic connectives,
and quantifiers. After being trained on small-scale tasks (such as sorting
short arrays), NLMs can recover lifted rules, and generalize to large-scale
tasks (such as sorting longer arrays). In our experiments, NLMs achieve perfect
generalization in a number of tasks, from relational reasoning tasks on the
family tree and general graphs, to decision making tasks including sorting
arrays, finding shortest paths, and playing the blocks world. Most of these
tasks are hard to accomplish for neural networks or inductive logic programming
alone.

该论文提出了神经逻辑机（NLM）这种结合神经网络和逻辑编程的神经符号架构用于归纳学习和逻辑推理，在小规模排序等任务上进行训练后，可以推广到大规模任务并在如家庭树和排序等任务中取得了优异表现。