Prediction of anomalous behavior in industrial assets based on sensor reading represents a key focus in modern business practice. As a matter of fact, forecast of forthcoming faults is crucial to implement predictive maintenance, i.e. maintenance decision making based on real time information from components and systems, which allows, among other benefits, to reduce maintenance cost, minimize downtime, increase safety, enhance product quality and productivity. However, building a model able to predict the future occurrence of a failure is challenging for various reasons. First, data are usually highly imbalanced, meaning that patterns describing a faulty regime are much less numerous than normal behavior instances, which makes model design difficult. Second, model predictions should be not only accurate (to avoid false alarms and missed detections) but also explainable to operators responsible for scheduling maintenance or control actions. In this paper we introduce a method called Interpretable Anomaly Prediction (IAP) allowing to handle these issues by using regularized logistic regression as core prediction model. In particular, in contrast to anomaly detection algorithms which permit to identify if the current data are anomalous or not, the proposed technique is able to predict the probability that future data will be abnormal. Furthermore, feature extraction and selection mechanisms give insights on the possible root causes leading to failures. The proposed strategy is validated with a large imbalanced multivariate time-series dataset consisting of measurements of several process variables surrounding an high pressure plunger pump situated in a complex chemical plant.

基于传感器读数的工业资产异常行为预测是现代商业实践中的重点。实际上，对即将发生的故障进行预测对于实施预测性维护至关重要，即基于组件和系统的实时信息进行维护决策，这不仅可以降低维护成本，减少停机时间，提高安全性，而且还可以提高维护成本。产品质量和生产率。然而，由于各种原因，建立能够预测未来故障发生的模型具有挑战性。首先，数据通常高度不平衡，这意味着描述故障状态的模式比正常行为实例少得多，这使得模型设计变得困难。其次，模型预测不仅应该准确（以避免误报和漏检），而且还应向负责安排维护或控制措施的操作员解释。在本文中，我们介绍了一种称为可解释性异常预测（IAP）的方法，该方法可通过使用正则化Logistic回归作为核心预测模型来处理这些问题。特别是与异常检测算法相反由于可以识别当前数据是否异常，因此所提出的技术能够预测未来数据异常的可能性。此外，特征提取和选择机制可洞悉导致故障的可能根本原因。所提出的策略通过一个大型不平衡多元时间序列数据集进行了验证，该数据集由围绕复杂化工厂中的高压柱塞泵的几个过程变量的测量值组成。