Industry 4.0 has facilitated the access to sensor and actuator data from manufacturing systems, leading to studies on data-driven anomaly detection, but limited attention has been paid to finding root causes and automating this process using formalized expert knowledge. This is crucial due to the scarcity of qualified engineers and the time-consuming nature of diagnosing issues in large production systems. To address this gap, we present a framework that combines data-driven anomaly detection with a knowledge graph that provides domain knowledge by leveraging typical explanations of such models (i.e.,data streams potentially caused the detection) for further diagnosis. The framework’s usefulness to infer affected components or data set labels has been evaluated using two deep anomaly detection approaches. For knowledge-based diagnosis, three query strategies that utilize various knowledge graph relationships are implemented through three Artificial Intelligence (AI) techniques. The proposed anomaly detection approach, informed by integrating expert knowledge via the graph structure of the knowledge graph and node embeddings for encoding time series, outperforms baselines and a deep autoencoder in detecting anomalies and in identifying anomalous data streams. In subsequent diagnosis, it achieves the best performance on a complete knowledge graph in combination with a graph pattern matching query by identifying the label or affected component in 60% of detected anomalies by providing 4.1 labels or 2.3 components until the correct one is identified. In case of a corrupted one, Symbolic-Driven Neural Reasoning (SDNR) and Case-Based Reasoning (CBR) with knowledge graph embeddings demonstrate advantages by halving the number of incorrect labels and unaffected components. • Applying three AI techniques (SPARQL - a Query Language for Resource Description Framework (RDF), Case-Based Reasoning (CBR), and Symbolic-Driven Neural Reasoning (SDNR)) for knowledge-based Root Cause Analysis (RCA) by leveraging typical explanations (i.e.,causative data streams) provided by data-driven anomaly detection models. • Proposing an informed deep self-supervised one-class anomaly detection approach that integrates domain knowledge in the form of time series relationships derived from the knowledge graph and knowledge graph embeddings. • Presentation of a general Failure Mode, Effects & Analysis (FMEA) ontology to model expert knowledge about faults and failures that is also instantiated for the used data.

To build intelligent manufacturing systems that react flexibly in case of failures or unexpected circumstances, manufacturing capabilities of production systems must be utilized as much as possible. Artificial Intelligence (AI) and, in particular, automated planning can contribute to this by enabling flexible production processes. To efficiently leverage automated planning, an almost complete planning domain description of the real-world is necessary. However, creating such planning descriptions is a demanding and error-prone task that requires high manual efforts even for domain experts. In addition, maintaining the encoded knowledge is laborious and, thus, can lead to outdated domain descriptions. To reduce the high efforts, already existing knowledge can be reused and transformed automatically into planning descriptions to benefit from organization-wide knowledge engineering activities. This paper presents a novel approach that reduces the described efforts by reusing existing knowledge for planning and scheduling in Industry 4.0 (I4.0). For this purpose, requirements for developing a converter that transforms existing knowledge are derived from literature. Based on these requirements, the SWS2PDDL converter is developed that transforms the knowledge into formal Planning Domain Definition Language (PDDL) descriptions. The approach’s usefulness is verified by a practical evaluation with a near real-world application scenario by generating failures in a physical smart factory and evaluating the generated re-planned production processes. When comparing the resulting plan quality to those achieved by using a manually modeled planning domain by a domain expert, the automatic transformation by SWS2PDDL leads to comparable or even better results without requiring the otherwise high manual modeling efforts.

Abstract Monitoring the state of currently running processes and reacting to ad-hoc situations during runtime is a key challenge in Business Process Management (BPM). This is especially the case in cyber-physical environments that are characterized by high context sensitivity. MAPE-K control loops are widely used for self-management in these environments and describe four phases for approaching this challenge: Monitor, Analyze, Plan, and Execute. In this paper, we present an architectural solution as well as implementation proposals for using MAPE-K control loops for adaptive workflow management in smart factories. We use Complex Event Processing (CEP) techniques and the process execution states of a Workflow Management System (WfMS) in the monitoring phase. In addition, we apply automated planning techniques to resolve detected exceptional situations and to continue process execution. The experimental evaluation with a physical smart factory shows the potential of the developed approach that is able to detect failures by using IoT sensor data and to resolve them autonomously in near real time with considerable results.