Fault diagnosis is critical for intelligent manufacturing by monitoring the status of a production line and preventing financial loss. Model-based fault diagnosis has the advantage of being able to explain the cause and propagation of faults over model-free diagnosis, but would need knowledge about the configuration model and context-specific information of the production line. Ontology modelling can provide context-specific information on top of a configuration model to benefit fault diagnosis. Typically ontologies are manually constructed and then used by a reasoner based on a set of predefined rules. From the perspective of fault diagnosis, this approach works as an expert system where both the ontology models and predefined rules are specific to a given system. Once the system has changed which happens from time to time as repairs and updates in a production line, or in the case of a different system, the ontology models and predefined rules would need to be manually modified or reconstructed. Here a model-based method is proposed to automate generation of configuration models with context-specific information using semantic web technology when a production line is healthy, and to use the generated configuration model and information for diagnosis when the production line has a fault. The method does not rely on predefined rules and reasoners, but rather uses dynamics models that are based on first-principle qualitative mechanics. It uses numerical optimization to minimize the discrepancy between sensor data from the production line and from simulation running the dynamics model to achieve automatic configuration modelling and fault diagnosis. With three use cases commonly found for a production line, i.e. automatic sensor placement modeling or misplacement diagnosis, motor fault diagnosis with single sensor modality, and motor fault diagnosis with sensory substitution, the feasibility of the proposed method is demonstrated. The method’s faster computational speed and comparable accuracy to a quantitative model-based approach suggests it may complement and accelerate the latter with early-stage selection of candidate models for both modelling and fault diagnosis.

通过监控生产线的状态和防止经济损失，故障诊断对于智能制造至关重要。与无模型诊断相比，基于模型的故障诊断具有能够解释故障原因和传播的优势，但需要了解配置模型和生产线的上下文特定信息。本体建模可以在配置模型之上提供特定于上下文的信息，以利于故障诊断。通常，本体是手动构建的，然后由推理器根据一组预定义规则使用。从故障诊断的角度来看，这种方法作为专家系统工作，其中本体模型和预定义规则都特定于给定系统。一旦系统随着生产线中的维修和更新不时发生变化，或者在不同系统的情况下，本体模型和预定义规则将需要手动修改或重建。这里提出了一种基于模型的方法，当生产线健康时，使用语义网络技术自动生成具有上下文特定信息的配置模型，并在生产线出现故障时使用生成的配置模型和信息进行诊断。该方法不依赖于预定义的规则和推理器，而是使用基于第一性原理定性力学的动力学模型。它使用数值优化来最小化来自生产线的传感器数据与运行动力学模型的仿真数据之间的差异，以实现自动配置建模和故障诊断。通过生产线常见的三个用例，即自动传感器放置建模或错位诊断、使用单一传感器模态的电机故障诊断和使用传感器替代的电机故障诊断，证明了所提出方法的可行性。该方法较快的计算速度和与基于定量模型的方法相当的准确性表明，它可以通过早期选择用于建模和故障诊断的候选模型来补充和加速后者。自动传感器放置建模或错位诊断、单传感器模态的电机故障诊断和传感器替代的电机故障诊断，证明了所提出方法的可行性。该方法较快的计算速度和与基于定量模型的方法相当的准确性表明，它可以通过早期选择用于建模和故障诊断的候选模型来补充和加速后者。自动传感器放置建模或错位诊断、单传感器模态的电机故障诊断和传感器替代的电机故障诊断，证明了所提出方法的可行性。该方法较快的计算速度和与基于定量模型的方法相当的准确性表明，它可以通过早期选择用于建模和故障诊断的候选模型来补充和加速后者。