The paper presents a method of obtaining short-termpositioning accuracy based on micro electro-mechanical system (MEMS) sensors and analysis of the results. A high-accuracy and fast-positioning algorithm must be included due to the high risk of accidents in cities in the future, especially when autonomous objects are taken into account. High-level positioning systems should consider a number of sub-systems such as global positioning system (GPS), CCTV – video analysis, a system based on analysis of signal strength of access points (AP), etc. Short-term positioning means that there are other locating systems with a sufficiently high degree of accuracy based on, e.g. a video camera, but the located object can disappear when it is hidden by other objects, e.g. people, things, shelves etc. In such a case, MEMS sensors can be employed as a positioning system. The paper examines typical movement profiles of a radio-controlled (RC) model and fundamental filtering methods in respect of position accuracy. The authors evaluate the complexity and delay of the filter and the accuracy of the positioning in respect of the current speed and phase of movement (positive acceleration, constant) of the object. It is necessary to know whether and how the length of the filter changes the position accuracy. It has been shown that the use of fundamental filters, which provide solutions in a short time, enables to locate objects with a small error in a limited time.

This paper proposes data-based fault detection methods for an electromechanical actuator (EMA) with a brushed DC motor. The jam and winding short faults are considered in the study as the most prominent EMA faults. The fault detection is based on evaluating the properties of the motor current, considering the basic electromechanical parameters of EMAs. The main advantages are a non-intrusive approach utilising a commonly accessible motor current measurement, simple configurability, and the ability to detect faults under varying operation modes of EMA, including changes of speed, load, or movement profiles. The proposed methods have been evaluated with a custom testing system, and the results have proven the performance of the proposed approach to detect faults under varying operating conditions in industrial applications.