Artificial intelligence (AI) is becoming increasingly popular in the context of industrial manufacturing. However, in industrial manufacturing in particular, it is important to ensure the trustworthiness of AI. In this article, we give an overview of different aspects of trustworthy AI in this context. At first, we divide the topic into three different components, namely data, algorithm, and IT infrastructure. We identify several aspects of these components that are required for the trustworthy use of AI. Measures to achieve trustworthy AI are then derived and illustrated on the basis of a specific use case. It is further intended in the ongoing work to evaluate the impact of the individual measures.

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We consider the problem of scheduling production jobs on a single machine with sequence dependent family setup times and individual job deadlines. Given a set of jobs, the goal is to minimize the total time to process all jobs while every job meets its deadline. We study algorithms that compute an exact solution to the problem. Motivated by one example use case, we exploit a natural structural observation that occurs in many production settings: the number of product configurations may be significantly smaller than the total number of jobs. We identify an algorithm that is efficient in this setting in terms of performance. We experimentally evaluate its running time and compare it with two other natural approaches of exact job scheduling.

The increasing size and complexity of cyber-physical production systems (CPPS) lead to an increasing number of faults, such as broken components or interrupted connections. Nowadays, faults are handled manually, which is time-consuming because for most operators mapping from symptoms (i.e., warnings) to repair instructions is rather difficult. To enable CPPS to adapt to faults autonomously, reconfiguration, i.e., the identification of a new configuration that allows either reestablishing production or a safe shutdown, is necessary. This article addresses the reconfiguration problem of CPPS and presents a novel algorithm calledAutoConf.AutoConfoperates on a hybrid automaton that models the CPPS and a specification of the controller to construct a QSM. This QSM is based on propositional logic and represents the CPPS in the reconfiguration context. Evaluations on an industrial use case and simulations from process engineering illustrate the effectiveness and examine the scalability ofAutoConf.

To improve the autonomy of Cyber-Physical Production Systems (CPPS), a growing number of approaches in Artificial Intelligence (AI) is developed. However, implementations of such approaches are often validated on individual use-cases, offering little to no comparability. Though CPPS automation includes a variety of problem domains, existing benchmarks usually focus on single or partial problems. Additionally, they often neglect to test for AI-specific performance indicators, like asymptotic complexity scenarios or runtimes. Within this paper we identify minimum common set requirements for AI benchmarks in the domain of CPPS and introduce a comprehensive benchmark, offering applicability on diagnosis, reconfiguration, and planning approaches from AI. The benchmark consists of a grid of datasets derived from 16 simulations of modular CPPS from process engineering, featuring multiple functionalities, complexities, and individual and superposed faults. We evaluate the benchmark on state-of-the-art AI approaches in diagnosis, reconfiguration, and planning. The benchmark is made publicly available on GitHub.

Decision support systems have become a critical component in the planning processes of companies needing to solve difficult optimization problems. Multi-stage, stochastic optimization problems pose a particular challenge for decision makers, as the uncertainty in the input data makes it hard to determine the correct decisions. The scalable stochastic optimization (SSO) technique proposes a way of solving these problems, but is not able to provide feedback to a decision maker regarding why it makes its decisions. We suggest a mechanism for explaining the feedback of SSO to help decision makers better understand a decision support system’s recommendations.

AI methods for fault handling in Cyber-Physical Production Systems (CPPS) such as production plants and tank systems are an emerging research topic. In the last years many methods for the detection of anomalies and faults, the diagnosis of the root cause and the automated repair have been developed. However, most of the methods are barely evaluated using a wide range of systems but applicability is shown using single use cases. In this paper, an ensemble of simulated benchmark systems is presented, which allows for a broad evaluation of AI methods for fault handling. The ensemble consists of seven different tank systems from process engineering with varying sizes and complexities and is made publicly available on Github. The suitability of the ensemble is shown using AI methods for fault handling such as anomaly detection, diagnosis and reconfiguration.

Cyber-Physical Systems (CPS) are systems that connect physical components with software components. CPS used for production are called Cyber-Physical Production Systems (CPPS). Since the complexity of these systems can be very high, finding the cause of an error takes a lot of effort. In this paper, a data-driven approach to identify causal dependencies in cyber-physical production systems (CPPS) is presented. The approach is based on two different layers of learning algorithms: one low-level layer that processes the direct machine data and a higher-level learning layer that processes the output of the low-level layer. The low-level layer is based on different learning modules that can process differently typed data (continuous, discrete or both). The high-level learning algorithms are based on rule-based and case-based reasoning. Thus, causal dependencies are detected allowing the plant operator to find the error cause quickly.