Frank Schiller

A concept for a safe realization of a state machine in embedded automotive applications

Currently, both fail safe and fail operational architectures are based on hardware redundancy in automotive embedded systems. In contrast to this approach, safety is either a result of diverse software channels or of one channel of specifically coded software within the framework of Safely Embedded Software. Product costs are reduced and flexibility is increased. The overall concept is inspired by the wellknown Vital Coded Processor approach. Since Mealy state machines are frequently used in embedded automotive systems, application software with a general Mealy state machine is realized differently with Safely Embedded Software starting from the high level programming language C with corresponding measurements.

Reuse of models in the lifecycle of production plants using HiL simulation models for diagnosis

The design and development process of industrial plants is highly supported by various models in the meantime. These models emulate the behavior of the plant by simulations before the plant is built in reality. Though models and simulations provide a lot of advantages, they involve also a downside: Modeling is mostly very expensive in time and costs. To enhance the cost-value ratio, either the modeling efforts are to be reduced or the benefits of the models are to be increased. Nowadays, the use of models for enabling a controller test is common as a measure to increase the benefits. In this paper, an approach for increasing the benefits by reusing Hardware-in-the-Loop (HiL) simulation models is presented. Exemplarily, HiL simulation models can be applied for realizing diagnostic methods enabling detection of plant malfunction during the operation phase. Another example is the use of these models for controller optimization during the operation phase in order to increase the performance of plants.

Monitoring and diagnostics of hybrid automation systems based on synchronous simulation

The competitive market poses high requirements to automation systems. Fulfilling considerable requirements concerning flexibility, availability and efficiency, they have become complex mechatronic systems, featuring both discrete and continuous behavior. For reducing efforts concerning time and costs during the life cycle of production plants, simulation methods are widely applied in the development process. In the operation phase, the identification and analysis of failures is fundamental. Bearing the challenges of hybrid automation systems, a methodology is proposed, realizing monitoring and diagnostics based on a simulation approach. Therein, a simulation represents the behavior of the system and the control system.

Controller architecture for safe cognitive technical systems

Cognition of technical systems, as the ability to perceive situations, to learn about favorable behavior, and to autonomously generate decisions, adds new attributes to safety issues. The system can cope with heavily changing conditions but its future behavior is not known a-priori. Therefore, present software solutions to safety like a comprehensive analysis of the specification and its implementation according to e.g. the V-model are not sufficient. The paper proposes an architecture for safe cognitive controllers consisting of an operational and a strategic functional part. While the first provides certified safety, the strategic part computes safe strategies based on appropriate dynamic models, adapted sets of safety specifications, and learned knowledge about potentially safety critical scenarios. Thus, the architecture explicitly uses cognitive functions to achieve safe behavior, and it allows the application of cognitively controlled plants for safety-related tasks.

Reuse of HiL simulation models in the operation phase of production plants

Industrial production plants are highly complex mechatronic systems. Due to the combination of mechanics, electronics, and software in the field of mechatronics, specific methods and tools are used in the development process. Nowadays, many solutions exist for describing the specification of production plants. In order to reduce the product and development costs as well as to increase their quality, the development process is strongly supported by models. These models enable an early validation of the required properties. Especially those models applied at the end of the development process or in virtual or hybrid commissioning represent the required behavior of the plant fairly in detail. Therefore, not only increasing attention is drawn to the reduction of efforts for modeling and simulation but to their reuse in similar developments, e.g. by providing construction kits for simulation or their application even in the lifecycle of the production plants. Unfortunately, this kind of reuse, e.g. during the operation phase, is not a focus of current research, although the potential of improving e.g. the operation phase is considerable. In this paper, an approach on how to apply models in the operation phase by synchronous and forward simulation is discussed and an example is presented.

