A Cognitive Approach based on the Actionable Knowledge Graph for supporting Maintenance Operations

Actionable Cognitive Twins for Decision Making in Manufacturing

Actionable Cognitive Twins are the next generation Digital Twins enhanced with cognitive capabilities through a knowledge graph and artificial intelligence models that provide insights and decision-making options to the users. The knowledge graph describes the domain-specific knowledge regarding entities and interrelationships related to a manufacturing setting. It also contains information on possible decision-making options that can assist decision-makers, such as planners or logisticians. In this paper, we propose a knowledge graph modeling approach to construct actionable cognitive twins f…

SurvCF(t)：预测性维护多元时间序列数据中生存分析的反事实解释

SurvCF(t): Counterfactual Explanations for Survival Analysis in Predictive Maintenance Multivariate Time Series Data

针对预测性维护多元时间序列数据的生存分析，提出SurvCF(t)框架，通过识别资产运行历史变化增加预测寿命，将解释设为约束优化问题，经多基准评估，弥合生存预测与维护差距，实现可解释决策型AI。

可操作的CBFI：结合结构分解与因果反事实追索的表格机器学习方法

Actionable CBFI: Integrating Structural Decomposition and Causal Counterfactual Recourse for Tabular Machine Learning

该研究提出A-CBFI框架，结合结构分解与因果反事实追索，在金融、医疗领域可降低76.9%主动干预负担，保持因果有效性与追索成本，实现针对性可操作的反事实追索。

Online learning of windmill time series using Long Short-term Cognitive Networks

Forecasting windmill time series is often the basis of other processes such as anomaly detection, health monitoring, or maintenance scheduling. The amount of data generated on windmill farms makes online learning the most viable strategy to follow. Such settings require retraining the model each time a new batch of data is available. However, update the model with the new information is often very expensive to perform using traditional Recurrent Neural Networks (RNNs). In this paper, we use Long Short-term Cognitive Networks (LSTCNs) to forecast windmill time series in online settings. These r…

AI Neurotechnology for Aging Societies -- Task-load and Dementia EEG Digital Biomarker Development Using Information Geometry Machine Learning Methods

Dementia and especially Alzheimer's disease (AD) are the most common causes of cognitive decline in elderly people. A spread of the above mentioned mental health problems in aging societies is causing a significant medical and economic burden in many countries around the world. According to a recent World Health Organization (WHO) report, it is approximated that currently, worldwide, about 47 million people live with a dementia spectrum of neurocognitive disorders. This number is expected to triple by 2050, which calls for possible application of AI-based technologies to support an early scree…

Graph Neural Networks for Leveraging Industrial Equipment Structure: An application to Remaining Useful Life Estimation

Automated equipment health monitoring from streaming multisensor time-series data can be used to enable condition-based maintenance, avoid sudden catastrophic failures, and ensure high operational availability. We note that most complex machinery has a well-documented and readily accessible underlying structure capturing the inter-dependencies between sub-systems or modules. Deep learning models such as those based on recurrent neural networks (RNNs) or convolutional neural networks (CNNs) fail to explicitly leverage this potentially rich source of domain-knowledge into the learning procedure.…