DOWELL: Diversity-Induced Optimally Weighted Ensemble Learner for Predictive Maintenance of Industrial Internet of Things Devices
DOWELL: Diversity-Induced Optimally Weighted Ensemble Learner for Predictive Maintenance of Industrial Internet of Things Devices
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DOI：
10.1109/jiot.2021.3097269
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发表时间：
2022-02
影响因子：
10.6
通讯作者：
Onat Güngör;T. Rosing;Baris Aksanli
中科院分区：
文献类型：
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作者：
Onat Güngör;T. Rosing;Baris Aksanli
工业物联网（I-IoT）为各种工业应用（如制造、物流等）提供了更智能的维护方法。这种方法基于持续观察系统数据，以预测设备故障并提高设备效率。这种智能维护，也称为预测性维护（PDM），可以找到最佳的维护计划，以降低运营和资本成本。准确的剩余使用寿命（RUL）预测是有效的PDM系统的关键。数据驱动的RUL估计方法由于其更容易实现而非常流行。我们观察到，数据驱动方法的性能根据数据集和底层系统参数而变化很大，因此很难有一个单一的算法和参数集，最适合所有设置。我们提出了一个集成学习框架，从20个不同的最先进的深度学习模型中选择准确和多样化的基础学习器。为了准确起见，我们通过构造一个优化问题来发现基本学习器的最佳权重。对于多样性，我们测量基本学习者预测之间的相似性，并迭代地选择最多样化的模型集，同时将准确性保持在一定水平。我们表明，我们的方法可以有39.2%的速度比基于准确性的合奏，只有3.4%的准确性损失的再训练。
The Industrial Internet of Things (I-IoT) enables a smarter maintenance approach for various industrial applications, such as manufacturing, logistics, etc. This approach is based on continuously observing system data to predict device failures and increase device efficiency. This smart maintenance, also known as predictive maintenance (PDM), finds an optimal maintenance schedule to reduce operational and capital costs. Accurate remaining useful life (RUL) prediction is critical for an effective PDM system. Data-driven RUL estimation methods are quite popular owing to their easier implementation. We observe that the performance of data-driven methods varies drastically based on the data set and underlying system parameters, thus making it difficult to have a single algorithm and a parameter set that work best for all settings. We propose an ensemble learning framework, where accurate and diverse base learners are selected out of 20 different state-of-the-art deep learning models. For accuracy, we discover the optimal weights of base learners by constructing an optimization problem. For diversity, we measure the similarity among base learner predictions and iteratively select the most diversified set of models while keeping the accuracy at a certain level. We show that our approach can have 39.2% faster retraining compared to an accuracy-based ensemble with only 3.4% loss in accuracy.