Unpacking the Role of Artificial Intelligence for a Multimodal Service System Design

Since requirements of service demands are becoming increasingly complex and diversified, one of the success factors of a multimodal service system is its capability to design a specific service instance satisfying a specific set of requirements. This capability is further highlighted in Ad Hoc Multimodal Service Systems (AHMSSs), where service instances rarely follow a standard form of service delivery and exist only for a limited time. However, due to the increasing scale and frequency of services in many business and public sectors, meeting the desired level of capability has become troublesome. A well-designed Artificial Intelligence (AI) approach can be a solution to the difficulty by addressing the underlying complexity and uncertainty of the AHMSS design process. To conceptualize and foster AI applications to an AHMSS, this study identifies key decision-making problems in the AHMSS design process and discusses the role of AI in the process. The results will form the basis for AI development and implementation for an AHMSS and relevant service systems

Fault Detection based on MCSA for a 400Hz Asynchronous Motor for Airborne Applications

Future health monitoring concepts in different fields of engineering require reliable fault detection to avoid unscheduled machine downtime. Diagnosis of electrical induction machines for industrial applications is widely discussed in literature. In aviation industry, this topic is still only rarely discussed.A common approach to health monitoring for electrical induction machines is to use Motor Current Signature Analysis (MCSA) based on a Fast Fourier Transform (FFT). Research results on this topic are available for comparatively large motors, where the power supply is typically based on 50Hz alternating current, which is the general power supply frequency for industrial applications.In this paper, transferability to airborne applications, where the power supply is 400Hz, is assessed. Three phase asynchronous motors are used to analyse detectability of different motor faults. The possibility to transfer fault detection results from 50Hz to 400Hz induction machines is the main question answered in this research work. 400Hz power supply frequency requires adjusted motor design, causing increased motor speed compared to 50Hz supply frequency. The motor used for experiments in this work is a 800W motor with 200V phase to phase power supply, powering an avionic fan. The fault cases to be examined are a bearing fault, a rotor unbalance, a stator winding fault, a broken rotor bar and a static air gap eccentricity. These are the most common faults in electrical induction machines which can cause machine downtime. The focus of the research work is the feasibility of the application of MCSA for small scale, high speed motor design, using the Fourier spectra of the current signal.Detectability is given for all but the bearing fault, although rotor unbalance can only be detected in case of severe damage level. Results obtained in the experiments are interpreted with respect to the motor design. Physical interpretation are given in case the results differ from those found in literature for 50Hz electrical machines