Jörg Neidig

Improved diagnosis by combining structural and process knowledge

This paper presents an approach for using process knowledge in addition to structural knowledge for plant-wide diagnosis. System diagnosis based on fault-detection and fault-diagnosis often uses structural knowledge (e.g. hierarchies of sub-systems and components) to identify the root cause of faults and malfunctions. The structural knowledge describes the internal relationships of a plant and is particularly suitable for diagnosis if an assembly or component breaks down, which affects other parts of the system. However, the root cause of faults cannot always be identified based on structural knowledge alone, but information about the processes (e.g. production processes) is required in addition. Based on an integration of the VDI/VDE-guideline 3682 “Formalized process description” the authors present a method to consider process knowledge for an improved system diagnosis.

The Smart SemProM

The Smart SemProM is one of the different hardware categories defined in the project. In short, it is a compact, self-contained, embedded device with limited computing power that is designed to perform a few dedicated functions and to interact with its environment. In this chapter it is described how such a device was developed and constructed. The aim was to provide a flexible testbed for a large number of Smart SemProM applications and use cases. Starting from the requirements derived from use case descriptions, a hardware prototype was designed. To create a flexible software environment, an application framework was developed to control the different applications and allow running of concurrent tasks. The potential of the resulting device is illustrated by two application examples.

Applying Digital Product Memories in Industrial Production

Industrial production and supply chains face increased demands for mass customization and tightening regulations on the traceability of goods, leading to higher requirements concerning flexibility, adaptability, and transparency of processes. Technologies for the “Internet of Things” such as smart products and semantic representations pave the way for future factories and supply chains to fulfill these challenging market demands. In this chapter a backend-independent approach for information exchange in open-loop production processes based on Digital Product Memories (DPMs) is presented. By storing order-related data directly on the item, relevant lifecycle information is attached to the product itself. In this way, information handover between several stages of the value chain with focus on the manufacturing phase of a product has been realized. In order to report best practices regarding the application of DPM in the domain of industrial production, system prototype implementations focusing on the use case of producing and handling a smart drug case are illustrated.

Introduction to Model-based Reliability Evaluation of Wireless Sensor Networks

A high level of reliability is a significant requirement for using wireless sensor networks in industrial environments. Model-based evaluation is usually applied in conventional systems to estimate the reliability. In contrast, for analyzing sensor networks, these methods are hardly tested and proven due to the unique properties of that kind of network. This paper presents a first model-based approach to quantitatively assess the reliability of sensor networks. Based on an abstract model of a sensor network, the degree of detail with respect to the characteristic aspects of sensor networks is increased in a stepwise manner. If network topologies are taken into account, analytical methods fail and the assessment of reliability measures has to be done numerically. Finally, a Monte Carlo Simulation is conducted to also cover dynamics in the topology. In conclusion, this paper shows that, by knowledge of link probabilities and lifetime distributions of single sensor nodes, model-based analysis may be used to estimate reliability measures of sensor networks.

The SemProM Data Format

Based on recently emerged technologies such as Radio Frequency Identification (RFID), 2D matrix codes, and embedded devices, products can be uniquely identified and tracked throughout the entire lifecycle. Data acquired along a product lifecycle can be associated to single items and unique instances of a product. Today, significant parts of these data can be stored directly on the item itself.

The Block Interface: Accessing Digital Product Memories

The block interface defines the access to the different information blocks inside the Digital Product Memory (DPM). It is applicable for a range of devices starting from passive data stores (Storage SemProM, e.g., RFID) up to intelligent devices with a dedicated processing unit (Smart SemProM, e.g., motes). This chapter gives an in-depth introduction to the requirements for such an interface and its implementation. The application of the interface is illustrated by an example.

Interaction Modalities for Digital Product Memories

Interacting with Digital Product Memories (DPMs) along the supply chain occurs in a variety of scenarios with different users in different locations with different tasks. This chapter discusses solutions for the modality layer, which establishes the end point of a communication channel between an actor and the DPM, connecting the user to the dialog logic of an application based on DPMs.

Model-based Knowledge Extraction for Automated Monitoring and Control

Abstract Typically, Plant Lifecycle Management Systems (PLMS) provide rich functionality for universal asset management and engineering during a design phase of production systems. Completing actual realization of these production systems and bringing them into operational mode turns out that necessary information from a PLMS, provided already during engineering step, will not be coupled with an appropriate system any more. It remains so as well, even when it is necessary to call back specific engineering background information for some scenario (e.g. for automated monitoring and control). Our approach presented here aims in finding an effective solution for this issue comprising the following: (1) a formal logic-based model for flexible information acquisition from a PLMS, and (2) an automated reasoning mechanism, which can be flexibly adopted for an implementation of various applications of the operational mode (e.g. diagnostic functionality). To evaluate our proposed concepts and techniques, we focus on the implementation example using the Siemens PLMS product COMOS.

Embedding Semantic Product Memories in the web of things

Today, RFID is used to identify a wide range of work pieces or individual products for tracking their movements through the logistics chain. For future purposes the idea of storing only a single ID must be extended to a Product Memory. This memory stores data of the complete product life cycle. This paper introduces an architecture and an implementation for integrating data in product memories. Our contribution encompasses software modules for a uniform sensor access, a sensor data ontology and web interfaces for product memory applications.

A SemProM Use Case: Maintenance of Factory and Automotive Components

Maintenance is essential to guarantee the availability of any technical equipment, but is the dominant cost factor during the equipment’s operating phase. In this chapter it is shown how Digital Product Memories (DPMs) can be used to optimize different maintenance tasks. Therefore, the analysis is focused on the requirements of two domains: industrial manufacturing and automobiles.