The paper concerns the digital twin design and fault-tolerant control of a production system. The real laboratory system under consideration consists of two subsystems: an assembly part and an automated guided vehicle transportation one. The proposed approach focuses, simultaneously, on both concurrency and synchronization problems as they are inevitable in such a kind of modern systems. The research contribution deals with the development of an analytical description of both an assembly system and a multiple automated guided vehicles transportation layer along with algorithms that are able to determine an optimum sequence of starting individual operations. Subsequently, diagnostics and fault-tolerant control schemes are elaborated and applied to the two-layer system being investigated. The core advantage of the proposed approach is that it can compensate or eliminate the impact of some faults, e.g. delays occurring in the assembly unit or reduced velocities of transportation means. This makes it possible to keep the desired schedule as far as possible. The paper concludes with a series of illustrative use cases and comparisons which exhibit the performance of the novel approach in both cases, i.e. faulty and fault-free.

The paper presents a new method of predicting the remaining useful life of technical devices. The proposed soft computing approach bridges the gap between analytical and data-driven health prognostic approaches. Whilst the former ones are based on the classical exponential shape of degradation, the latter ones learn the degradation behavior from the observed historical data. As a result of the proposed fusion, a practical method for calculating components’ remaining useful life is proposed. Contrarily to the approaches presented in the literature, the proposed ensemble of analytical and data-driven approaches forms the uncertainty interval containing an expected remaining useful life. In particular, a Takagi–Sugeno multiple models-based framework is used as a data-driven approach while an exponential curve fitting on-line approach serves as an analytical one. Unlike conventional data-driven methods, the proposed approach is designed on the basis of the historical data that apart from learning is also applied to support the diagnostic decisions. Finally, the entire scheme is used to predict power Metal Oxide Field Effect Transistors’ (MOSFETs) health status. The status of the currently operating MOSFET is determined taking into consideration the knowledge obtained from the preceding MOSFETs, which went through the run-to-failure process. Finally, the proposed approach is validated with the application of real data obtained from the NASA Ames Prognostics Data Repository.

The paper deals with the problem of health-aware fault-tolerant control of a vehicle fleet. In particular, the development process starts with providing the...

A permanent growth of forklifts’ indoor practical applications such as high-storage warehouses makes them dominating transportation tools. It rises the need for their coordinated scheduling including uncertainties related to the human operator behaviour. To tackle this problem, in the paper a novel comprehensive fuzzy logic-based methodology enabling modeling cooperating forklifts and their robust fault-tolerant scheduling is proposed. The advantage of the developed methodology is that it can overcome inevitable conditions like faults, environmental or human-like disturbances and uncertainties. To settle this problem a fuzzy algebraic max plus algebra-based model is proposed and accompanied with a suitable predictive scheduling algorithm. Subsequently, the proposed approach is extended by fault diagnosis mechanisms resulting in a new centrally managed predictive fault-tolerant scheduling system. The efficiency of the proposed approach is evaluated using various simulation scenarios which involve cooperating forklifts transportation tasks operating in a high-storage warehouse of RAFI GmbH & Co. KG company.