Increasing Reliability of Intelligent Manufacturing Systems by Adaptive Optimization and Safety Supervision

Abstract For concurrent consideration of reliability and safety of manufacturing systems on the one hand, and flexibility and performance on the other, a specialized control architecture was proposed recently. It consists of a strategic controller for online computing (sub-)optimal control strategies under consideration of dynamic models, safety specifications, and learned knowledge. The second building block is an operational controller for providing certified safety when dealing with behaviors that are not completely predictable, for instance in case of faults. This paper extends the architecture by a specific technique for the strategic controller: For the case of an intelligent manufacturing system which reacts adaptively to a human operator, control trajectories for a robot arm are computed online such that collision with the operator is excluded. The computation is based on solving a mixed-integer programming problem that takes a dynamic safety area around the operator into account as constraint.

Analysis of Nested CRC with Additional Net Data in Communication

Cyclic Redundancy Check (CRC) is an established coding method to ensure a low probability of undetected errors in data transmission. CRC is widely used in industrial field bus systems where communication is often executed through different layers. Some layers have their own CRC and add their own specific data to the net data that is meant to be sent. Up to now, this nesting is not yet included in the safety proof of systems. Hence, additional effort is made to achieve a required degree of safety which was probably on hand but could not be proven. The paper presents an approach to involve the nesting in the calculation of the residual error probability based on methods of coding theory. This approach helps to reduce the number of worst case assumptions in the overall safety proof and finally to reduce the necessary online efforts like the number of parity bits.

Residual error probability of embedded CRC by stochastic automata

Cyclic Redundancy Check (CRC) is an approved coding technique to detect errors in industrial communication. Using a checksum calculated with the help of a generator polynomial, CRC guarantees a low probability of undetected errors (residual error probability, Pre). The choice of an appropriate polynomial has significant impact on the quality of error detection. Since CRC itself is very efficient, it is obvious to embed safety-critical data protected by an additional CRC into the net data protected by the original CRC in order to increase the error detection of the safety-critical data. The paper introduces a method to determine the corresponding Pre by means of stochastic automata. Using the example of the fieldbus PROFIBUS-PA as embedding communication protocol, polynomials for the additional CRC were analyzed. As result, the impact of generator polynomials in the additional CRC on the Pre as well as the improvement of the error detection capabilities is shown.

Diagnosis Based on a Probabilistic Model of Dynamic Systems

Abstract The diagnostic task of technological processes consists in isolating faults, which have caused the detected abnormal behaviour (Fault Detection and Isolation). In addition to the measured input and output signals the solution of diagnostic tasks requires a description of the process. In the paper, a method of model-based diagnosis is proposed. In contrast to approaches based on quantitative models (differential equations), our approach is based on a qualitative model. There assertions of signals are represented by logical propositions, and the model consists of implications of the form causes ??? effect. The method introduced here takes the model uncertainty into consideration. The logical formulae are associated with conditional probabilities of the form P (effect/causes) providing the probability of a certain effect under condition that certain causes have been occurred. These probabilities are the result of a statistical evaluation, which is often available in power plants. In addition to the logical description a graphic tool will be introduced, the causality graph, which supplies criteria for the partition of the probabilistic model into submodels and the decomposition of the diagnostic task into subtasks. In this way, the complexity of the diagnostic task is reduced and the efficiency of its solution increased.

Analysis of nested CRC with additional net data by means of stochastic automata for safety-critical communication

Cyclic Redundancy Check (CRC) is an approved coding method for error detection. It ensures a low probability of undetected errors, i.e. a low residual error probability, and is easy to implement. In industrial fieldbus systems, communication is usually executed through different layers. Each layer may have its specific check such that a nesting of checks exists. At present, this nesting is not included in the safety proof of systems. Hence, additional effort is made to achieve the required degree of safety which was probably on hand but could not be proven. The paper presents a method to involve the nesting in the calculation of the residual error probability by means of stochastic automata. This method helps to reduce the number of worst case assumptions in the overall safety proof and finally to reduce the necessary on-line efforts like the number of parity bits.